Comparative Gene Expression Analysis to Identify Common Factors in Multiple Cancers

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COMPARATIVE GENE EXPRESSION ANALYSIS TO IDENTIFY COMMON FACTORS IN MULTIPLE CANCERS

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the Graduate

School of The Ohio State University

Leszek A. Rybaczyk, B.A.

*****

The Ohio State University

2008

Dissertation Committee:

Professor Kun Huang, Adviser

Professor Jeffery Kuret Approved by

Professor Randy Nelson

Professor Daniel Janies ------

Adviser

Integrated Biomedical Science Graduate Program ABSTRACT

Most current cancer research is focused on tissue-specific genetic mutations.

Familial inheritance (e.g., APC in colon cancer), genetic mutation (e.g., p53), and overexpression of growth receptors (e.g., Her2-neu in breast cancer) can potentially lead to aberrant replication of a cell. Studies of these changes provide tremendous information about tissue-specific effects but are less informative about common changes that occur in multiple tissues. The similarity in the behavior of cancers from different organ systems and species suggests that a pervasive mechanism drives carcinogenesis, regardless of the specific tissue or species. In order to detect this mechanism, I applied three tiers of analysis at different levels: hypothesis testing on individual pathways to identify significant expression changes within each dataset, intersection of results between different datasets to find common themes across experiments, and Pearson correlations between individual genes to identify correlated genes within each dataset. By comparing a variety of cancers from different tissues and species, I was able to separate tissue and species specific effects from cancer specific effects. I found that downregulation of

Monoamine Oxidase A is an indicator of this pervasive mechanism and can potentially be used to detect pathways and functions related to the initiation, promotion, and progression of cancer.

Dedicated to my wife

iii

ACKNOWLEDGMENTS

I want to thank my adviser, Dr. Kun Huang, for his seemingly unending patience, guidance and advice. Without which I never would have finished this research.

I am indebted to Dr. Jared Butcher for his constant support and input that proved invaluable during my research. I am also grateful to Drs. Donald Holzschu, Meredith

Bashaw, and Scott Moody for encouraging me to pursue academia.

I want to especially acknowledge my committee members, Drs. Randy Nelson,

Jeff Kuret, and Dan Janies who gave up valuable time and resources so that I could succeed.

I wish to thank Dr. Christopher Hans for volunteering to be the graduate studies representative on my committee.

I want to express my gratitude to both sets of my parents, Drs. Pramod and

Dorothy Pathak as well as Mr. and Mrs. Jerome McNally for all their help during the course of my training.

I also wish to acknowledge the administrative staff in my program who shepherded through this difficult process.

VITA

April 23, 1980……………………...……………...... Born – Albuquerque, New Mexico

2005……………………………………………………B.A. Psychology, Ohio University

2005-present……………………Graduate Research Associate, The Ohio State University

PUBLICATIONS

Research Publication

1. L.A. Rybaczyk, M.J. Bashaw, D.R. Pathak, S. Moody, R. Gilders, D. Holzschu, “An overlooked connection: serotonergic mediation of estrogen-related physiology and pathology.” BMC Women’s Health, vol. 5; (2005): 12. (Highly accessed)

2. L.A. Rybaczyk, M.J. Bashaw, D.R. Pathak, K. Huang, “An indicator of cancer: downregulation of Monoamine Oxidase-A in multiple organs and species.” BMC Genomics, 9(1):134, 2008. (Highly accessed)

FIELDS OF STUDY Major Field: Integrated Biomedical Sciences

TABLE OF CONTENTS

Page

Abstract……………………………………………………………………………………ii

Dedication……………………………………………………………………………...…iii

Acknowledgements……………………………………………………………………….iv

Vita………………………………………………………………………………………...v

List of Tables...... ix

List of Figures...... x

Chapters

1. Introduction………………………………..………………………………………1

1.1 Serotonin and Cancer.....……….………………………………………………3

1.2 Comparative Analysis of Gene Expression in Multiple Cancers……………...4

1.3 Organization of this Dissertation…….………………………………...... 6

2. Genechip Technology…………….………………………………………………..7

2.1 Biological Issues…………..…………………………………………………..9

2.2 Current Statistical Approaches………………………………………………10

2.3 Summary……………………………………………………………………..15

3. Serotonin Physiology in Multiple Pathologies with a Focus on Cancer…………17

3.1 Serotonin Regulation………………………………………………………...18

3.2 Serotonin in the Central Nervous System...………….………………………20

3.3 Serotonin in the Musculoskeletal System……………………………………24

3.4 Serotonin in the Vascular System……………………………………………26

3.5 Serotonin in the Immune System…………………………………………….29

3.6 Serotonin in Cancer………………………………………...………………..33

3.7 Summary………………………………………………....…………………..37

4. Hypothesis Testing of the Tryptophan/Serotonin Metabolic Pathway……….….39 4.1 Methods…….……………………………………………..…………………42 4.2 Results……....………………………………………………………………..44 4.3 Discussion..…………………………………………………………………..46 4.4 Summary……....……………………………………………………………..46

5. Whole Genome Analysis……………………….………………………………...48

5.1 Methods……..…………………….…………………………………….……50 5.1.1 Dataset Collection……………………………………………….…51 5.1.2 Dataset Handling…………………………………………………...52 5.1.3 Gene Selection……………………………………………………..52 5.2 Results………………………………………………………………………..56

5.2.1 Frequency of Differential Expression for Genes…………………..57

5.2.2 Human Genes………………………………………………………58

5.3 Discussion……..……………………………………………………………..59

5.4 Summary……………………………………………………………………..60

6. Correlating MAO-A Expression to Identify Differentially Expressed Pathways..61

6.1 Methods………………………………………………………………………62

6.1.1 Dataset Selection.…………………………………………………..62

6.1.2 Correlations………………………………………………………...63

vii

6.2 Results………………………………………………………………………..64

6.3 Discussion……………………………………………………………………64

6.4 Summary…………...…………………………………………………………66

7. Conclusions and Future Directions……………………………...………………67

7.1 Conclusions and Future Directions for Tier I: Hypothesis Testing of the Tryptophan/Serotonin Metabolic Pathway……………...……..……………..68

7.2 Conclusions and Future Directions for Tier II: Whole Genome Analysis…...70

7.3 Conclusions and Future Directions for Tier III: Correlating MAO-A Expression to Identify Differentially Expressed Pathways…………….…….71

7.4 Conclusion…………...………..……………...... ……………………………73

References…………………………………………………………….………………….75

Appendix A Tables………………….………………………………………………..…106

Appendix B Figures….………………………………………………………………….136

viii

LIST OF TABLES

Table Page

1 Description of first datasets identified for analysis……………………………..107

2 Genes listed in the tryptophan pathway in KEGG……………………………...110

3 Descriptive information on human datasets extracted………………………….112

4 Descriptive information on paired datasets extracted from GEO ……………...113

5 Descriptive information on animal datasets extracted from GEO ……………..114

6 The genes with a frequency of 11 out of 19…………………………………….115

7 Genes with frequency of occurrences more than 22 out of 40……………….....116

8 The DAVID output of gene function clustering of the genes with frequency of occurrences more than 22 out of 40...... 118

9 The top six signaling networks identified using Ingenuity Pathway Analysis with a frequency of occurrences more than 22 out of 40...... 122

10 The genes with significant frequency of occurrences in only human datasets....123

11 The DAVID output of gene function clustering of the genes with frequency of occurrences more than 19 out of 32...... 125

12 The top six signaling networks from Ingenuity Pathway Analysis classification of the genes with frequency of occurrences more than 18 out of 32...... 134

13 The top six signaling networks from Ingenuity Pathway Analysis classification of the genes that correlated with MAO-A...... 135

LIST OF FIGURES

Figure Page

1 A flow chart representing the analytical technique used...... 137

2 Expression of MAO-A in normal and cancer tissue samples...... 138

( , + 1) 3 CDF of Beta 2 2 for L=19 datasets...... 139 𝐿𝐿 𝐿𝐿 𝑁𝑁 − ( , + 1) 4 CDF of Beta 2 2 for L=40 datasets...... 140 𝐿𝐿 𝐿𝐿 𝑁𝑁 − ( , + 1) 5 CDF of Beta 2 2 for L=32 datasets...... 141 𝐿𝐿 𝐿𝐿 6 A histogram of the𝑁𝑁 −frequencies of common differentially expressed genes for the 19 datasets (Group A)...... 142

7 A histogram of the gene frequencies for 40 datasets (Group B)...... 143

8 A graph representing the significance of the various pathways for 40 datasets based on an Ingenuity Pathway Analysis...... 144

9 The distribution of significant genes in humans...... 145

10 A graph representing the significance of the various pathways for 32 human datasets based on an Ingenuity Pathway Analysis...... 146

11 Graph representing the significance of the various pathways for 32 human datasets based on an Ingenuity Pathway Analysis...... 147

12 A representation of the G2/M check point...... 148

13 The glycolitic/gluconeogeneic pathway generated by IPA...... 149

14 An enlargement of the portion of the gluconeogeneic pathway that negatively correlated with MAO-A...... 150

CHAPTER 1

INTRODUCTION

Many diseases have defied the ability of modern research to identify treatments and etiologies. The inability to measure thousands of genes simultaneously was one major impediment. The rapid advancement of genetic techniques including qPCR and genechip technology that occurred in the 1990’s dramatically accelerated the pace of research today. Being able to identify multiple genes whose regulation is altered has led to the discovery of genes that were previously thought not to be involved with disease. The drawback to this explosion of information is that the methods and statistics needed to handle and analyze this much information do not necessarily exist.

Bioinformatics is the science of creating, maintaining and analyzing large databases of biomedical information. A major branch of bioinformatics is the study of gene expression profiles using genechip technology. Genechips measure the expression of thousands of mRNAs simultaneously, and can be used to identify similar gene expression changes that occur in multiple types of cancer.

The potential knowledge that can be gained from reanalysis of genechip data has led to the creation of publicly available databases composed of thousands of genechip experiments. In fact, all National Institute of Health (NIH) funded grants

are required to make their data available to the public for reanalysis. One of the

largest databases is the Gene Expression Omnibus (GEO,

http://www.ncbi.nlm.nih.gov/geo) maintained by the National Center for

Biological Information or NCBI. NCBI’s GEO consists of data derived from a

variety of genechip experiments encompassing multiple types of disease. The

availability of a large amount of genechip data from different disease types

enables researchers to carry out reanalysis to identify previously unexplored genes

as candidates for diagnostic markers or therapeutic targets.

With thousands of genechip experiments performed every year, the sheer

volume of data is astounding. One way to reduce the vast nature of the data is to

examine epidemiologic risk factors and associate them with molecular changes

for a specific disease, such as cancer. However, in cancers a factor can be both

protective and detrimental. The effect of any risk factor is dependent on the specific tissue. For example, exposure to polyaromatic-arylhydrocarbons (PAHs)

increases the incidence of liver cancer but is protective for breast cancer [1, 2]. In

contrast, excessive levels of 17β-estradiol (E2) have been shown to be a risk

factor for both breast and liver cancer. These two disparate compounds are both

ligands for nuclear receptors. E2 binds to multiple subtypes of estrogen receptors

which then drive gene transcription downstream of estrogen response elements.

PAHs behave in a similar fashion by binding to the Arylhydrocarbon receptor

(AhR) which then initiates transcription downstream of xenobiotic/hypoxic

response elements. There is much literature on the relationship between E2 and

the receptor for PAHs [3]. One aspect of this literature is the relationship of both estrogen and PAHs to the small molecule serotonin (5-HT) [4, 5]. The role of 5-

HT and its precursor tryptophan in cellular physiology suggests that the metabolism of tryptophan, and as a result serotonin metabolism, may be involved in the initiation, promotion, and/or progression of cancers in general.

1.1 Serotonin and Cancer

Although conventionally thought of as a neurotransmitter, 5-HT is active throughout the body and acts as an effector for multiple hormones, especially E2.

By acting as E2’s effector, 5-HT is pivotal in reproduction and wound healing.

One of the first genes to be expressed by trophoblasts is Monoamine Oxidase-A

(MAO-A), the enzyme that metabolizes serotonin. It is expressed to degrade the

5-HT that is released by maternal platelets during implantation into the uterine lining. Serotonin and its precursor tryptophan are critical to the maintenance of the maternal-fetal interaction. They act in conjunction with other factors to provide fetal immune privilege, promote angiogenesis, act as clotting factors, prevent apoptosis, act as growth signals, initiate invasion of the placental cells

into the uterine wall, signal for mitosis, and finally, after birth, reactivate the immune system. The biologic characteristics of the maternal-fetal interaction are

analogous to the hallmarks of cancer and could potentially be used to explain

many of the cellular characteristics associated with cancer. To elucidate the role of tryptophan and its derivatives on carcinogenesis, I used genechip data and compared gene expression between cancerous tissues and normal tissues for

individual types of cancer and then identified expression changes that were

common to a variety of cancer types. Initially, I focused on the reliability of expression changes within tryptophan metabolism. I then expanded my analysis to the entire genome. Lastly, I used my findings to elucidate biological processes that appear to be common among cancers.

1.2 Comparative Analysis of Gene Expression in Multiple Cancers

Because of the inherent problems associated with current genechip analytical techniques, I had to develop a new methodology that allowed me to detect changes in gene expression common to different cancers in multiple species. One goal of this project was to explore new statistical methods for carrying out comparative studies on gene expression profiles across multiple datasets that represent the same disease process. Here, I take a three-tier approach.

First, I identify the genes that are differentially expressed in each cancer by conducting hypothesis testing on individual genes to detect significant expression changes. Second, I aggregate the lists of the differentially expressed genes for different cancers to find common functional processes across multiple types of

cancer. And thirdly, I conduct Pearson correlations between individual genes to identify conserved networks among the various cancers.

In essence, my approach can be considered a hybrid numerical-semantic method. Since the first stage is the traditional numerical method, the second stage is a quantitative analysis on the semantic information – the gene list, and the third stage is a combination of the numeric and semantic information. These approaches have been shown to be very effective in both the literature and my preliminary work. [6, 7] By using similarities and differences in gene expression profiles, I can identify different factors and molecular signatures that are important to different cancers.

I used this approach to identify genes that are differentially expressed in the majority of cancers. By analyzing an ensemble of genechip datasets

representing a variety of cancerous tissues and treating each dataset as a single

replicate of an experiment (Appendix B, Figure 1), I was able to identify common

expression changes in cancer that are novel and substantial. I found that one gene

in particular, MAO-A, was always changed. I then expanded the scope of my

work and found that MAO-A suppression was associated with a number of the

classical cancer hallmarks. Finally I discovered that MAO-A downregulation

correlates biological network whose role in cancer is well characterized. This

network, the G2/M checkpoint, is associated with initiation, promotion, and

progression in multiple cancers.

1.3 Organization of this Dissertation

Immediately following in Chapter 2, I describe genechip technology, its biological applications, and the current state of statistical procedures used. In

Chapter 3, I provide a detailed review of the literature and describe the normal serotonin physiology and its role in disease. I have shown that serotonin plays important and consistent roles in many diseases as a hormone and in detail

describe its proposed role in breast cancer etiology. [8] Chapter 4 describes my

first study that began by examining the role of the serotonin precursor,

tryptophan, in cancer. Ultimately I found that serotonin played a role in multiple

cancers, through the downregulation of MAO-A.

In Chapter 5, I expanded my analysis to the entire genome and also

expand my dataset to include other types of cancer such as sarcomas and

lymphomas. I then focus strictly on human cancers to eliminate differences that might exist because of species specificity. In Chapter 6, I test for MAO-A associated genes by conducting multiple correlations on the eight human cancer datasets in which both control and cancerous tissues are from the same patient

(paired data). I conclude with Chapter 7 where I discuss the findings from

Chapters 4 and 5 along with those in 6. These findings provided the evidence that

led me to hypothesize a putative mechanism for cancer and my conclusions along

with future directions for these findings.

CHAPTER 2

GENECHIP TECHNOLOGY

Genechips (sometimes called microarrays) are composed of complementary RNA (cRNA) or complimentary DNA (cDNA) oligonucleotides that are bound to a hard substrate; the combined oligonucleotides and substrate are called a genechip. There are two primary methods by which genechips are made. The oldest is by “spotting” RNA oligomers (50-500bp) on a substrate such as a glass slide. A newer method is by synthesizing the strands directly on the chip. The oligomers in this method are usually shorter (25-50bp) and are denser compared to spotted arrays. By convention, the strands are called probes or probesets and the probesets that code for the same gene in a particular location on the chip are referred to as a spot. The exact methodologies used to manufacture genechips are well described but beyond the scope of this work.

The purpose of genechips is to measure the extent of expression of various genes. To do this, mRNA is isolated from a biological sample and reverse transcribed to make cDNA. The cDNA is then transcribed using biotin labeled oligoneucleotides to make biotin labled cRNA. Following transcription, the labeled RNA conjugated with a fluorescent tag and is then placed on the genechip where it hybridizes to the respective complementary oligonucleotide strands. The

chip is then washed to remove any unbound cRNA and placed in a fluorometer to

measure the fluorescent intensity for each spot.

Affymetrix is the leading manufacturer of genechips and a typical

Affymertix chip can measure the expression of over 22,000 genes. Multiple genechips can be used to compare mRNA levels between different conditions, time points, species, and tissues. In addition, genechips can be used to determine the expression of splice variants.

Splice variants can be detected using traditional techniques but testing for

more than a small number of variants is highly impractical. Multiple splice

variants can be identified by using genechips since there are multiple spots for

each gene and each spot can measure the expression of a different exon. [9] In this way, genechips not only measure the expression of a particular gene but also to

proportion each variant is expressed.

Techniques that are able to identify genetic expression changes such as

splice variants, decreased transcription of an enzyme, or increased production of

growth factors provide insight into how disease exerts its pathogenic effect.

Genechips do these tests en mass. A common misconception is that genechips

measure genotypic changes, while there are chips that detect single nucleotide

polymorphisms (SNPs) the majority of genechip experiments examine mRNA

levels, which is a phenotype. By measuring these levels genechip data can be used

to identify common phenotypical changes that occur during the disease process.

2.1 Biological Issues

Genechips have been criticized for being less accurate than other methods

used to measure mRNA such as qPCR and Northern blotting. The main objection

is that there is more noise associated with genechips compared to other

techniques. The probesets used on genechips are shorter than those used in other

techniques potentially allowing for binding of inappropriate mRNA. Also, once

the mRNA is bound to the chip no amplification of the signal takes place.

Compared to other techniques, genechips contain more noise. The short probesets

and lack of amplification are two of the major contributors to the noise associated

with genechips.

Further complicating the analysis is the difference of probeset affinities, a

problem inherent to all genetic techniques. The affinity of individual spots can

vary greatly depending on CG content making comparisons between different

genes difficult. This can partially be overcome by normalization and the best

technique is called CG robust mean array (CGRMA). CGRMA takes into account the intensities on all the chips used in the experiment as well as the CG content of each probe set. Another way to normalize arrays is by inclusion of internal standards. Affymetrix provides a set of standardization spots that can be used to create normalized intensities among chips. Although useful for

Affymetrix datasets, these standardization probesets do not create a measure that can be used with other manufacturer’s chips, custom chips or between species.

The affinity of the probesets between the various chips can be highly variable and even calculating the affinity based on CG content and correcting for it does not guarantee that values will be comparable. Minute differences in processing methods between the different chip types and/or individual laboratory practices can drastically affect the mRNA binding and alter the affinity. Small difference in temperature cause dramatic changes in affinity, making comparison of chips from different datasets impossible.

2.2 Current Statistical Approaches

Currently microarrays are used in a manner similar to large scale qPCR.

Unfortunately, often genes that are thought not to be relevant to the study are simply unanalyzed and filtered out. This is done to simplify interpretation of the results, which can be extraordinarily difficult when so many genes are measured simultaneously. By excluding certain genes, the interpretation is simplified and the probability of Type I error, or false positives, is decreased. The drawback of excluding genes is that certain genes may be highly involved but remain unidentified (Type II error). However, initially decreasing the number of genes by using domain knowledge to increase the power of the analysis is the most pragmatic way of improving the quality of the analysis, as long as a broader spectrum analysis follows.

Current statistical techniques are not the best choice for analyzing

genomic expression changes. Student’s t-test is the most widely used statistical

test to determine significance between two populations. One major flaw with t-

tests is they assume that populations have normal distributions and are

homoscadastic; assumptions of normal distributions and homoscadasticity are

rarely true for real-life data, but these flaws in analysis are currently accepted. In some instances, when the original distribution is log normal, the assumption of normality can be met by using log transformation. Non-parametric techniques such as the Wilcoxon-Mann-Whitney or Spearman rank correlation avoid the issue of distribution by utilizing ranking. This way there is no assumption of having two normal distributions. Unfortunately, they still assume

homoscadasticity.

Besides the problems of assumption of normality and/or homoscadasticty,

performing multiple tests increases the Type I error. Due to the inherent statistical

properties associated with running multiple tests, on average false positives will be present, alpha being the p-value and n being the number of tests. To correct for this, established statistical techniques are applied. The most popular correction for multiple tests is the Bonmeferonni-Holm (BH) correction. [10] The

BH correction is a step wise correction, compared to the traditional Bonferonni correction which is often considered too stringent. [11] Another metric that is used to try to overcome multiple testing is the false discovery rate (FDR).

Although there are many ways of calculating the FDR, the most common is to

multiply the p-value from a t statistic by the number of genes discovered. If a

gene x has a p-value of 0.001 and 200 genes were discovered to be below your

threshold α, then the FDR would be 0.001 200 or 0.2 [11]. Ultimately, the FDR is simply the percentage of genes discovered that are false positives. The FDR is not a significant improvement given all the limitations of a t-test.

While statistical techniques can be used to determine the probability of a false positive, they still are unable to identify false positives. Several algorithms have been developed that attempt to address this issue and they include the FDR,

Significance of Microarray (SAM) [11], and the Univariate Permutation Test

(UPT). [12] Unfortunately, all of these methods utilize the f and t statistics, which have assumptions of normal distributions. By increasing the number of replicates of an experiment, comparative analysis can isolate truly changed genes.

This requires that multiple datasets that have potentially different characteristics be analyzed together.

Direct comparison of data from identical platforms by cross- renormalization using the standards provided by the manufacturer is not ideal.

The majority of publicly accessible datasets have previously been normalized by one of several normalization methods. The most common is locally weighted

regression or lowess. Lowess normalization is a non-linear normalization. If the data was originally normalized using a non-linear normalization, such as lowess, 12

reapplying normalization would skew the values. Normalization of genechip data

is done in order to reduce the associated noise. Following normalization, noise is

random and is equally distributed among all the samples. The random distribution

of the noise has less effect than other confounding factors.

If the data is not from identical chip types, then differences in scale effect

the ability to compare chips. Often there is a non-linear relationship between

different chip types, making transformation of values unfeasible. Eliminating the

issues of affinity (biological confound) and scale (statistical confound) could be

done by creating a point estimate of the values and variance for each gene’s

expression change. This methodology will still retain the biological confound of

affinity even though it could remove issues of scale. By using the ratio of change

in gene expression between disease and control and the standard error of the mean

for each group, instead of point estimates, affinity and scale become moot.

Repeating this technique makes it obvious that genechips are capable of detecting

much smaller changes than the accepted standard of at least a two-fold change in

expression. Multiple datasets are needed to detect these minute changes. The noise associated with microarray work prevents any single experiment from detecting these small changes but by using multiple datasets, a lower threshold can be detected.

Even with increased sensitivity, statistical procedures such as the t-

statistic, only provide information about the absolute location of the central

tendency for each group and do not address the relative central tendency between

two groups or variables. The primary reason is that using only scalar values will

produce only scalar results. Visual inspection of the data is often adequate to determine other information such as the direction of change when there not many

variables (less than 5 covariates). Evaluating the residuals post hoc provides

information about the direction of change but is time consuming and impractical

for large datasets. Knowing the direction of change is often just as important as knowing that there is a change.

Correlations address the direction of change between two variables. The

Pearson’s product moment correlation (Pearson r), Spearman’s Rho (Spearman

rank), and Kendall’s Tau are the most common correlation tests used1. The

Pearson r correlation is very sensitive to multiple factors including non-normality

and non-linearity. The Spearman rank is less sensitive to non-linearity. Both the

Spearman rank and the Pearson r measure the strength of association between two

variables. Kendall’s Tau is slightly different and is used for analyzing ordinal

values. Although it is possible to produce a correlation matrix for all genes using

any of these tests, examining this large of a matrix will produce a great deal of

1 A fourth test, Goodman–Kruskal Gamma, is rarely used since it measures the difference between concordant ranks and discordant ranks.

false positives and would be impractical to interpret. These problems can be

solved by using domain knowledge of specific gene expression changes. By

beginning with a known genetic expression change, a single correlation matrix can be rapidly generated and then filtered based on a threshold value.

Clustering, a common technique in bioinformatics, utilizes various aspects of the different correlation tests. Clustering splits the data into related groups.

Unfortunately, clustering algorithms primarily examine positive correlations and often ignore negative correlations. Biologically, there are times that a gene is suppressed and as the suppressor increases the gene expression decreases. This would be difficult to detect without examining negative correlations.

2.3 Summary

Current statistical techniques are not the best choice for analyzing genomic expression changes because of assumptions of normality and homoscadsticity. In addition, the family wise error rate corrections are often too conservative and produce a great deal of false negatives. The situation is only made worse by differences in normalization techniques that make comparing different datasets to each other directly impossible. To be able to compare datasets across disease, species, and different types of chips, techniques similar to those used in epidemiology can be used, such as meta-analysis. But rather than adjusting values to create point estimates, multiple datasets can be analyzed,

15 treating each dataset as a single replicate of a single experiment. This increases the power and decreases the Type I error of the experimental analysis. By utilizing multiple datasets, the variability within single datasets can be overcome and a better estimator can be created.

CHAPTER 3

SEROTONIN PHYSIOLOGY IN MULTIPLE PATHOLOGIES WITH A

FOCUS ON CANCER

The effects of serotonin play a critical role in mammalian physiology.

Serotonin is highly regulated by estrogen, and estrogen receptors and serotonin receptors coexist in cells in a wide variety of tissues. In mammalian females, estrogen that acts extracellularly is primarily produced in the reproductive organs, and concentrations in blood serum and other tissues change over the lifespan and within the ovarian cycle.[13] The most active and most studied form of estrogen in mammals is 17-β estradiol (hereafter E2), although less active forms are also present.[14] Changes in E2 typically occur in conjunction with changes in progesterone, and are to some degree dependent on progesterone priming. I will primarily focus on physiological levels of E2 assuming the presence of progesterone between puberty and menopause, and assuming its absence after menopause. Differences in estrogen concentrations are associated with physiological changes affecting the CNS, skeletal, vascular, and immune systems.

The mechanisms producing these changes have yet to be fully elucidated. [15]

This critical review of the literature illustrates how many of E2’s effects are mediated by changes in the actions of serotonin (5HT). Serotonin is usually considered to be a neurotransmitter, but surprisingly, only 1% of serotonin in the human body is found in the brain [16]. The remaining 99% is found in other tissues, primarily plasma, the gastro-intestinal tract, and immune tissues, where serotonin acts as a hormone regulating various physiological functions including vasodilation[17], clotting[18], recruitment of immune cells[19-21], gastrointestinal motility,[22] and initiation of uterine contraction [23, 24]. Serotonin also has peripheral functions in a wide variety of animal phyla [25-28] and is similar in chemical structure to auxin, which regulates plant cell shape, growth, and movement [29].

3.1 Serotonin Regulation

Both naturally-occurring and pharmacologically-induced changes in E2 alter the concentration of serotonin through several mechanisms. First, E2 increases transcription of both tryptophan hydroxylase (TPH)2 and the protein

needed to activate TPH, YWAH [30-32] This causes an increase in serotonin

concentrations in the body [33, 34]. Second, E2 inhibits expression of the

serotonin reuptake transporter (SERT) gene and acts as an antagonist at SERT,

thus promoting the actions of serotonin by increasing the time that it remains

2 The conversion of tryptophan to 5-hydroxytryptophan via TPH1 or TPH2 is the rate-limiting steps in synthesis of serotonin from tryptophan. 18

available in synapses and interstitial spaces. [35, 36] Finally, E2 downregulates

MAO-A, the enzyme responsible for serotonin metabolism, [37] and thereby

increase the amount of serotonin available.

Beyond increasing concentrations of serotonin, E2 also modulates the

actions of serotonin because the activation of E2 receptors affects the distribution

and state of serotonin receptors. Higher levels of E2 in the presence of

progesterone upregulate E2 β receptors (ERβ) and downregulate E2 α receptors

(ERα).[38] ERβ activation results in upregulation of the 5HT2A receptor,[39] while ERα activation results in an increase in 5HT1A receptors via nuclear factor

kappa B (NFkB).[40] Therefore, increasing E2 causes an increase in the density

and binding of the 5HT2A receptor,[41, 42] which could explain the observed

increases in 5HT2A density for post-menstrual teenage girls.[43] 5HT2A activity

stimulates an increase in intracellular Ca++,[44] which causes changes in cellular

function.[29, 45] 5HT2A activation subsequently causes Protein Kinase C (PKC)

activation. The effects of increased Ca++ and PKC in cells are system-specific and explain many of the physiological consequences of serotonin activation. One effect of PKC activation is the uncoupling of 5HT1A auto-receptors[46] and decreasing serotonin’s effect at these receptors.[47, 48] Following 5HT2A activation of PKC, 5HT1A receptors become unable to reduce serotonin

production through negative feedback and serotonin concentrations increase.[46-

48] E2 compounds this effect by directly inhibiting 5HT1A function.[49, 50] 19

With reduced levels of E2, 5HT1A receptors are dysinhibited and counter

the effects of 5HT2A receptor activation. Increased activation of 5HT1A in the

immune system results in greater mitotic potential via cyclic adenosine

monophosphate (cAMP) and extra cellular response kinase (ERK).[51-54]

Additionally, the reinstatement of 5HT1A auto-regulation decreases serotonin

concentrations by allowing negative feedback inhibition of serotonin production

and release. Normal physiology depends on maintaining a balance between

++ 5HT2A receptor produced Ca inflow and 5HT1A receptor suppression of cAMP

production. Pathologies result when this balance is perturbed, and the specific

manifestation of these pathologies depends on which system is affected.

3.2 Serotonin in the Central Nervous System

Changes in estrogen are correlated with a variety of effects in the CNS,

such as changes in pain transmission, headache, dizziness, nausea, temperature

regulation, and mood.[55] Serotonin systems regulate these same functions[55,

56] in a direction consistent with mediation of E2 effects. For pain, E2 acts as a

central analgesic,[57] and pain sensation is inhibited by the activation of some

serotonergic neurons.[16] Analgesic drugs that exploit this effect at the 5HT2A receptor are already available.[16, 58-61] I hypothesize that E2’s upregulation of the 5HT2A receptor in the brain might contribute to E2-mediated pain relief, in which case central administration of 5HT2A receptor antagonists would decrease 20

E2’s analgesic effects. In the spinal cord, altered expression of 5HT2A receptors can both increase and decrease pain.[60, 61] E2’s upregulation of 5HT2A in the spinal cord could be a factor in the development of fibromyalgia, which presents as increased generalized pain sensation. Serotonergic regulation of fibromyalgia is supported by evidence that fibromyalgia is comorbid with other serotonin-related pathologies,[62] and that fibromyalgia patients have altered tryptophan metabolism[63] and can be treated with 5HT2A antagonists.[62] E2’s effect on serotonin could also explain why fibromyalgia is more frequently observed in females than males.[64] Cancer pain is similar to fibromyalgia in its neruogenic origin. Cancer pain patients have decreased tryptophan levels[65] and drugs that affect the serotonergic system appear to be more effective analgesics than opioids.

Cancer pain is often linked to sleep disturbances and depression.[66]

Depression is more common in women than in men and is known to be mediated by serotonin receptor levels.[56, 67] Specifically, depression is linked to decreased density of serotonin receptors and decreased efficacy of serotonin in the brain. The increased risk, timing of onset, and effectiveness of treatment of depression in women may be mediated by estrogen’s effect on serotonin receptors. The onset of depression in women is a characteristic of times when estrogen levels are relatively low (in early pregnancy, postpartum, and around and following menopause) or low in comparison to progesterone (the luteal phase of the menstrual cycle).[68, 69] In women with depression around or following

menopause, the effectiveness of treatment with selective serotonin reuptake inhibitors (SSRIs) is enhanced by simultaneous administration of estrogen,[67] and doses of estrogen alone are effective at treating premenstrual, postpartum, and perimenopausal depression, especially for depression linked to aberrant expression of 5HT2A receptors.[39, 70] ERβ regulates the antidepressant effect of

E2 in mice; ERβ knockout mice fail to show the decrease in immobility usually

induced by E2 doses in a forced swim test. [71] The increased levels of serotonin

and increased activity of the 5HT2A receptor caused by E2 could be the

mechanism for E2’s antidepressant effects, in which case 5HT2A receptor agonists

could also enhance the anti-depressant effects of E2. Interestingly depression and

serotonin levels are specifically linked to breast cancer, for which high E2 levels

have been reported to be a risk factor. Among breast cancer patients, women who

are depressed as measured by decreased MAO-A levels, have significantly worse

outcomes compared to those who are not.[72]

In addition, many cancer patients suffer from hot flashes[66] similar to

those that occur during menopause. The loss of estrogen at menopause results in

decreased density of 5HT2A receptors and lower activity of serotonin, which could

explain aberrant temperature regulation, including hot flashes and night sweats.

Although the effects of temperature changes are felt throughout the body, 5HT2A receptors in the CNS are responsible for temperature regulation. Administration of drugs acting at the 5HT2A receptor restores normal temperature regulation 22

following ovariectomy [73] and chemically induced changes in body

temperature[74] The nighttime prevalence of hot flashes and night sweats could

be a result of the conversion of serotonin to melatonin at night, resulting in lower

circulating serotonin levels.[75] Phytoestrogens preferentially bind to ERβ

receptors[44] and are effective at reducing hot flashes and night sweats.[76] The

mechanism by which these compounds work could be an ERβ-produced

upregulation of 5HT2A receptors.

Two of the major side effects of hormone replacement therapy (the

treatment for hot flashes) and chemotherapy, are dizziness and nausea, which are

controlled in the CNS. The mechanism by which these side effects occur has not

been fully elucidated. It is possible that E2’s effect on serotonin pathways is

responsible for these symptoms. 5HT2A receptors activate vestibular neurons

which maintain balance [77] and are found in emetic centers, which are involved

in chemically-induced vomiting [78]. My hypothesis is corroborated by the use of serotonergic drugs to minimize these side effects of both treatments.[79]

Migraines are common among cancer patients[80, 81] and females are also at greater risk for headaches,[56] which can result from vasodilation in the brain [82]. Activation of an additional serotonin receptor, 5HT1B, is one

mechanism by which vasodilation occurs. 5HT1B receptors are not uncoupled by

E2 (unlike 5HT1A receptors), and their vasodilatory effect is typically balanced by

activation of 5HT2A receptors, which result in vasoconstriction [83]. After E2 23

exposure, increased serotonin concentrations result in greater activation of both

the 5HT1B and 5HT2A receptors. Under normal conditions, upregulation and

activation of 5HT2A receptors enable them to balance the effects of 5HT1B receptors [41, 42, 84]. I suggest that females’ increased headache risk might result if high serotonin concentrations are maintained without adequate compensatory

5HT2A activity.

3.3 Serotonin in the Musculoskeletal System

E2 and 5-HT also affect the skeletal system. As bones grow, they are

continually remodeled and reshaped. Normal bone development is affected by

growth hormone, parathyroid hormone, calcitonin, and environmental factors like

dietary calcium intake and physical activity. In addition to these factors, estrogen

and serotonin play an important role in the development and maintenance of bone

mass. For bone growth to occur, two types of cells are required: osteoblasts,

which form new bone, and osteoclasts, which resorb bone. During puberty,

osteoclasts and osteoblasts are in balance and resorb and build bone

simultaneously, but osteoporosis results when osteoclasts increase relative to

osteoblasts. These effects have been linked to E2 concentrations in both males

and females,[85, 86] and I propose that they can be explained by examining E2-

produced changes in serotonergic function in bone growth and loss. 5HT2A receptor activation causes an increase in expression of osteoblast progenitor cells,

maintaining bone density.[87] SERT activation, in contrast, increases osteoclasts in bone, aiding in bone growth in childhood,[88] but resulting in loss of bone density and increases in extracellular Ca++ postpartum[89, 90] and in menopause.

[91, 92] Studies of female mice lacking the ERα, the ERβ, or both suggest these

two receptors might counterbalance each other’s effects on longitudinal bone

growth,[93] with ERβ primarily responsible for decreasing bone growth and increasing bone resorbtion.[94] Because ERα and ERβ have opposing effects on serotonin systems, mediation by serotonin could explain E2’s effects on the skeletal system such as the decrease in bone density observed following menopause or when E2 function is otherwise compromised. However, bone loss begins around age 30 in men and women and this early bone loss cannot be entirely explained by differences in E2 concentrations or by my proposed model

[95].

In the muscle, 5-HT acts to regulate glucose transport by altering expression and insertion of GLUT transports independent of insulin. [96, 97] The serotonergic regulation of glucose transport is mediated via two different mechanisms. First serotonin increases GLUT4 insertion in the muscle membrane via the 5HT2A receptor.[97, 98] Second 5-HT increases transcription of GLUT4

and GLUT1 via a MAO-A mediate mechanism. [96] Both the GLUT4 and the

GLUT1 are passive transporters for glucose. The GLUT1 is highly expressed in

the placenta [99], while the GLUT4 is primarily expressed in more mature tissue.

[100]

Premenopausal women show decreased levels of insulin insensitivity

while following menopause the risk of insulin insensitivity is parallel to men if

not greater.[101] This suggests that E2 is protective against the development of

diabetes. It is paradoxical then that some women develop gestational diabetes

when E2 levels are the highest. [102] This suggests that some other mechanism is

also involved in glucose transport and regulation. There seems to be a strong relationship between circulating interlukins and the development of gestational diabetes, and a strong relationship with pre-eclampsia with gestational diabetes.

[102, 103] 5-HT is capable of altering both IL-6 and IL-8 levels, while MAO-A activity is altered in placental cells from pre-eclamptic pregnancies. [20, 104,

105] Cumulatively, these findings implicate the serotonergic system in the development of insulin insensitivity in females.

3.4 Serotonin in the Vascular System

In the vascular system, estrogen and serotonin have been shown to individually alter clotting, cholesterol, vasoconstriction, and heart attack risk

[106-109]. Both high and low levels of E2 have been associated with increased risk of thromboembolism; high levels result in increased clot formation, while low levels result in slower clot breakdown. Unusually high concentrations of estrogen

(beyond normal physiological levels) directly increase the likelihood of clotting by increasing production of clotting factors VII through X in the liver.[55] In

addition, these levels of E2 might increase clotting by increasing serotonin, which

is constitutively present in human plasma and platelets and works to promote

clotting[18] and increase density of platelets.[79] Increased clotting and

thromboembolism at low concentrations of E2[110] can also be explained using

serotonergic changes. Postmenopausal women have longer latency to lysis of

clots, and E2 replacement therapy returns latencies to pre-menopausal

levels.[111] Patients with slower clot breakdowns have decreased uptake and

release of serotonin from platelets,[112] and at low E2 levels serotonin’s ability to

break down clots via the 5HT2A receptor is limited,[113, 114] so I suggest that

lower serotonin activity associated with lower E2 levels could also contribute to

increased clotting.

Increased concentrations of E2 are also associated with decreased

cholesterol, and at menopause, there is an increase in total serum cholesterol,

which is reduced by estrogen-containing hormone replacement therapy.[115] I

suggest higher cholesterol after menopause is linked to the effects of serotonin.

Serotonin increases membrane fluidity by incorporation of cholesterol into

membranes, decreasing bioavailable cholesterol.[116, 117] Increased membrane

fluidity also increases serotonergic function, creating a positive feedback

loop.[118, 119] If serotonin is an intermediary between estrogen and cholesterol,

then in the presence of high concentrations of E2, I would expect more cholesterol

incorporated into membranes, thereby reducing cholesterol present in the plasma.

My hypothesis is supported since the administration of drugs that reduce

concentrations of serotonin in the plasma cause increases in plasma cholesterol

despite consistent levels of E2 [109].

Both clotting and cholesterol contribute to heart attack risk. Women are at lower risk of heart attack than men prior to menopause, but changes in the vascular system after menopause result in the loss of protection from heart disease.[55, 56] In females, recent evidence suggests that physiological levels of

E2 protect against heart attacks, while testosterone makes heart attacks more likely.[120] E2 acting at ERβ is responsible for this protective effect, as mice lacking ERβ have greater mortality and increased heart failure indicators following experimentally induced myocardial infarctions.[121] In addition antidepressants have been shown to be decrease the risk of myocardial infarction.[122] I suggest that these effects in females can be explained in part by serotonin receptor changes. Specifically, in the presence of physiological E2 and therefore ERβ activation, serotonin preferentially acts on 5HT2A receptors and to

reduce vasospasm in cardiac tissue. After menopause, when 5HT2A receptors

have been downregulated, serotonin instead acts on 5HT1A receptors, which cause

adrenergic stimulation of smooth muscle[123] and increase likelihood of cardiac

vasospasm.[124] This increases the risk of heart attack.[123, 125-127] In 28

addition, testosterone, which increases following menopause, compounds the

actions of serotonin at 5HT1A receptors by preventing desensitization of 5HT1A receptors.[128] These changes in sensitivity of cardiac vessels, combined with increased clotting and lipid levels, would be expected to increase heart attack risk, arteriosclerosis and strokes. However, E2 is not solely responsible for protection from heart attack, progesterone also plays a role. Hormone replacement therapy

(HRT) containing E2 and medroxyprogesterone instead of E2 and progesterone has been shown to increase heart attack.[129] Although the study showing increased heart attack risk during HRT is controversial,[130] it is possible that decreased concentrations of serotonin produced by treatment with medroxyprogesterone [124, 131] could contribute to this increased risk.

3.5 Serotonin in the Immune System

Both E2 and serotonin are also active in the immune system, and in this system, their interaction is well-documented. E2 suppresses major histocompatablilty complex II (MHC II) proteins in a tissue-specific manner[132] and acts centrally to suppress the immune system[133] by helping to activate

5HT2A receptors in the thymus.[42, 94, 134, 135] Estrogen treatment also indirectly suppresses MHC II protein expression via serotonin.[133, 136]

Specifically, increased 5HT2A activity causes decreased MHC II production,[137] and decreased selection against self-reactive helper T cells (TH1).[138] In 29

addition, the concurrent inactivation of 5HT1A receptors decreases TNF-α

production.[139, 140] Although self-reactive TH1 cells are present, I hypothesize

that E2’s suppression of MHC II prevents them from becoming activated, and

therefore while sufficient E2 is present they fail to attack tissues. Following

menopause, or when E2 levels are unusually low, suppression of MHC II and

immune function is lost, allowing self-reactive TH1 cells to become active and

pathogenic. It is possible that estrogen and serotonin’s modulation of the immune

system prevents immune attack on offspring during pregnancy (when estrogen is

at relatively high concentrations) and avoids infection after delivery (when

estrogen is relatively low).[141] Further high levels of E2 increases expression of indoleamine 2,3 dioxygenase (IDO) shifting tryptophan metabolism away from serotonin. Increased IDO has been implicated in both maternal fetal interactions and immune evasion by cancer. [142, 143] IDO acts by suppressing the ability of dendritic cells to active T cells through alterations in MHC expression. [136]

MHC II protein and self-reactive T cells appear to be the common

denominators among autoimmune disorders in women, suggesting a role for E2

and serotonin in mediating these disorders. Multiple sclerosis (MS) is associated

with the presence of MHC II protein polymorphic pathogenic alleles[144, 145]

and serotonin depletion.[146] This serotonin depletion could be a consequence of

low E2, so the decrease in MS symptoms during pregnancy [147] could be

explained by higher concentrations of E2. Also the severity of MS symptoms

increases as serotonin levels decrease[148], symptoms worsen in phases of the menstrual cycle when there is low E2[149], and low levels of E2 result in changes

in the 5HT signaling pathway [150]. In female SERT knockout mice, symptoms

of experimental allergic encephalomyelitis (a MS model) are less severe and have

a greater latency to occurrence, possibly as a result of increased serotonin

availability. [151] Not only may low serotonin levels be linked to MS, but the

effects of serotonin on MS may involve 5HT2 receptors in particular. Gene-

microarray analysis of brain lesions found lower 5HT2 receptor expression in all 4

MS patients compared to controls.[152] A potential mechanism by which MS

could be induced is through infection of the John Cunningham virus (JC virus).

JC Virus causes progressive multifocal leukoencephalopathy (PML) through

destruction of oligodendrocytes.[153] JC virus is a retrovirus and like other

retroviruses downregulates the receptor that it used to enter the cell. In the case of

the JC virus this receptor is the 5HT2A.[154]

Another possibility is serotonin depletion by conversion of serotonin to

melatonin in the absence of light. Melatonin is primarily synthesized in the pineal,

but has been shown to be produced in physiological relevant concentrations in

other tissues exposed to light such as skin and hair follicles. [155-158] Alterations

in serotonin concentrations associated with increased melatonin production could

potentially explain the increased incidence of MS in more northern climates [159]

(where daylight periods are shorter) and the reason that light therapy can be

effective in reducing symptoms of MS. [160] Similarly, self-reported incidence of Type I diabetes (IDDM) is negatively correlated with exposure to UV radiation and positively correlated with latitude in Australia.[161] Melatonin suppresses estrogen function [75] and suppresses 5HT2A receptor activity.[162] Further,

melatonin might be the link between E2 and helper T-cell (TH1) activity, as

melatonin has been shown to upregulate expression of TH1-stimulating factors

such as TNF-α and IFN-γ.[163] TNF-α increases the expression of MHC class II

proteins and activates TH1 cells, [164] which are hallmarks of MS.

Similar MHC class II polymorphisms and T cell dysfunctions have been

implicated in lupus,[165, 166] and lower levels of free tryptophan[167] and MHC

II protein overexpression is also linked to autoimmune attack on beta cells in

Type I diabetes (IDDM).[168] Overexpression of MHC II following failure to

select against self-reactive T-cells is also a useful model for rheumatoid arthritis,

Graves disease, and Hashimoto’s thyroiditis, in which T-cells react to proteins

produced in the body, failing to discriminate them from invading organisms.[169]

Women in whom estrogen-regulated serotonin signaling is compromised would

be expected to have higher levels of MHC class II protein expression and may

present these pathologies. However, simply over-expressing MHC II proteins is

not sufficient to activate the immune system and induce autoimmune

disorders.[169] The links between autoimmune disorders, serotonergic systems,

and E2 suggest that manipulation of serotonin or E2 could be used to successfully

32 treat these pathologies. Consistent with this suggestion, ER agonists reduce the symptoms of autoimmune disorders.[170, 171]

3.6 Serotonin in Cancer

After describing the physiological role of serotonin, I will use breast cancer as an example of the interaction between E2 and 5-HT and their relationship to carcinogenesis. Cataloging the interactions of serotonin with all the different types of cancer is beyond the scope of this work. However I can describe the basis for carcinogenesis before focusing on breast cancer. Carcinogenesis is conceptualized as consisting of three distinct phases: initiation, promotion and progression. Initiation is the irreversible alteration of a normal cell; promotion involves both proliferation of initiated cells and suppression of apoptosis of these cells; and progression is the irreversible conversion of one of the promoted initiated cells to an invasive, metastatic tumor cell.[172] Therefore, any endogenous milieu that induces apoptosis or suppresses mitogenesis of initiated cells could reduce breast cancer risk.

For breast cancer, one of the prevailing theories for the role of E2 is that longer duration of lifetime exposure to E2 is associated with increased risk, so that early menarche and late menopause result in greater likelihood of developing breast cancer. [173] Adding a role for serotonin does not conflict with this idea, but it does help explain several epidemiological findings that are not accounted

for by a relationship between increased E2 exposure alone and breast cancer.

First, the highest breast cancer incidence is in post-menopausal women, when endogenous E2 levels are much lower than before menopause. As described above, the higher E2 concentrations in the presence of progesterone prior to menopause cause an increase in 5HT2A receptor density and serotonin activity that

promotes apoptosis. In contrast, 5HT1A activation (which occurs preferentially

after menopause) decreases apoptotic signaling via caspase-3 suppression.[52]

Therefore, if E2 is acting on breast cancer in part by serotonin modulation, then I

would predict that the decrease in E2 after menopause should increase risk of

breast cancer. This is consistent with the observed breast cancer incidence

curve.[174] The failure of low levels of E2 to inhibit cancer growth is also

reflected in patterns of tumor development within the estrous cycle. In mice,

breast tumor growth occurs primarily in diestrus (when E2 is low), and tumor size

is maintained or shrinks when E2 levels are high.[175] In conjunction with the

alterations in serotonin receptor distribution the loss of E2 could affect cells by

altering the metabolism of tryptophan to serotonin and instead form

arylhydrocarbon agonists. The Arylhydrocarbon receptor is ligand bound nuclear

receptor that belongs to the Per-ARNT-Sim family. The only known natural

ligands of the AhR are products of a reaction between Aspartate

Amniotransferase (AST) and tryptophan. The loss of E2 increases free AST

34 levels that alter the metabolism of amino acids. [176] This alteration could result in the production of AhR agonists from tryptophan.[4]

Second, in Pike’s Breast Tissue Age model, a one-time rapid increase in breast tissue age and therefore breast cancer risk is included immediately following the first full-term pregnancy.[177] The extension of Pike’s model includes multiple births by incorporating smaller increases in risk at each additional full-term pregnancy.[178] This pattern of increased risk for breast cancer immediately following full-term pregnancies is well-documented.[179-

181] E2 concentrations increase steadily during pregnancy, peaking at about 100 times normal cycling levels.[15] In the days around parturition, these concentrations drop precipitously to levels below those of normal cycling females, where they are maintained for at least a month and potentially much longer (depending on suckling suppression). [182] The observed increase in breast cancer risk can be accounted for by the concurrent decrease in E2 and therefore changes in 5HT2A receptor function immediately prior to parturition.

The loss of E2's effect on serotonin could account for the immediate increase in risk. Mammary involution is mediated by serotonin and in the absence of serotonin cells that were meant to undergo apoptosis remain[183]. However this cannot explain the long-term reduction in risk, which is likely related to other changes associated with parturition or lactation.

Third, obesity exerts differential effects on breast cancer risk over the

lifespan; decreasing risk prior to menopause and increasing risk following

menopause.[184, 185] Under the prevailing theory of cumulative E2 exposure,

obesity (which increases E2 levels[186]) would always be expected to increase

breast cancer risk. However, the effect of E2 using serotonin mediation described

above can account for the observed differential effects. Increased E2 in the

presence of progesterone increases activation of 5HT2A receptors, while increased

E2 in the absence of progesterone increases activation of 5HT1A receptors. The effects of these two receptors on apoptotic activity would predict that obesity exerts a protective effect before menopause and increases risk after menopause.

The importance of the presence of progesterone for this protective effect is underscored by recent HRT studies, which show that the use of estrogen and progesterone does not increase breast cancer risk,[187] while the use of estrogen

and medroxyprogesterone (which decreases serotonin in some tissues[26, 188]) has been shown to increase breast cancer risk. Consistent with the observed

effects of HRT, oral contraception with Depo-Provera,® which includes

medroxyprogesterone rather than progesterone, has been shown to increase breast

cancer risk.[187, 189] This is in line with the findings that selective serotonin

reuptake inhibitors (SSRIs), which increase serotonin levels, have been shown to

decrease the incidence of cancer in both animals and humans [190, 191]. The

36 increased serotonin levels that result through E2 upregulation or SSRIs may be responsible for the protective effect both exert on cancer.

3.7 Summary

Most research on pathologies in women's health has centered on changes in E2. My review of data from a variety of fields suggests that serotonin is one of many ways that estrogen exerts its effects on physiology and pathology. There is a body of literature that shows many of the effects specifically attributed to estrogen receptor function occur further downstream as a result of alterations in various pathways. Theses pathways ultimately relate to the primary function of

E2, which is reproductive. And serotonergic mediation of the estrogen system likely provides reproductive benefits that are not yet understood. Several of the effects I have discussed produce reproductive benefits: immune suppression during pregnancy decreases the chance of lost pregnancies, postpartum activation of the immune system increases antibodies in milk, increased clotting and vasoconstriction in the uterus prevents bleeding during birth, and increased available calcium during lactation improves the quality of breast milk. Notably, the same mechanism that results in these potential benefits in the reproductive system also produces changes in the remainder of the body that have consequences in physiology and pathologies. Whether the potential reproductive benefit of these effects is adequate to account for the maintenance of the

estrogen/serotonin link remains to be explored. I suggest serotonergic mediation

might contribute to explaining E2’s effects on some pathologies, including heart

attacks, multiple sclerosis, and in particular cancer. Drugs that increase 5-HT

induce apoptosis of tumorous tissue both in vitro and in vivo.[192, 193]

However, serotonergic involvement in cancer has only been minimally described. Fortunatly, the role of E2 has been extensively studied in many cancers. E2 as a risk factor has been most studied in relation to breast cancer, but increased levels of E2 have also been associated with increased risk of other reproductive cancers in both women and men. [5] [194] [195] [196] Additionally

E2 has been observed to be a risk factor for liver cancer [197] while it appears to be protective for colon.[198] The mechanisms of these associations have not been fully elucidated. These relationships between cancer risk and E2 can be better understood in the context of serotonergic signaling. Even though different cancers seem disparate there are commonalities, as shown in the “Hallmarks of

Cancer”[199] Here, the role of 5-HT in cellular physiology suggests serotonin pathways may be involved in the initiation, promotion, and/or progression of cancers. Altering specific aspects of the serotonergic system, rather than simply increasing E2, could allow clinicians to target treatments in particular tissues or towards particular receptor types, alleviating undesirable side effects of E2

administration.

CHAPTER 4

HYPOTHESIS TESTING OF THE TRYPTOPHAN/SEROTONIN

METABOLIC PATHWAY

One of the key goals in cancer research is to identify biological changes

that distinguish normal tissue from cancerous tissue. A common approach to

identifying oncogenes has been to assess gene expression in each type of cancer

and compare it to non-cancerous tissue of the same organ. Comparisons within a

single cancer type (e.g., breast cancer) or class (e.g., leukemia) have yielded

potential oncogenic mechanisms that have been successfully used to develop

therapeutic strategies for individual cancer types. For instance, real-time

polymerase chain reaction (qPCR) research has found increased expression of

oncogenes like c-myc [200] and decreased expression of tumor suppressors like

Rb [201]. Western blotting has been used to show overexpression of functional

erb-B2 in breast cancers [202] and ovarian cancers [203]. Unfortunately,

comparing the data obtained from studies of individual types of cancer has resulted in only limited success at detecting consistent changes among different

types of cancers. One such success is the identification of a mutation in p53, a

protein responsible for repairing cellular DNA, which occurs in approximately

50% of all cancers [204]. The discovery of similarities among various cancer

39 tissues is the first step in identifying a common mechanism that contributes to the development of cancer. Once a change is identified, appropriate therapeutic targets can be developed to help physicians identify at-risk individuals and improve patient care. Indeed, novel therapeutic strategies have been developed as a result of the extensive study of p53 [205].

Although best known as a neurotransmitter, only 1% of the tryptophan

(Trp) derivative, serotonin (5-HT), is found in the nervous system. The remaining serotonin is found in the periphery and is active in the immune, circulatory, reproductive, musculoskeletal, and gastrointestinal systems [8]. Depending on receptor distributions, serotonin activity can promote or reduce apoptosis [52].

My previous work explored the activity of serotonin in an array of pathologies, particularly those in which epidemiological data suggests gender differences [8]. I propose that estrogenic effects on serotonergic function and receptor distribution could explain gender differences in pathologic incidence, as well as some of the effects of estrogen on breast cancer. Rather than being specific to breast cancer, the role of serotonin and its precursor Trp in cellular physiology suggests that the metabolic pathway of tryptophan and as a result serotonin metabolism may be involved in the promotion or progression of cancers in general. There is some literature supporting this hypothesis [142, 191, 193, 206-211], but further research is needed to understand the exact relationship between Trp or its metabolites and cancer. Recently a mechanism was proposed by which catabolism of Trp by

indoleamine 2,3-dioxygenase (IDO) can be linked to immune evasion in tumor

cells [142, 212]. Other studies suggest that decreased serum tryptophan levels are

predictive of poorer prognosis and quality of life in cancer patients. [208] For

serotonin specifically, studies have shown that the selective serotonin reuptake

inhibitors (SSRIs) which prevent the reuptake of serotonin thus increase

extracellular serotonin levels, have anti-cancer activity in cancer cell lines[211],

decrease incidence of cancer in both animals [190] and humans [191], and can be

used as a treatment for lymphoma/leukemia [213].

Using genechip technology to study multiple types of cancer

simultaneously can identify whether multiple cancers have similar gene

expression changes [214]. Knowing that tryptophan/serotonin metabolism is

linked to carcinogenesis I hypothesized that it was altered in multiple cancer

among a variety of species. To test this hypothesis, I analyzed an ensemble of

cancer genechip datasets focusing on genes involved in tryptophan metabolism,

which include serotonergic genes among them. I first compare gene expression

between cancerous tissues and normal tissues for each type of cancer and then identify changes that are common to a variety of cancer types. Using this technique, I will be able to identify the relationship between the cancer and the serotonergic system.

4.1 Methods

19 genechip datasets consisting of cancerous tissue from 10 different

organs derived from human, mouse, rat or zebrafish were extracted from the GEO

profiles database. Datasets were first identified by using the search terms “cancer”

or “metastasis”. All datasets in the GEO profiles database as of March 31st 2007

were considered. The genechip data was selected such that both control and

cancer samples were contained in the same dataset. Because of the differences in

gene expression that are inherent to cell culture [215], only data derived from

primary biopsies were used. By definition cancer implies an invasive phenotype therefore all other tumor types were excluded such as adenomas, and carcinomas in situ. Among datasets that contained multiple types of cancer, the cancer with the closest number of samples to the control was used for analysis.

Appendix A, Table 1 describes the datasets used, which include 12 human

cancer datasets, five mouse cancer datasets, one rat cancer dataset, and one

dataset from zebrafish. All animal datasets were derived from cancers that were induced using viral, genetic, or by chemical means. No xenografts were included in this analysis. In total, 242 cancerous samples and 139 control samples were used. Prior to analysis, data that was logarithmic was transformed back to its original values, and all “null” values were excluded. Data points that were from more than one patient, also called pooled samples, were also excluded. Previous reports have shown that probe-sets cannot be averaged [9] therefore all analyses

were preformed on the probe-set with the highest mean expression value. Each

genechip dataset was normalized (both intrachip and interchip) before being

deposited in the GEO database. I independently validated the normalization in

every dataset by inspecting the distribution of expression values. Datasets that

were not appropriately normalized were excluded.

My initial interest was in tryptophan-related genes, so I examined all

human, mouse, and zebrafish tryptophan pathway genes (~60 genes depending on

the species and gene chip) listed in Kyoto Encyclopedia of genes and genomes

(KEGG) using BRB-array tools [216] developed by the National Cancer

Institute’s Biometrics Research Branch (NCI BRB). Appendix A, Table 2 lists all

the tryptophan genes in KEGG. First, differences in the expression levels of the

genes were examined within each of the individual cancer datasets. I performed

either related samples or independent samples t-tests (as appropriate, see

Appendix A, Table 1) on the change in expression of the genes between control

and cancer samples for each dataset. Since I focused on the behavior of individual

genes across multiple types of cancer rather than groups of genes in individual

datasets I only compensated for the multiple t-tests over the composite number of

datasets. The probability that I would observe the same false positive from among

approximately 60 genes, in at least 16 out of the 19 separate datasets, is at most of

the order of 7.8x10-17, an unlikely outcome. Therefore finding one specific gene

(MAO-A) which is consistently down-regulated in 16 out of 19 (using the

Bonferroni-Holm or BH correction) datasets is an indication that the observed

down-regulation of MAO-A in cancer tissues cannot be attributed to chance

alone. Nonetheless, I did adjust for multiple t-tests among the 19 datasets by using

the Bonferroni-Holm adjustment [10, 217]. Specifically, I sorted the p-values for

t-tests of expression of each gene from smallest to largest and compared the i-th

p-value to the original alpha level (0.05) divided by the number of data sets+1-i.

Thus I compared the smallest (first) p-value with 0.05/18 = 0.0028 and the largest

(last) p-value with 0.05/1 = 0.05. For each dataset a list of tryptophan related genes whose expression level was significantly changed using these criteria was generated. Then the frequency of each gene appearing in all the lists was counted and the genes were sorted by the frequency from high to low. Only MAO-A was differentially expressed in the majority of the datasets analyzed.

4.2 Results

By conducting a series of analyses focusing on tryptophan related gene expression data (see Appendix A, Table 2 for a list of genes analyzed) in the GEO database (gene expression omnibus) maintained by NCBI [186], I found that only

Monoamine Oxidase A (MAO-A, E.C. 1.4.3.4) showed consistent decreased expression, in cancers among a variety of tissues from humans, rodents, and zebrafish. Specifically, only MAO-A expression was significantly altered in all 13 of the datasets that used non-cancerous patients as controls and half of the paired

44 datasets. Appendix A, Table 1 provides specific p-values and the mean fold change in MAO-A for the datasets analyzed. Although the extent of downregulation varied among patients, cumulatively 95.4% of all of the tissue samples from human cancer patients, and 94.2% of all animal cancer cases showed lower MAO-A expression than the single lowest control sample in their respective dataset. Changes in expression for unpaired data are provided in

Appendix B, Figure 2.

Within each dataset, between 67% to 100% of patients had MAO-A expression below the lowest control sample (see Appendix A, Table 1 for individual values). Examining data from individual patients among the paired data revealed a remarkable pattern of downregulation in cancerous tissue among paired samples analyzed. Only a subset of the datasets that compared cancerous tissue to normal tissue from the same patient failed to show significant downregulation.

The three datasets that did not contain a significant shift in MAO-A expression after correcting for multiple t-tests were the two papillary thyroid cancer datasets and one gastric cancer dataset. However 69% of thyroid cancer patients and 100% of gastric cancer patients exhibited a decrease in expression compared to non- cancerous tissue from the same patient. A possible explanation for this discordance is that a less pronounced downregulation of MAO-A occurs in patients with these two types of cancer.

4.3 Discussion

Most current cancer research is focused on tissue-specific genetic

mutations. Familial inheritance (e.g., APC in colon cancer), genetic mutation

(e.g., p53), and overexpression of growth receptors (e.g., Her2-neu in breast

cancer) can each lead to aberrant replication of a cell. Studies of these changes

provide tremendous information about tissue-specific effects but are less

informative about common changes that occur in multiple tissues. The similarity

in the behavior of cancers from different organ systems and species indicates the potential for a universal change among cancers, regardless of the specific tissue or

species. This study suggests that downregulation of MAO-A is such a change and

could be an important indicator or even a factor in the development and spread of

many types of cancers.

4.4 Summary

Identifying consistent changes in cellular function that occur in multiple

types of cancer could revolutionize the way cancer is treated. Previous work has

produced promising results such as the identification of p53. Recently drugs that

affect serotonin reuptake were shown to reduce the risk of colon cancer in man.

Here, I analyzed an ensemble of cancer datasets focusing on genes involved in the

tryptophan metabolic pathway. Genechip datasets consisting of cancerous tissue

from human, mouse, rat, or zebrafish were extracted from the GEO database. I

46 first compared gene expression between cancerous tissues and normal tissues for each type of cancer and then identified changes that were common to a variety of cancer types. I found that significant downregulation of MAO-A, the enzyme that metabolizes serotonin, occurred in multiple tissues from humans, rodents, and fish. MAO-A expression was decreased in 95.4% of human cancer patients and

94.2% of animal cancer cases compared to the non-cancerous controls. These are the first findings that identify a single reliable change in so many different cancers. My next study investigated the link between MAO-A suppression and the development of cancer to by analyzing the entire genome in order to determine the extent that MAO-A suppression contributes to cancer risk.

CHAPTER 5

WHOLE GENOME ANALYSIS

Genetic causes are the mostly cited reasons for initiation and development of cancers. During the past 40 years, a tremendous amount of research efforts have been spent on identifying genes related to cancers including proto-oncogenes

like MYC, ERK, EGFR, KRAS and the tumor suppressors such as p53, PTEN,

Rb. According to the American Cancer Society there are more than 100

recognized oncogenes and about 30 tumor suppressor genes [218]. In spite of the

vast number of candidates no common genetic mechanism has been identified that

is applicable across cancers despite the phenotypic similarities. For any type of

cancer, usually multiple genes have been identified; for instance, BRCA1 and

HER2 in breast cancer,[219, 220] or KRAS and AXIN2 in lung cancer. [221, 222]

The abundance of potential candidate oncogenes in combination with the vast

amount of tumor suppressors has lead to the notion that “no two cancers are

alike”. Further enforcing this idea was a 2007 Science publication that compared

common mutations found in breast cancer patients with those found in colon

cancer patients. This study identified a list of more than 200 gene mutations for

each type of cancer.[223] Yet these results were based on only 11 patients from

each type of cancer. The two mutation lists shared very little similarity except for

the well-known tumor suppressor p53.

In spite of the dramatic difference between the genotypes of different cancers,

the fact that they are all histologically classified as “cancer” implies a strong

similarity in their pathology. These common phenotypes among the cancers are

well accepted and have been summarized in the paper “Hallmarks of Cancer” in

which six common traits of cancer are discussed. [199] According to Hanahan

and Weinberg these common traits are:

• “self-sufficiency in growth signals,

• evading apoptosis,

• insensitivity to anti-growth signals,

• sustained angiongenesis,

• limitless replicative potential,

• tissue invasion & metastasis”.

For each of these cellular traits there is a corresponding biochemical trait that is influenced by dynamic genetic expression changes that impact not only the cell

but the microenvironment as well. It seems fallacious that viruses, chemicals, or

other factors lead to mutations or deletions in so many diverse oncogenes or

tumor suppressors that then act on the same repertoire of pathways in a variety of

different tissues and species.

To identify a potential mechanism, that might explain why the variety of different means of cancer initiation result in similar cellular physiology I applied a well established statistical approach from quality control to microarray datasets.

This novel approach allowed me to compare gene expression profiles form multiple datasets. This application is novel since there I used the 19 datasets from my earlier serotonergic metabolism analysis (Group A). I also wanted to increase my sample size, therefore I relaxed my inclusion criteria and arrived at 40 unique datasets across multiple species (Group B). To assure that the gene expression profiles were relevant to human cancers, I restricted my subsequent analysis to the

32 human cancers datasets (Group C). Using this technique, I was able to identify genes that were differentially expressed in a number of different cancers, with the majority linked to the regulation the G2/M checkpoint.

5.1 Methods

Related work which was published in 2004, compared the gene expression profiles for six different types of cancers and generated a list of common genes.

[214, 224] This work is the basis for the well-known gene expression data exploration portal Oncomine, that has since gone through several iterations. [225]

My work shares some similarities in the concept of comparative analysis, but my methods differ significantly in terms of the objective, the inclusion / exclusion criteria for datasets, statistical methods, and pathway analysis. With the large

50 amount of new datasets available after 2004, I can apply more stringent selection criteria and obtain results with a higher degree of specificity and confidence.

5.1.1 Dataset Collection

The original 19 genechip datasets identified for the serotonergic analysis were used for the initial first set of analyses. Appendix A, Tables 1 describe the datasets used, which include 12 human cancer datasets, five mouse cancer datasets, one rat cancer dataset, and one dataset from zebrafish. All animal datasets were derived from tumors that were induced in the animal using viral, genetic, or chemical means. No xenografts were used in this analysis. In total the original 19 datasets consisted of 242 cancerous samples and 139 control samples.

After analyzing the original 19 datasets, I relaxed my inclusion/exclusion criteria and expand my analysis to include extra datasets that became available after

March 2007. I also included the results of the search terms sarcoma, lymphomas, and brain cancer. In my previous analyses, if there was more than one type of cancer present within a dataset, I only used the cancer that had the closest number of samples to the control. Here I analyzed each type of cancer in every dataset independently. Appendix A, Tables 3-5 describe the datasets used.

5.1.2 Dataset Handling

Prior to analysis, data that was logarithmic was transformed back to its

original values, and all “null” values were excluded. Data points that were from

more than one patient, also called pooled samples, were excluded. Previous

reports have shown that probe-sets cannot be averaged [9] therefore all analyses

were performed on the probe-set with the highest mean expression value. Each

genechip dataset is normalized (both intrachip and interchip) before being

deposited in the GEO database. I independently validated the normalization on

every dataset by inspecting the distribution of expression values.

5.1.3 Gene Selection

For each dataset, differentially expressed genes were identified using

BRB-array tools [216] developed by the NCI Biometrics Research Branch. First,

differences in the expression levels of the genes were examined within each of the

individual cancer datasets. I performed either related samples or independent samples t-tests (as appropriate, see Appendix A, Tables 3-5) on the change in expression levels of the genes between control and cancer samples for each dataset.

The probability of inferring that a particular gene is differentially

expressed in one or more given datasets is a function of alpha level (usually 0.05).

Normally, in bioinformatics, this level of an alpha leads to significant Type I

error. Under the null hypothesis of no differential gene expression, the expected

percentage of Type I errors in any one dataset is 100. So for any given

dataset with tests of significance, the number of false positives will be

approximately. Therefore analyzing a dataset that consists of genechips

each containing 22,000 probesets, at 0.05, on average would result in

22,000 0.05 1,100 false positives. However, analyzing two independent

datasets using the same threshold, the expected number of common false positives

would be 22,000 0.05 0.05 55. Among three datasets, the expected

number of common false positives is 2.75, and among four datasets, this expected

number is 0.1375, (e.g. less than one).

The general formula for the number of expected common false positives is

where the number of tests is , is the significance level, and is the number of datasets. Hence, rather than avoid Type I error in any individual dataset, I was willing to tolerate it given that as the number of datasets analyzed increased the probability of committing the same Type I error in multiple datasets decreases exponentially. This is one reason that I did NOT compensate for

running multiple t-tests among datasets using methods such as the Bonferroni

correction or false discovery rate (FDR). [10, 226] Given the number of datasets 53

that I was using, methods such as the Bonferonni correction were overly stringent

and unnecessary.

There was also a pressing biological reason that compelled me not to

compensate for running multiple t-tests. My goal was to determine the global

behavior of each gene in multiple datasets rather than its action in any single

dataset. Therefore the selection criterion for putative differentially expressed

genes had to remain the same for all the datasets. Methods such as the FDR

determine significance for each dataset independently [226] and as a result were

not suitable for my purposes.

For each dataset, the genes with p-values (for the t-tests) less than 0.05

were put into a list. Then, the lists for the datasets were aggregated and the

occurrence frequency of each gene in the list was counted. For a gene A, I denote

its occurrence frequency as . In addition, I denote as the total count

that the gene A is truly differentially expressed. Instead of compensating for running multiple t-tests, I rank the genes based on occurrence frequency function

. . The genes with differential expression in more than half of the datasets (e.g. in at least L/2 of the L datasets in my study) are considered to be genes that have a high frequency of differential expression and are selected for further analysis. In order to assess the likelihood that a gene is truly differentially expressed in at least half the datasets I used the following function.

Given a gene B with where , I denote the probability that

it is truly differentia lly e xpressed in exactly k datasets as

| (1)

Therefore I would select gene B if

| ∑ 0.95 (2)

In which

N 0.05 1 0.05 (3)

! Where is the number of choices of k from N. Therefore I obtain the !!

following lower bound for the left side of the inequality (2).

N | ∑ 0.05 1 0.05

. ! 1 (4) ! !

Based on the above formula, I know that the inequality (2) will always

hold if the right side of the inequality (4) is larger than 0.95. The last right-hand

side of Equation (4), is the cumulative distribution function (CDF) of the Beta

distribution. For a given L, the minimum N that would make the right-hand side of

(4) greater than 0.95 can now be evaluated. For a large L, an approximate asymptotic solution for N is given by the following equation:

1.645 0.051 0.05 (5)

As a result, I generated a list of differentially expressed genes for each

dataset and then selected the genes such that I had at least 95% confidence of them being truly differentially expressed in over half of the datasets. Appendix B,

Figures 3-5 show the graphs of the Beta CDF that determines the threshold for the

datasets. As a negative control, I also randomly selected six unrelated diseases.

Among the negative controls there was no gene with a frequency six. I tested

several such cases and obtained the same results (data not shown).

5.2 Results

The frequency functions for all the genes were computed and a histogram

of the frequencies is shown in Appendix B, Figure 6 which represents the

histogram for the original 19 datasets (Group A). The histogram shows a reversed

sigmoidal shape with the highest frequency values on the x-axis representing only

select genes. Appendix B, Figure 7 shows the histograms generated from 40 datasets (Group B). For the negative control, the frequency function drops exponentially and does not appear sigmoidal. This observation implies that the 56

sigmoidal shape for the histogram for the cancer datasets is due to similar gene

expression profiles rather than an artifact. It is also important to note that multiple

types and brands of genechips were used thereby eliminating probeset bias.

5.2.1 Frequency of differential expression for genes

Using my equation, I found that genes must have been differentially

expressed in more than 11 out of the 19 datasets (Group A) and more than 22 out

of 40 (Group B) datasets to have 95% confidence. In both cases, MAO-A is one

of the most frequently differentially expressed genes. The top genes with a

frequency of significance in more than 11 datasets are listed in Appendix A, Table

6, and Appendix A Table 7 lists the top scoring genes in more than 22 out of 40

datasets.

Out of the total 29,061 probesets surveyed in the 40 datasets, only 628

genes were significant and made my list. The list was then imported into DAVID

(http://david.abcc.ncifcrf.gov/home.jsp) and the ontologies were enriched. Table 7

in Appendix A provides a list based on frequency, and Table 8 provides the

DAVID results. I then analyzed the list of genes using Ingenuity Pathway

Analysis (IPA) to identify metabolic and signaling pathways. IPA uses a right- tailed Fisher's Exact Test to calculate the over represented networks/functions/pathways for a given set of genes. Using IPA, I found that among different species (Group B) 355 of the genes out of 628 genes identified,

57 have been shown to be closely related to cancer in previous studies. This suggests that there are many genes such as MAO-A that are closely related to cancer but have not been studied in that context. IPA also identified the top metabolic and signaling pathways that were differentially expressed. The top signaling pathway was Hepatic Stellalte fibrosis followed by Arylhydrocarbon Receptor Signaling.

Figure 8 in Appendix B shows the graphs representing the significance of the various pathways and Appendix A, Table 9 provides the genes listed by pathway.

After correcting for multiple tests the only metabolic pathway that was significant was nicotinamide metabolism.

5.2.2 Human Genes

To remove any species specific effects, I restricted my data to only human datasets. Figure 9 in Appendix B shows the distribution of significant genes in humans. It is notable that the curve is no longer strictly sigmoidal. Using my equation, I set the threshold for significance at 19 out of 32. Among human datasets, 21,154 genes were differentially expressed in at least one dataset. The total number of significant genes that were differentially expressed in more than

18 datasets was 1,142. These genes were again classified by DAVID. Table 10 in

Appendix A lists the genes by frequency, and Table 11 by DAVID classification.

Using IPA, I identified the most over represented pathways. Interestingly, among

58 signaling networks, Arylhydrocarbon signaling was the most overrepresented pathway. The top six networks identified by IPA were:

• Aryl Hydrocarbon Receptor Signaling

• Glucocorticoid Receptor Signaling

• Hepatic Fibrosis / Hepatic Stellate Cell Activation

• Actin Cytoskeleton Signaling

• ERK/MAPK Signaling

• Leukocyte Extravasation Signaling

Each pathway remarkably corresponds to one of the six hallmarks. Appendix B,

Figure 10 and Table 12 in Appendix A provides the list of differentially expressed networks. No metabolic pathways were differentially expressed after correcting for multiple tests.

5.3 Discussion

A review of the literature reveals that serotonin, the major metabolite of

MAO-A, is involved in Arylhydrocarbon Receptor signaling [4], a known carcinogenic pathway. However, the majority of studies conducted on the function of AhR have been done in cell culture using serum. One of the major stores of serotonin in vivo is platelets and upon clotting they release large amounts of serotonin. A difficulty in studying the effects of MAO-A and other serotonergic pathways and how they relate to cancer in cell culture is the

59 abundance of serotonin and other factors that directly affect this mechanism in serum. Aspartate Amino Transferase (AST) is needed for serotonergic precursors to be conjugated into an AhR agonist. AST is another major protein found in serum and its levels vary depending on the handling, and temperature of the serum. Interpreting results obtained from cell culture experiments is therefore confounded by the alterations in plasma AST following handling as well as the abundance of serotonin found in serum. My samples were all primary tissue that had not been cultured. Therefore, I was able to isolate this pathway since it was not activated in my control tissue.

5.4 Summary

Using the novel statistical analysis created to test for serotonergic genes, I was able to identify genes that were differentially expressed in a number of different cancers in different species. This analysis produced a list of 628 genes which show differential expression in at least 13 types of cancers. By using IPA ontology enrichment and pathway analysis to discover the relationships between these genes and their respective function I found that they were highly related to the six common traits of cancer. However, there are several genes that have been rarely related to cancer in previous research such as MAO-A yet they are somehow related to the hallmark features. My findings lead to many hypotheses regarding the mechanism of cancer initiation, promotion, and progression.

CHAPTER 6

CORRELATING MAO-A EXPRESSION TO IDENTIFY DIFFERENTIALLY

EXPRESSED PATHWAYS

I previously showed that regardless of the cancer site, method of induction, or species, a single gene, MAO-A, is reliably downregulated.[227]

MAO-A is the enzyme responsible for the metabolism of serotonin and to the best of my knowledge there is no published study of MAO-A knockout mice that describes an increased incidence of cancer. The lack of a strong association between MAO-A knockouts and cancer suggests that the observed downregulation of MAO-A is more likely an indicator of an underlying cellular process rather than MAO-A being directly involved in carcinogenesis. The most widely accepted theory regarding cancers is that genetic instability resulting from multiple mutations leads to the cells acquiring hallmark characteristics.[199] The above hallmarks are associated with cancers induced by viruses, carcinogens, in addition to genetic mutations. To identify how MAO-A could be related to these hallmarks and the observed the genetic mutations in cancer, I conducted multiple correlations on the paired human data in my dataset to find MAO-A associated genes.

6.1 Methods

Previous work done only explored correlated genes in cell culture and did not explore the difference between normal and diseased tissue. [7] To test if there are detectable correlations within the diseased patients, I examined the genes that correlated with MAO-A across normal and diseased tissue from the same individual. The use of correlation techniques has already been shown to be effective in silico and has been biologically validated in vitro. [7]

6.1.1 Dataset Selection

Genechip datasets were selected from the GEO database [186] maintained by NCBI using previously described criteria. In addition, only datasets that contained paired data were selected. Table 4 in Appendix A describes the datasets used and the number of patients in each dataset. There were 8 human cancer datasets containing 64 patients each with a normal and diseased sample. Applying a paired t-test with a p≤0.01 all but one dataset showed significant down regulation of MAO-A. Comparing the expression levels of MAO-A in normal tissue to levels in diseased showed that 89% of individual patients showed downregulation.

6.1.2 Correlations

I calculated Pearson correlation between MAO-A expression levels and

expression levels of every gene on the genechip to identify which genes correlated

with MAO-A within each dataset. For each pair of genes , Ω I compute the Pearson correlation , as

∑ ,

Note that the value of n is identical for all genes within the same dataset.

Since I was interested in identifying a potential network, I targeted genes

whose expression highly correlated with MAO-A expression. I used a threshold of

|r|≥0.4 for highly correlated. This threshold has previously been shown to be

adequately stringent. [7] The individual lists of correlation coefficients were

filtered so that genes with a correlation coefficient of |r|≥0.4 in all the datasets

were excluded. The lists of correlated genes were then merged and sorted based

on the number of datasets in which a gene was significantly correlated with

MAO-A in a specific direction. I selected genes that appeared in more than half of

the datasets which represented the top 1% (392/37,872) of genes. The top 392

genes were imported into IPA and analyzed to determine what pathways were

involved.

6.2 Results

Among the 392 genes that were identified as being highly correlated with

MAO-A, IPA analysis of signaling and metabolic pathways identified key

regulatory networks. Among signaling pathways, genes involved in Hepatic

Stellate Cell activation were the most abundantly correlated with MAO-A

followed by the genes involved in the G2/M checkpoint. Genes from the AhR

signaling pathway were also identified as correlates of MAO-A expression levels.

Among metabolic pathways, branched chain amino acid metabolism was the most

highly related followed by propanoate metabolism. In addition,

glycolysis/gluconeogenisis was highly related to MAO-A expression levels. A

table with all significant signaling pathways and a graph is included in Appendix

A, Table 13 and Appendix B, Figure 11, respectively. Visual examination of the

various pathways revealed the genes which drive the production of lactate and

genes that suppress G2/M checkpoint were negatively correlated (upregulated)

with MAO-A. Genes which positively correlated with MAO-A (downregulated)

were involved in the metabolism of pyruvate and branched chain amino acids.

6.3 Discussion

Hepatic Stellate Cell activation occurs via one of the serotonin receptors,

the 5HT2A, and leads to proliferation of stellate cells and liver fibrosis.[228] My

data provides support for my earlier hypothesis that one way serotonin mediates

64 carcinogenicity is through receptor activation. The correlation analyses also indicated a strong link between MAO-A downregulation and activation of AhR signaling. In addition, MAO-A downregulation was highly linked to G2/M checkpoint suppression and gluconeogenesis. Visual analysis of the pathways showed that only specific genes in the G2/M checkpoint and glycolysis/gluconeogenesis were negatively correlated (upregulated) with MAO-

A. Appendix B, Figures 12, 13, and 14 show the genes which correlated with

MAO-A in the G2/M and glycolysis/gluconeogenesis pathways.

6.4 Summary

By using correlations to identify networks that had a functional relationship to MAO-A downregulation, I found that MAO-A downregulation was related to the most basic functions of cellular physiology. These correlated networks regulate replication and energy balance. After inspecting the data I was able to identify how that these functions relate to MAO-A downregulation. Two of the key findings were that in G2/M signaling, only suppressive genes were correlated and among genes involved in glycolysis/gluconeogenesis only genes that were related to gluconeogenesis were correlated with MAO-A downregulation. These changes would cause an increase in the number of mutations that each daughter cell would carry and increased concentrations of lactate. My findings are consistent with the established literature showing that

65 cancers contain a greater proportion of mutations than non-cancerous cells. and contain increased concentrations of lactate.

CHAPTER 7

CONCLUSIONS AND FUTURE DIRECTIONS

Current techniques for gene selection using multiple datasets, such as

Oncomine [214, 225], have not been able to identify the change in MAO-A detected here. One reason is the amount of data manipulation that is involved in these techniques. Manipulating data is subject to the effects of the data processing inequality. This inequality can be summarized by saying that the more data is manipulated the more information is lost. The data processing inequality is one of several reasons that in this manuscript I applied a high level three tier approach to the analysis of multiple datasets. At each of the different tiers, I conducted the analysis within the original dataset and did not have to manipulate the data, preventing the loss of information. The tiers consisted of hypothesis testing on individual pathways to identify significant expression changes within each dataset, intersection of results between different datasets to find common functions across experiments, and Pearson correlations between individual genes to identify correlated genes within each dataset. Each different tier required different types of analysis to provide distinct types of information with complementary implications.

The first tier found that MAO-A downregulation is pervasive in multiple

types of cancer. The second tier, which analyzed the entire genome, detected a

significant amount of genes that were common in multiple types of cancer across

species. More importantly many of the genes detected have previously been identified as being involved in carcinogenesis, suggesting that the other genes

found using this approach warrant further study. The genes identified were part of

several conserved pathways that are critical to normal cellular physiology. These

findings imply that low level changes occur in cancer altering multiple processes

involved in primary cellular function. Finally, the third tier of my analysis

provided evidence for a putative mechanism that is either driven by or causes these low level changes. My identification of the AhR pathway is suggestive of a

highly conserved role for this receptor, justifying further in vivo and in vitro

experimentation in order to elucidate the exact role of this receptor and how it

affects cellular physiology with respect to cancer and tryptophan/serotonin

metabolism.

7.1 Conclusions and Future Directions for Tier I: Hypothesis Testing of the

Tryptophan/Serotonin Metabolic Pathway

There are already previously published reports showing that PET scans

can detect decreased MAO-A levels in high cancer risk patients.[229] My

research shows that whole body PET scans might be a non-invasive way to

68 identify MAO-A “cold spots”, where localized downregulation indicates increased risk of cancer at that site. This type of a marker would permit more accurate diagnosis, leading to earlier treatment, and improved outcomes. Clinical studies are needed to determine whether changes in MAO-A can be used as a prognostic indicator of cancer risk in patients with a precancerous state.

Although the role of MAO-A in cancer has yet to be fully understood, examination of my results suggests its expression may act as a marker for the development of cancer. The presence of reduced expression of MAO-A in precancerous states implies its levels indicate progression towards cancer, suggesting that MAO-A levels can be used to identify individuals that should receive increased surveillance and testing for the potential onset of cancer.

Interestingly, previously published reports show that smokers exhibit decreased MAO-A in lung tissue [229] and MAO-A inhibitors have been identified in tobacco [230]. If decreased expression of MAO-A is demonstrated in clinical trials to be a risk factor for development of cancer, it may be particularly important for individuals with low levels of MAO-A to be advised against smoking. Future studies should investigate links between smoking,

MAO-A suppression, and the development of cancer to determine whether MAO-

A suppression might be a mechanism by which smoking contributes to increased cancer risk.

7.2 Conclusions and Future Directions for Tier II: Whole Genome Analysis

It is important to determine a common molecular phenotype among all

cancers, such as changes in gene expression, which can then correlate to the mechanisms responsible for the hallmarks of cancer. Once these mechanisms are identified, they will provide insight to the process of cancer initiation and promotion. These mechanisms can then be exploited to create broad spectrum drug targets for cancer prevention/therapeutics.

My finding that among a variety of different cancers the most overrepresented pathway following an analysis of the whole genome was the AhR signaling pathway has potential to be one of these broad spectrum targets. The

AhR pathway has been linked to carcinogenesis in multiple ways. The role that it plays in cancer seems paradoxical since exposure to the classic AhR agonist tetrachlorodibenzodioxin (TCDD) is protective for breast cancer and a risk factor for liver cancer. [1, 2] The AhR pathway has been most studied in relation to xenobiotic metabolism since TCDD is pervasive environmental toxicant whose effects are linked to many alterations in systems such as the reproductive, neuronal, and immune system. [30, 231, 232] It is believed that TCDD binds to the AhR and causes a conformational change allowing it to dimerize with

ARNT/HIF1α and Hsp90. [233] The AhR-ARNT-hsp90 complex is then

translocated to the nucleus where it drives transcription of genes with HRE/XRE

promoters.

The less well known AhR activation mechanism is through hypoxia.

Under hypoxic conditions a similar response is seen to that of TCDD

administration. [234] Under normoxic conditions HIF-1α/ARNT is canonically expressed and rapidly degraded. When a cell becomes hypoxic HIF-1α/ARNT is stabilized resulting in the translocation of AhR into the neucleus and transcription of the HRE/XRE battery of genes. [233] This hypoxic response is thought to be ligand independent. However there have not been studies done to show that it is in fact a ligand independent process. Under hypoxic conditions tryptophan reacts with the products of gylcolisis/gluconeogenisis and forms AhR agonists such as indole-3-pyruvate. [4] It may be possible that this formation of AhR agonists in the hypoxic state is responsible for AhR mediated gene transcription in hypoxia.

Hypoxia is often associated with cancer and if AhR activation due to tryptophan condensation products is the mechanism by which AhR drives transcription then it could be potentially targeted with pharmaceuticals.

7.3 Conclusions and Future Directions for Tier III: Correlating MAO-A

Expression to Identify Differentially Expressed Pathways

Although at this point it is not clear how MAO-A downregulation is related to the downregulation of branched amino acids metabolism, identification of the glycolysis/gluconeogenisis metabolic pathway provides further support for the activation of AhR through endogenous ligands that are formed when

tryptophan or its derivatives react with the compounds produced AST activity.[4]

This phenomenon of AhR activation as a result gluconeogensis may be related to

the increased levels of lactate seen in cancers3. My findings that propanoate

metabolism, which begins with the conversion of lactate, was highly correlated

with MAO-A downregulation and was the most overrepresented metabolic

pathway in the frequency analysis support the hypothesis that AhR activation

could be related to gluconeogenesis.

It is well established that cancers have a greater proportion of mutations

than normal cells. [235] This increase in the number of mutations is thought to be

due to the increased speed by which cancerous cells divide. Unfortunately studies

on the replication rate of cancerous cells show that they do not divide faster than

non-cancerous cells, but rather unlike normal tissue in cancer there are more cells

replicating. [237] The preponderance of the evidence suggests that a normal

signaling mechanism which suppress the G2/M checkpoint, becomes aberrantly

activated allowing mutations to go uncorrected that then snowballs into the

variety of mutations seen with cancers. Whether the initial mutation that is caused by hypoxia, free radicals, or other agents the end result will be aberrant regulation of cellular function which the G2/M check point is supposed to protect against.

Suppression of the G2/M checkpoint results in an inability to recognize changes

3 The phenomenon of increased lactate concentrations in cancer cells is called the Warburg effect. [236] It is often mistaken to mean increased glycolysis, [238] but the actual finding is of increased lactate without reference to gylcolysis. [236] 72 in the DNA and allows the cell to continue through the cell division cycle. The correlation between suppression the canonical G2/M signaling cascade and MAO-

A downregulation could be used to identify an upstream factor that is responsible for both changes. If there is such a factor, then it could potentially be exploited to develop more effective therapeutics.

7.4 Conclusion

Most current cancer research is focused on tissue-specific genetic mutations. Familial inheritance (e.g., APC in colon cancer), genetic mutation

(e.g., p53), and overexpression of growth receptors (e.g., Her2-neu in breast cancer) can each lead to aberrant replication of a cell. Studies of these changes provide tremendous information about tissue-specific effects but are less informative about common changes that occur in multiple tissues. The similarity in the behavior of cancers from different organ systems and species indicates the potential for a universal change among cancers, regardless of the specific tissue or species. This work suggests that downregulation of Monoamine Oxidase A is such a change and is an important indicator for cancer.

In this manuscript, I applied three tiers of analysis at different levels: hypothesis testing on individual pathways to identify significant expression changes within each dataset, intersection of results between different datasets to find common function changes across experiments, and Pearson correlations

73 between individual genes to identify correlated genes within each dataset. Using this approach, I found that not only is MAO-A downregulation the most pervasive alteration in gene expression identified to date but also that is related to changes is metabolism and replication.

Future studies can explore my findings to potentially identify the specific changes common to every cancer that are responsible for initiation, promotion, and progression. The identification of these alterations could transform clinical oncology analogous to the way internal medicine was revolutionized after the discovery of antibiotics. Previously, infections were incurable and lead to painful and unnecessary deaths. Once antimicrobial agents were identified, infections were no longer as great of a threat and now are treated by the general practitioner.

If a common mechanism can be identified in cancer, chemotherapeutics can be created making cancer what infections are today, a simple trip to the doctor.

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APPENDIX A

TABLES

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Human Independent Samples (Case-control)

Mean Fold Percentage Control Cancer Type GSE Number GEO Description Species Difference of cancer (N) (N) in MAO-A p-value samples < lowest control

Malignant Cutaneous malignant Normal GSE3189 H Melanoma -15.0 4.9*10-21 100% melanoma (7) (45)

Small Cell Normal Lung neuroendocrine lung GSE1037 H Lung -9.7 1.1*10-10 100% tumor classification Carcinoma (19) (15)

Malignant Human Malignant Normal pleural GSE25493 Pleural H Pleura -7.4 4.6*10-10 90% mesothelioma Mesothelioma (5) (40)

Normal Basal-like Breast tumor GSE61 H Breast Tumors -5.6 1.6*10-4 100% characterization (10) (10)

Hypopharyngeal Normal Early GSE2379 cancer at various H Uvula Stage -3.4 7.7*10-4 100% stages of progression (3) (4)

Normal Basal-like Basal-like breast GSE3744 H Breast Cancer -5.7 0.002 88% cancer tumors (7) (18)

3 Lung tissue and cell lines were excluded. Only primary tumor samples and normal pleura were analyzed.

Table 1 Descriptive information on datasets extracted from the GEO database used in this study. Cases are grouped by whether control and cancer tissues came from different human patients (independent samples), the same human patients (paired samples), or animal models. Tissues are from humans (H) mice (Mus musculus, M), rats (Rattus norvegicus, R), or zebrafish (Danio rerio, Z). For each dataset, sample sizes of control and cancer tissues are provided. The fold difference in mean intensities of MAO-A describes the amount of suppression of the expression of that gene in cancerous tissue; for example, a fold difference of - 4 indicates 4 times less expression in cancer tissue than in normal tissue. P-values reflect significant fold differences in expression between cancer and control tissues using the appropriate t-test; cancer types are listed in order of decreasing significance of MAO-A expression. All datasets showed a significant change as determined by the sequential Bonferoni-Holm adjustment unless marked §. The percentage column contains the percent of individual cancer tissue samples in each data set that had lower levels of MAO-A expression than the lowest single control sample.

continued

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Table 1 continued

Paired Samples

Fold GEO Series Cancer Type Species Control (N) Cancer (N) Difference p-value % Number in MAOA

Human Squamous Cell Control Cancer GSE3268 H -2.7 0.001 100% Carcinoma of the lung (5) (5)

Normal Renal clear Clear Cell Carcinoma of GSE781 H Cell Carcinoma -2.2 0.002 100% the human kidney (7) (7)

Adjacent Pulmonary Tumor GSE2514 H Normal -1.7 0.004 80% adenocarcinoma (10) (10)

Normal Cancer GSE26854 Gastric cancer H -3.6 0.03§ 100% (6) (6)

Papillary Normal GSE3678 Papillary thyroid cancer H thyroid Cancer -1.2 0.08§ 71% (7) (7)

Papillary Normal thyroid GSE3467 Papillary thyroid cancer H -1.1 0.286§ 67% (9) Carcinoma (9)

4Paired data was extracted and unpaired samples were excluded.

Animal Models

Fold GEO Series Cancer Type Species Control (N) Cancer (N) Difference p-value % Number in MAO-A

Urethane-induced lung tumor model of Adjacent 6.6*10-10 pulmonary Tumor GSE2514 M Normal -1.7 86% adenocarcinoma (29) (10)

Normal Liver Liver GSE35195 Liver cancer model Z Tumor -2.2 1.7*10-5 100% Tissue (10) (10)

continued

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Table 1 continued

LH overexpressing virgin mice (luteinizing Wild type LH- hormone Breast overexpressing GSE3348 M -1.7 5.9*10-5 100% overexpression causes tissue Breast tissue spontaneous mammary (3) (3) tumors)

Granule Tumor Cells Patched heterozygous cell from GSE2426 model of M -1.6 6.9*10-5 100% Precursor Heterozygots medulloblastoma (4) (5)

C57/BL6 APC(Min/+) GSE422 Colon cancer M wild-type mutant -18.9 1.1*10-4 100% (6) (10)

N-methyl-N- nitrosourea-induced Normal Cancer GSE18726 R -1.7 5.9*10-5 100% breast cancer model (11) (9) (R)

Mammary Wild type Mammary tumors in Normal GSE2528 tumorigenesis in M MMTV-neu -1.8 0.001 100% Breast MMTV-neu model Model (3) (7)

5Zebrafish do not have separate MAO-A and MAO-B therefore MAO levels were analyzed.

6 Biological replicates were averaged for the analysis.

109

Gene symbol Gene name Gene symbol Gene name

HemK methyltransferase family AADAT aminoadipate aminotransferase HEMK1 member 1

HSD17B10, hydroxysteroid (17-beta- AANAT Arylalkylamine N-acetyltransferase HADH2 dehydrogenase)

ABP1 Amiloride binding protein 1 HSD17B4 hydroxysteroid

ACAT1, ACAT acetyl-Coenzyme A acetyltransferase 1 INDO, IDO indoleamine-pyrrole 2,3 dioxygenase

indoleamine-pyrrole 2,3 dioxygenase- ACAT2 acetyl-Coenzyme A acetyltransferase 2 INDOL1 like 1

Aminocarboxymuconate semialdehyde ACMSD INMT indolethylamine N-methyltransferase decarboxylase

AFMID Arylformamidase KMO Kynurenine 3-monooxygenase

ALDH1A3 Aldehyde dehydrogenase 1 family, member A KYNU kynureninase

ALDH1B1 Aldehyde dehydrogenase 1 family, member B LCMT1 Leucine carboxyl methyltransferase 1

ALDH2 Aldehyde dehydrogenase 2 family LCMT2 Leucine carboxyl methyltransferase 2

ALDH3A1, Aldehyde dehydrogenase 3 family LNX1 ligand of numb-protein X 1 ALDH3

ALDH3A2 Aldehyde dehydrogenase 3 family, member A MAOA monoamine oxidase A

ALDH7A1 Aldehyde dehydrogenase 7 family, member A MAOB monoamine oxidase B

METTL2B, ALDH9A1 Aldehyde dehydrogenase 9 family, member A methyltransferase like 2B METTL2

AOC2 amine oxidase, copper containing 2 METTL6 methyltransferase like 6

Nuclear transcription factor, X-box AOC3 amine oxidase, copper containing 3 NFX1 binding

AOX1 Aldehyde oxidase 1 OGDH Oxoglutarate

ASMT acetylserotonin O-methyltransferase OGDHL oxoglutarate dehydrogenase-like

Coactivator-associated arginine PRMT2, CARM1 Protein arginine methyltransferase methyltransferase HRMT1L1

Table 2 Genes listed in the tryptophan pathway in KEGG. Each dataset was filtered for the tryptophan

related genes and all tryptophan related genes included in the dataset were analyzed as described above.

The column labeled Gene symbol contains the abbreviated gene symbol. The column labeled gene name

contains the full gene name. continued

110

Table 2 continued

CAT Catalase PRMT3, HRMT1L3 Protein arginine methyltransferase

CYP1A1, Cytochrome P450, family 1, PRMT5 Protein arginine methyltransferase 5 CYP1 subfamily A

Cytochrome P450, family 1, CYP1A2 PRMT6, HRMT1L6 Protein arginine methyltransferase subfamily A

CYP1B1, cytochrome P450, family 1, subfamily PRMT7 Protein arginine methyltransferase 7 GLC3A B

PRMT8, HRMT1L3, DDC dopa decarboxylase Protein arginine methyltransferase HRMT1L4

enoyl Coenzyme A hydratase, short ECHS1 TDO2 Tryptophan 2,3-dioxygenase chain, 1,

EHHADH enoyl-Coenzyme A TPH1, TPRH, TPH Tryptophan hydroxylase 1

GCDH glutaryl-Coenzyme A dehydrogenase TPH2 Tryptophan hydroxylase 2

3-hydroxyanthranilate 3,4- HAAO WARS, IFI53 tryptophanyl-tRNA synthetase dioxygenase

HADH, hydroxyacyl-Coenzyme A tryptophanyl tRNA synthetase 2, WARS2 HADHSC dehydrogenase mitochondrial

hydroxyacyl-Coenzyme A Williams Beuren syndrome chromosome HADHA WBSCR22 dehydrogenase region

111

Human Independent Samples (Case-control)

GEO Dataset Control Cancer Species Description Number (N) (N) GDS1070 H Early Stage Hypopharengeal cancer 4 4 Late Stage Hypopharengeal Cancer with Local GDS1070 H 4 4 recurance Late stage Hypopharengeal Cancer with no GDS1070 H 4 11 metastasis GDS1110 H Patched model of medullabastoma 4 5 GDS1375 H Cutaneous Malignant Melonoma 18 45 GDS1742 H Germ Cell Cancer 3 23 GDS1962 H Glioblastoma Gleanson Grade 4 23 81 GDS1962 H All Brain Cancers 23 157 GDS1962 H Astrocytomas 23 26 GDS1962 H Oligodendroglioma 23 50 GDS2250 H BRCA1 mutations Breast Cancer 7 2 GDS2250 H Basal Like Breast Cancer 7 20 GDS2250 H Non-Basal like Breast Cancer 7 18 GDS2609 H Early Onset Colorectal Cancer 10 12 GDS619 H Adenocarcinoma of the Lung 19 12 GDS619 H Large Cell Carcinoma of the Lung 19 12 Large Cell Neuroendocrine Carcinoma of the GDS619 H 19 8 Lung GDS619 H Small Cell Lung Carcinoma 19 15 GDS619 H Typical Carcinoid Tumor of the Lung 19 12 GDS73 H Diffuse Large B-cell Lymphoma 3 21 GDS84 H Basal Like Breast Cancer 14 10 GDS84 H Luminal like Breast Cancer 14 32 Late Stage Hypopharengeal Cancer with GDS1070 H 4 15 metastasis GDS1209 H Sarcoma 15 39 GDS 1220 H Malignant pleural mesothelioma 10 44 Table 3 Descriptive information on datasets extracted from the GEO database Tissues are from humans

(H). For each dataset, sample sizes of control and cancer tissues are provided.

112

Paired Samples

GEO Dataset Control Cancer Species Description Number (N) (N) GDS1210 H Gastric Cancer 6 6 GDS1312 H Squamous Cell Lung Cancer 5 5 GDS1650 H Pulmonary Adenocarcinoma 19 19 GDS1665 H Pappilary Thyroid Cancer 9 9 GDS1732 H Pappilary Thyroid Cancer 7 7 GDS2635 H Ductal Breast Cancer 5 5 GDS2635 H Lobular Breast Cancer 5 5 GDS505 H Renal Clear Cell Carcinoma 8 8 Table 4 Descriptive information on datasets extracted from the GEO database used in this study. Control and cancer tissues came from the same human (H) patients (paired samples). For each dataset, sample sizes of control and cancer tissues are provided.

113

Animal Models

GEO Dataset Control Cancer Number Species Description (N) (N) Urethane Model of Pulmonary GDS1649 M Adenocarcinoma 15 29 GDS389 M APC induced Adenoma of the Colon 6 5 GDS389 M APC induced carcinoma of the Colon 6 5 GDS1222 M MMTV 3 7 GDS1272 M LH Overexpressing 3 3 GDS1363 R N-Metyl-Nitros-Urea induced Breast Cancer 11 24 GDS2220 Z Liver Cancer Model 10 10 Table 5 Descriptive information on datasets extracted from the GEO database used in this study. Tissues are from mice (Mus musculus, M), rats (Rattus norvegicus, R), or zebrafish (Danio rerio, Z). For each

dataset, sample sizes of control and cancer tissues are provided.

114

Frequency of Gene Gene Symbols Occurrence Count 16 2 TCF3, MAOA 15 2 COL1A1, BMP1 14 4 RUNX1, MMP14, MCM3, EZH2 13 29 VIL2, TXNIP, TOP2A, TNPO1, TCF4, TACSTD2, STAT1, S100A11, RAB2, PSMB4, PLAUR, P4HA1, NR3C1, NME1, NDRG2, MME, MKI67, MCM6, MAD2L1, ID4, FGFR2, ERBB4, DUSP1, COL1A2, CBX3, C100RF116, BUB1, ANK3, ABLIM1 12 77 WWP2, WDR1, USP14,UCK2, TTK, TRPC1, TRIM29, TRIM14, TGIF, STX6, SSPN, SPP1, SP100, SOX4, SOX15, SORBS1, SMARCA3, SAP18, RRM2, PTRF, PSMB2, PPARG, PLEKHC1, PCNA, NTRK2, NEBL, MYBL2, MSR1, MEF2C, MCM7, KPNA2, KIF14, ITPR2, ITPR1, INPP5A, IFI30, ID1, HYAL1, HS2ST1, H2AFY, H2AFX, GRB10, GPD1L, FOXM1, FOLR1, FHL1, FGFR4, FEZ2, FEN1, FAS, FARP1, FADS1, ESR1, EPAS1, EIF3S9, EDNRB, DSCR1L1, DNMT1, CXCL12, CTNND1, COX5A, COPS8, COL3A1, COL10A1, CKS2, CITED2, CHD4, CDC27, CDC25C, CCT3, CAT, CASP2, CALM1, ARPC1B, AOX1, AHNAK, ADRB2 Table 6 The genes with frequency of occurrences more than 11 out of 19 are listed here. The frequency of how many times the gene appeared, the number of genes that occurred at frequency and the gene symbols are provided. In total 114 genes are listed.

115

Frequency of Gene Gene Symbols Occurrence Count 36 1 MAOA 34 1 COL1A2 33 5 TCF4,SPARC,ITPR2,FN1,FHL1 32 1 ABLIM1 31 5 STAT1, EPAS1, CXCL12, COL3A1, CLEC3B 30 5 TOP2A, TIMP2, SPP1, COL6A1, CKS2 RGS5, RBP4, LAPTM4B, KCNMA1, HSPD1, GPX3,FLT1, FGFR2, 29 13 CFLAR, CFD, CD47, CAST, ACOX1 UCK2, TCF3, STMN1, SSPN, SNURF, SNRK, S100A10, RFC5, PRDX2, PCNA, NTRK2, MYH11, MYBL2, MKI67, MGLL, 28 38 MEF2C, MCM7, MCM6, LAGE3, KRAS, ITPR1, ITGA6, IL6ST, HSP90B1, FLI1, FEN1, COX6C, COL1A1, CNN1, CHPT1, CD302, CAV1, CAT, CALM1, BUB1, BMP1, AQP1,ANXA1 WWP2, WWC1, TPM1, TMEM43, TGFB2, TFPI, TEK, SULT1A1, SORBS2, SLC1A1, SELENBP1, SDC1, RRM2, RRAGD, PTRF, PRKCA, PPAP2B, PECAM1, PDLIM3, PDCD4, NUCB2, NR3C, MMP14, MME, MFAP4, MAL, LIMCH1, LAMA2, IVD, ITGB4, 27 56 IRS2, ID1, HLF, GSTM5, FUBP1, FOLR1, EZH2, ETS2, ERBB3, ENG, DUSP6, DPT, DIO2, CXCL2, CX3CR1, COL5A2, CDKN1C, CDC25C, CCT5, CCND2, CCNB2, CBX3, AYTL2, AURKB, ASAH1,ADARB1 ZEB1, YME1L1, WSB1, WDR1, VIL2, UBE2D2, UBE2C, TYMS, TXNIP, TPD52,TNXB, TNPO1, TK1, TGFA, TCF7L2, TACSTD2, SLPI, SLC39A6, SERBP1, SASH1, RUNX1, RFC4, RAB31, PTPRM, PTN, PTGS2, PTEN, PPP2R1B, PPL, PLEKHC1, PDCD6IP,NEBL, MTDH, MSR1, MSN, MMP9, METTL7A, MBP, 26 80 MAD2L1, LPL, LMNA, LDHA, LDB2, LAMC1, LAMA3, KIT, JAG2, ITSN1, ITGB1, INHBB, ILF3, HSPA4, HMGB3, H2AFY, H2AFV, GPD1L, GNAS, FOXN3, FARP1, FABP4, ENPP2, EMP1, EDNRB, DOCK9, DBI, CSNK1A1, COX6A1, COL11A1, CDKN2A, CD81, CD36, CCNA2, CALU, BYSL, AURKA, ATP1A1, ARPC1B, ALDH1A1, ACTA2, ABCA8 ZBTB20, UBTF, UBE2G1, UBAP2L, TUBB, TSTA3, TSPAN7, TRPC1, TPI1, TNFRSF25, TMED10, TLOC1, TGOLN2, TGFBR3, TFDP1, SYNGR1, SYNCRIP, SPTBN1, SORBS1, SLC4A4, SH3GLB1 ,SERPINH1, RUVBL1, RUNX1T1, ROD1, RNASE4, RIF1, RDX, RBM39, PTGER3, PTGDS, PSMD1, PRDX4, PPIB, PGK1, PDXK, PDLIM5, PAFAH1B1, PABPC1, NEDD9, MYL9, MTHFD2 ,MMP11, MFNG, MCM4, MCM3, MAPRE3, MAPK14, 25 98 LMO2, KLF4, KIF2C, KDELR3, IQGAP1, IFI30, ID3, HPGD, HMMR, H3F3A, GPM6B, GNA11, GLUD1, GAS1, FKBP1A, FHL2, FBXO9, FADS1, ETV1, ETFB, ESR1, EMCN, DMD, CYCS, CTSB, CP, COX5B, CLDN5, CLCN3, CHD4, CENPF, CDK4, CDH1, CDC25B, CD9, CD34, C5ORF13, BLM, BCAP29, ATP5C1, AQP4, AOX1, ANXA2, ANXA11, ALDH7A1, AKAP12, AHNAK, ADA, ACSL1, SEPT11 Table 7 The genes with frequency of occurrences more than 22 out of 40 are listed here. The frequency of how many times the gene appeared, the number of genes that occurred at frequency and the gene symbols are provided. In total 628 genes are listed. continued 116

Table 7 continued

Frequency of Gene Gene Symbols Occurrence Count WWTR1, WNK1, VWF, VDR, UBFD1, UBE2D3, TSPAN4, TRAK1, TNC, TIMP3, TIMM17A, THBS1, TACC1, SUMO1, STRAP, STK24, STAT5B, SSX2IP, SRPX, SPINT1, SPARCL1, SON, SMARCA2, SLC1A4, SEPP1, SDHC, S100A11, RPN2, ROCK2, RHOA, RABGAP1L, PTPRE, PTPRB, PTPN21, PSMD11, PSMB8, PSCD3, PRSS23, PRKAR1A, PRIM2 ,PLK2, PLK1, PKMYT1, PIGF, PHTF2, PHB, PDPN, PDE4DIP, PCSK5, PCF11, OAT, NUTF2, NOL5A, NNT, NMT1, NFIC, NFE2L2, NF1, NDN, NCOA1, NCAM1, MYLK, 24 120 MPDZ, MGST2, MAF, KRT17, KLC1, KIFC1, KIAA0101, KCNAB1, IQGAP2, IK, IGF1, ID4, HADH, H2AFX, GPD1, GNG11, GART, GALNT1, GABARAPL1, FRZB, FMO5, FBLN1, ENPEP, EIF5, EIF4EBP2, EIF4EBP1, EGFR, DKK3, DARC, DAB2, CYP4B1, CXCL14, CUGBP2, CTNND1, CTNNB1, CSE1L, CRABP2, COX7C, COPS8, COL6A2, COL4A2, COL10A1, CLIC4, CEP68, CDC2, CAV2, CALD1, CAD, C10ORF116, BTD, B2M, ATP1B3, APPBP2, ANK3, ACADSB, ABHD5, ABCB1, AAK1 YY1, VEGFA, VAMP4, USP8, USP14, UCHL1, UBL3, UBE2I, TXNDC13, TTK, TSC22D1, TRIO, TRAM1, TPR, TPM2, TOX4, TOM1L1, TNPO2, TNK2, TMPRSS2, TMPO, TLE4, THRA, TGM2, TGFBR2, TFF3, TEAD4, TARDBP, TAF9, SYT1, SVEP1, SULF1, SUB1, STX7, ST8SIA4, SSR1, SSBP, SPON1, SOX4, SNX3, SNCA, SMPDL3A, SMC2, SLC9A3R1, SLC20A1, SLC16A1, SLC11A2, SKAP2, SHMT2, SET, SEC61G, SDC2, SCP2, SCARB2, RSAD2, RPS8, RPS19, RPL37, RGS3, REEP5, RECK, RAD1, RABEP1, RAB5A, PVRL3, PTTG1, PTPRF, PTPRC, PTPRA, PTK2, PTHLH, PSMC4, PSMB5, PSMB2, PPP2R5C, PPP1R12B, PPARG, PNO1, PMS2L1, PML, PLK4, PLAUR, PHF17, PEBP1, PDLIM4, PDGFRA, P4HB, P4HA1 ,NRP2, NR3C1, NR2F1, NNMT, NID1, NFKBIA, NFATC3, NFASC, NEK2, NEDD4L, NASP, NAP1L1, MYO1B, 23 208 MYH10, MMP7, MAPT, MAPK8, MAPK13, MAP4K4, LTBP4, LRP1, LPHN2, LIMS1, LIG3, LIFR, LAMP2, LAMA4, KCNJ8, KCNJ15, JTV1, JMJD1C, JAK1, ITPK1, ISG15, INPP5D, INPP4A, INADL, IL1B, IGFBP6, IGFBP3, IDS, HSDL2, HPRT1, HPCAL1, HNRPA3, HMGB2, HLTF, GSN, GRB10, GOSR2, GNB5, GGH, GBP1, GAS7, GAS6, FUT1, FUS, FRMD4B, FOXA1, FKBP4, FIGF, FEZ2, FDFT1, ERBB2, EPHB2, EMP2, ELOVL6, EDG2, DYRK2, DDR2, DDIT4, DDEF1, CTBP1, CRTAP, CRIP1, CREM, CPD, COBL, CNTN1, CLU, CLIC1, CLASP2, CKS1B, CDK6, CDC45L, CD93, CD44, CCT3, CCND1, CCDC86, CASP1, CAND1, CAMSAP1, C9ORF61, BTG3, BRD2, BMP2, BIN1, BAZ1B, ATP5D, ATP2A2, ASF1A, ARFGEF1, ARF3, APOE, APLP2, AP1G1, ANP32E, AMOTL2, ALDH6A1, ALAD, AKAP13, ADH5, ADD3, ACTL6A, ACTB, ACLY

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Functional Group 1 Median: 0.5061965488424176 Geo: 1.0938404191170493E-7 COLLAGEN, TYPE III, ALPHA 1 (EHLERS-DANLOS SYNDROME TYPE IV, AUTOSOMAL COL3A1 DOMINANT) COL1A1 COLLAGEN, TYPE I, ALPHA 1 COL10A1 COLLAGEN, TYPE X, ALPHA 1(SCHMID METAPHYSEAL CHONDRODYSPLASIA) COL4A2 COLLAGEN, TYPE IV, ALPHA 2 COL11A1 COLLAGEN, TYPE XI, ALPHA 1 COL6A1 COLLAGEN, TYPE VI, ALPHA 1 COL1A2 COLLAGEN, TYPE I, ALPHA 2 COL6A2 COLLAGEN, TYPE VI, ALPHA 2 COL5A2 COLLAGEN, TYPE V, ALPHA 2 Functional Group 2 Median: 0.5 Geo: 4.7034565326804557E-7 V-ERB-B2 ERYTHROBLASTIC LEUKEMIA VIRAL ONCOGENE HOMOLOG 2, ERBB2 NEURO/GLIOBLASTOMA DERIVED ONCOGENE HOMOLOG (AVIAN) CDK4 CYCLIN-DEPENDENT KINASE 4 KIT V-KIT HARDY-ZUCKERMAN 4 FELINE SARCOMA VIRAL ONCOGENE HOMOLOG FIBROBLAST GROWTH FACTOR RECEPTOR 2 (BACTERIA-EXPRESSED KINASE, FGFR2 KERATINOCYTE GROWTH FACTOR RECEPTOR, CRANIOFACIAL DYSOSTOSIS 1, CROUZON SYNDROME, PFEIFFER SYNDROME, JACKSON-WEISS SYNDROME) TGFBR2 TRANSFORMING GROWTH FACTOR, BETA RECEPTOR II (70/80KDA) PDGFRA PLATELET-DERIVED GROWTH FACTOR RECEPTOR, ALPHA POLYPEPTIDE FMS-RELATED TYROSINE KINASE 1 (VASCULAR ENDOTHELIAL GROWTH FLT1 FACTOR/VASCULAR PERMEABILITY FACTOR RECEPTOR) TRIO TRIPLE FUNCTIONAL DOMAIN (PTPRF INTERACTING) NTRK2 NEUROTROPHIC TYROSINE KINASE, RECEPTOR, TYPE 2 JAK1 JANUS KINASE 1 (A PROTEIN TYROSINE KINASE) AURKB AURORA KINASE B ROCK2 RHO-ASSOCIATED, COILED-COIL CONTAINING PROTEIN KINASE 2 MAPK13 MITOGEN-ACTIVATED PROTEIN KINASE 13 MAPK14 MITOGEN-ACTIVATED PROTEIN KINASE 14 BUB1 BUB1 BUDDING UNINHIBITED BY BENZIMIDAZOLES 1 HOMOLOG (YEAST) TNK2 TYROSINE KINASE, NON-RECEPTOR, 2 CDC2 CELL DIVISION CYCLE 2, G1 TO S AND G2 TO M SNRK SNF RELATED KINASE EPHB2 EPH RECEPTOR B2 DYRK2 DUAL-SPECIFICITY TYROSINE-(Y)-PHOSPHORYLATION REGULATED KINASE 2 PLK2 POLO-LIKE KINASE 2 (DROSOPHILA) DDR2 DISCOIDIN DOMAIN RECEPTOR FAMILY, MEMBER 2 TTK TTK PROTEIN KINASE ERBB3 V-ERB-B2 ERYTHROBLASTIC LEUKEMIA VIRAL ONCOGENE HOMOLOG 3 (AVIAN) Table 8 The DAVID output of gene function clustering of the genes with frequency of occurrences more

than 22 out of 40. DAVID identified 10 functional groups among the genes. continued

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Table 8 continued

CSNK1A1 CASEIN KINASE 1, ALPHA 1 PLK1 POLO-LIKE KINASE 1 (DROSOPHILA) TEK TYROSINE KINASE, ENDOTHELIAL (VENOUS MALFORMATIONS, MULTIPLE TEK CUTANEOUS AND MUCOSAL) AURKA AURORA KINASE A PKMYT1 PROTEIN KINASE, MEMBRANE ASSOCIATED TYROSINE/THREONINE 1 NEK2 NIMA (NEVER IN MITOSIS GENE A)-RELATED KINASE 2 STK24 SERINE/THREONINE KINASE 24 (STE20 HOMOLOG, YEAST) WNK1 KINASE DEFICIENT PROTEIN MAP4K4 MITOGEN-ACTIVATED PROTEIN KINASE KINASE KINASE KINASE 4 PLK4 POLO-LIKE KINASE 4 (DROSOPHILA) AAK1 AP2 ASSOCIATED KINASE 1 Functional Group 3 Median: 0.5 Geo: 5.3414748243749445E-5 EPAS1 ENDOTHELIAL PAS DOMAIN PROTEIN 1 NFE2L2 NUCLEAR FACTOR (ERYTHROID-DERIVED 2)-LIKE 2 TFDP1 TRANSCRIPTION FACTOR DP-1 FOXA1 FORKHEAD BOX A1 MADS BOX TRANSCRIPTION ENHANCER FACTOR 2, POLYPEPTIDE C (MYOCYTE MEF2C ENHANCER FACTOR 2C) TRANSCRIPTION FACTOR 3 (E2A IMMUNOGLOBULIN ENHANCER BINDING FACTORS TCF3 E12/E47) WWTR1 WW DOMAIN CONTAINING TRANSCRIPTION REGULATOR 1 MAF V-MAF MUSCULOAPONEUROTIC FIBROSARCOMA ONCOGENE HOMOLOG (AVIAN) TEAD4 TEA DOMAIN FAMILY MEMBER 4 TCF4 TRANSCRIPTION FACTOR 4 Functional Group 4 Median: 0.5013660428682438 Geo: 1.315982811780418E-4 RFC4 REPLICATION FACTOR C (ACTIVATOR 1) 4, 37KDA MCM4 MCM4 MINICHROMOSOME MAINTENANCE DEFICIENT 4 (S. CEREVISIAE) MCM7 MCM7 MINICHROMOSOME MAINTENANCE DEFICIENT 7 (S. CEREVISIAE) MCM3 MCM3 MINICHROMOSOME MAINTENANCE DEFICIENT 3 (S. CEREVISIAE) MCM6 MINICHROMOSOME MAINTENANCE DEFICIENT 6 (MIS5 HOMOLOG, S. POMBE) (S. MCM6 CEREVISIAE) RFC5 REPLICATION FACTOR C (ACTIVATOR 1) 5, 36.5KDA Functional Group 5 Median: 0.5 Geo: 2.2839022397475606E-4 PTPRF PROTEIN TYROSINE PHOSPHATASE, RECEPTOR TYPE, F PTPRB PROTEIN TYROSINE PHOSPHATASE, RECEPTOR TYPE, B PTPRM PROTEIN TYROSINE PHOSPHATASE, RECEPTOR TYPE, M PTPRA PROTEIN TYROSINE PHOSPHATASE, RECEPTOR TYPE, A PTPRE PROTEIN TYROSINE PHOSPHATASE, RECEPTOR TYPE, E continued

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Table 8 continued

Functional Group 6 Median: 0.5 Geo: 3.952288915328324E-4 SEC61G SEC61 GAMMA SUBUNIT GOSR2 GOLGI SNAP RECEPTOR COMPLEX MEMBER 2 SSR1 SIGNAL SEQUENCE RECEPTOR, ALPHA (TRANSLOCON-ASSOCIATED PROTEIN ALPHA) TLOC1 TRANSLOCATION PROTEIN 1 TRAM1 TRANSLOCATION ASSOCIATED MEMBRANE PROTEIN 1 KDEL (LYS-ASP-GLU-LEU) ENDOPLASMIC RETICULUM PROTEIN RETENTION RECEPTOR KDELR3 3 BCAP29 B-CELL RECEPTOR-ASSOCIATED PROTEIN 29 Functional Group 7 Median: 1.0 Geo: 4.0776932496781805E-4 FUBP1 FAR UPSTREAM ELEMENT (FUSE) BINDING PROTEIN 1 ETV1 ETS VARIANT GENE 1 HLF HEPATIC LEUKEMIA FACTOR SUB1 SUB1 HOMOLOG (S. CEREVISIAE) ETS2 V-ETS ERYTHROBLASTOSIS VIRUS E26 ONCOGENE HOMOLOG 2 (AVIAN) NFIC NUCLEAR FACTOR I/C (CCAAT-BINDING TRANSCRIPTION FACTOR) FLI1 FRIEND LEUKEMIA VIRUS INTEGRATION 1 TSC22D1 TSC22 DOMAIN FAMILY, MEMBER 1 UBTF UPSTREAM BINDING TRANSCRIPTION FACTOR, RNA POLYMERASE I SOX4 SRY (SEX DETERMINING REGION Y)-BOX 4 Functional Group 8 Median: 0.0035361858129427147 Geo: 9.631921325382052E-4 ST8SIA4 ST8 ALPHA-N-ACETYL-NEURAMINIDE ALPHA-2,8-SIALYLTRANSFERASE 4 TSPAN7 TETRASPANIN 7 FUCOSYLTRANSFERASE 1 (GALACTOSIDE 2-ALPHA-L-FUCOSYLTRANSFERASE, H BLOOD FUT1 GROUP) UDP-N-ACETYL-ALPHA-D-GALACTOSAMINE:POLYPEPTIDE N- GALNT1 ACETYLGALACTOSAMINYLTRANSFERASE 1 (GALNAC-T1) RPN2 RIBOPHORIN II Functional Group 9 Median: 7.306926086578781E-5 Geo: 0.0010849084520049457 ESR1 ESTROGEN RECEPTOR 1 NR2F1 NUCLEAR RECEPTOR SUBFAMILY 2, GROUP F, MEMBER 1 PPARG PEROXISOME PROLIFERATIVE ACTIVATED RECEPTOR, GAMMA NUCLEAR RECEPTOR SUBFAMILY 3, GROUP C, MEMBER 1 (GLUCOCORTICOID NR3C1 RECEPTOR) NR3C2 NUCLEAR RECEPTOR SUBFAMILY 3, GROUP C, MEMBER 2 VDR VITAMIN D (1,25- DIHYDROXYVITAMIN D3) RECEPTOR THYROID HORMONE RECEPTOR, ALPHA (ERYTHROBLASTIC LEUKEMIA VIRAL (V-ERB- THRA A) ONCOGENE HOMOLOG, AVIAN) continued

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Table 8 continued

Functional Group 10 Median: 0.26203885161655127 Geo: 0.01946040827801024 UBE2I UBIQUITIN-CONJUGATING ENZYME E2I (UBC9 HOMOLOG, YEAST) UBE2G1 UBIQUITIN-CONJUGATING ENZYME E2G 1 (UBC7 HOMOLOG, YEAST) UBE2C UBIQUITIN-CONJUGATING ENZYME E2C UBE2D2 UBIQUITIN-CONJUGATING ENZYME E2D 2 (UBC4/5 HOMOLOG, YEAST) UBE2D3 UBIQUITIN-CONJUGATING ENZYME E2D 3 (UBC4/5 HOMOLOG, YEAST)

121

-Log(B-H P- Pathway Molecules value) Hepatic Fibrosis / Hepatic Stellate Cell Hepatic Fibrosis / Hepatic Stellate Cell 6.09E+00 Activation Activation

Aryl Hydrocarbon Receptor Signaling 3.73E+00 Aryl Hydrocarbon Receptor Signaling

Leukocyte Extravasation Signaling 3.73E+00 Leukocyte Extravasation Signaling

Tight Junction Signaling 2.36E+00 Tight Junction Signaling

Glucocorticoid Receptor Signaling 2.25E+00 Glucocorticoid Receptor Signaling

Wnt/beta-catenin Signaling 2.13E+00 Wnt/beta-catenin Signaling

Table 9 The top six signaling networks from Ingenuity Pathway Analysis classification of the genes with frequency of occurrences more than 22 out of 40. The signaling networks correspond to various characteristics of cancer.

122

Frequency of Gene Count Gene Symbols Occurrence 29 4 TOP2A, STAT1, MAOA, FN1 28 2 EPAS1, COL1A2 27 8 TCF4, SPARC, SORBS2, NR3C2, ITPR2, HLF, FHL1, CKS2 WDR1, RFC5, NEBL, GPX3, GPD1L, FARP1, COL3A1, 26 10 CLEC3B, ASAH1, ABLIM1, ABCA8 UBE2C, TUBB, TNFRSF25, TIMP2, SPTBN1, SPP1, SASH1, RIF1, RFC4, PECAM1, NTRK2, MCM7, MCM6, LDB2, 25 28 KCNMA1, ITPR1, HSPD1, HSP90B1, FUBP1, CXCL12, COL6A1, CHD4, CFD, CENPF, CBX3, CAST, CALM1, C5ORF13 TYMS, TPM1, TFPI, TCF3, PKMYT1, PDLIM5, NCOA1, MYH11, MYBL2, MMP11, MKI67, METTL7A, LAGE3, 24 32 KIAA0101, HMMR, H3F3A, GPM6B, FOXN3, COX6C, CEP68, CDC25B, CDC2, CCT5, C10OR116, BLM, AURKB, AURKA, ARPC1B, ANXA1, ACOX1, ABCB1, SEPTIN 11 YME1L1, UBE2D2, TGFBR3, TGFA, TACC1, SVEP1, SSPN, SNURF, SNRK, SERPINH1, SEPP1, S100A10, RPS8, PTN, PTEN, PSMD11, PPP1R12B, PPL, PPAP2B, PMS2L1, PCNA, NUCB2, MSN, MMP14, MFAP4, MEF2C, MCM4, MAF, LAPTM4B, LAMA2, KRAS, KIT, ITGB4, INHBB, ID4, HPGD, 23 65 H2AFY, GNAS, GART, FRMD4B, FLT1, FLI1, FGFR2, FBXO9, EDNRB, CX3CR1, COL11A1, COBL, CDKN2A, CDK6, CDH1, CDC25C, CD47, CD302, CCNA2, CAV1, CALU, CALD1, C9ORF61, BUB1, BMP1, APPBP2, ANXA11, AKAP13, ACADSB ZEB1,WWC1,WSB1,VIL2,UCK2,UBL3,UBE2J1,UBE2I,TXND C13,TSTA3,TRIO,TRIM14,TPX2,TPR,TPD52,TNPO1,TLOC1,T LE4,TK1,TGFBR2,TF,TACSTD2,SYNGR1,SYNCRIP,SUMO1,S ULF1,STMN1,STK24,SRCAP,SPON1,SMARCA2,SLC1A1,SH MT2,SH3GLB1,SERBP1,SDC1,RUNX1,RRM2,RRAGD,RPL37, RGS5,RDX,RAPGEF2,RAD1,RAB40B,RAB2A,QKI,PTRF,PTH LH,PSMC4,PSMB2,PRKD3,PRDX2,PPP2R1B,PMS1,PLLP,PLE KHC1,PHTF2,PHF17,PGK1,PDZD2,PCF11,PAICS,OBSL1,NRP 2,NR3C1,NFASC,NF1,NCAM1,NASP,MYLK,MYH10,MTHFD2 22 150 ,MSTP9,MME,MICAL2,MGLL,MBP,MAPRE2,MAL,LPHN2,L OXL2,LOC92482,LMO2,LIMCH1,LIFR,LDHA,LAMC1,KIAA0 999,JMJD1C,JAG2,ITSN2,ITPK1,ITGA6,IQGAP2,IQGAP1,INP P4A,ILF3,IL8,IL6ST,HPCAL1,HNRPA3,HMGB3,H2AFX,H2AF V,FXR1,FUS,FTO,FRY,FMO2,FEN1,FAM13A1,FABP4,EZH2,E TS2,ERBB3,ERBB2,ENPP2,DYRK2,DUSP6,DST,DPT,DOCK9, DMD,DARC,CYCS,CXCL14,CTNND1,CREM,COX6A1,COX5 B,COL10A1,CNN1,CKS1B,CHRDL1,CFLAR,CDC45L,CD36,C CNB2,CAV2,CAND1,BYSL,BTD,ARHGEF6,AQP1,ALDH7A1, ALDH6A1,ADH1C,ADD3,AAK1 Table 10 The genes with significant frequency of occurrences in only human datasets are listed here. The frequency of how many times the gene appeared, the number of genes that occurred at frequency and the gene symbols are provided. In total 1,142 genes are listed. continued

123

Table 10 continued

Frequency of Gene Count Gene Symbols Occurrence ZMYND8, ZBTB20, WWP2, VCAN, UCHL1, UBE2G1, UBE2D3, UBAP2L, TXNIP, TWF1, TSPAN7, TSC22D1, TRIP13, TRIM2, TPI1, TOR1AIP1, TNXB, TNS1, TNPO2, TMCC1, THBS1, TGOLN2, TGFB2, TFDP1, TEK, TCF7L2, TBL1X, TARDBP, SULT1A1, STAU2, STAT5B, SSBP1, SRP9, SORBS1, SNRPN, SLC9A3R1, SLC1A4, SFRS5, SFRS18, SFN, SEMA6A, SEMA5A, SAP18, RUNX1T1, RSAD2, RQCD1, RPS14, RPL15, ROD1, ROCK2, RNASE4, RGS3, RECK, RBP4, RBBP4, RAPGEF3, RABGAP1L, RAB31, RAB27A, PTPRN2, PTPRM, PTPN21, PTGS2, PRPF31, PRKCA, PRIM2, PPP2R3A, PPIB, PPFIA1, PML, PLOD2, PLK4, PLEKHM1, PLAU, PJA2, PDZRN3, PDPN, PDLIM3, PDE8B, PDE4DIP, PCCA, PALM2- AKAP2, PAK2, NRG1, NFE2L2, NEK2, NEDD9, NDC80, MYO1B, MUC1, MTUS1, MTDH, MSR1, MPZL2, MMP9, 21 199 MEIS2, MCM3, MAOB, MAD2L1, M-RIP, LSM5, LRPPRC, LPL, LMNA, LAMA3, KRT17, KIF2C, KIF14, KIF13B, KDELR3, KCNJ8, KCNAB1, KAL1, JTV1, ITSN1, ITGB8, ITGB1, ISG15, IRS2, INPP5D, IL13RA1, IK, IDS, HSD17B6, HPRT1, HNRNPC, HLTF, HLA-F, HEXIM1, GSTM5, GPR161, GPR116, GNA11, GLS, GGA2, GAS7,GAS1, GABARAPL1, FUT1, FTSJ1, FOSB, FOLR1, FKBP1A, FGFR1OP, FBLN1, ESR1, EPHB2, ENG, EMP2, DPYD, DPY19L1, DIO2, DICER1, CXCL2, CTBP1, CSTA, CSNK1A1, CSE1L, CRABP2, COL1A1, CLIC5, CLASP2, CHPT1, CG018, CENPA, CDKN1C, CDK4, CDC14B, CD81, CD59, CD44, CD34, CCND2, CCDC86, CAT, CAPRIN1, CAMSAP1, CAD, C1ORF41, BIN1, BCL7A, BCL11A, AYTL2, ATP5C1, ATP2A2, ARTS-1, ARHGEF12, ANP32E, ANKRD15, ANKRD12, ALDH1A1, AKT3, AHNAK, AGGF1, ADH1B, ADARB1, ACTL6A, ACSL1, ABI2, ABHD5

124

Functional Group 1 Median: 5.244362629481108E-6 Geo: 6.436064962771025E-10 KIF13B KINESIN FAMILY MEMBER 13B KIF14 KINESIN FAMILY MEMBER 14 KIF23 KINESIN FAMILY MEMBER 23 KIFC1 KINESIN FAMILY MEMBER C1 KIF2C KINESIN FAMILY MEMBER 2C Functional Group 2 Median: 0.5 Geo: 1.6150302384489691E-9 RNPS1 RNA BINDING PROTEIN S1, SERINE-RICH DOMAIN PABPC1 POLY(A) BINDING PROTEIN, CYTOPLASMIC 2 HNRPA3 HETEROGENEOUS NUCLEAR RIBONUCLEOPROTEIN A3 HNRPH3 HETEROGENEOUS NUCLEAR RIBONUCLEOPROTEIN H3 (2H9) SFRS5 SPLICING FACTOR, ARGININE/SERINE-RICH 5 ROD1 ROD1 REGULATOR OF DIFFERENTIATION 1 (S. POMBE) PTBP1 POLYPYRIMIDINE TRACT BINDING PROTEIN 1 SART3 SQUAMOUS CELL CARCINOMA ANTIGEN RECOGNISED BY T CELLS 3 LSM5 LSM5 HOMOLOG, U6 SMALL NUCLEAR RNA ASSOCIATED (S. CEREVISIAE) SNRPN, SNURF SMALL NUCLEAR RIBONUCLEOPROTEIN POLYPEPTIDE N SNRPE SMALL NUCLEAR RIBONUCLEOPROTEIN POLYPEPTIDE E SNRPG SMALL NUCLEAR RIBONUCLEOPROTEIN POLYPEPTIDE G SNRPB SMALL NUCLEAR RIBONUCLEOPROTEIN POLYPEPTIDES B AND B1 THOC2 THO COMPLEX 2 PRPF18 PRP18 PRE-MRNA PROCESSING FACTOR 18 HOMOLOG (YEAST) PRPF31 PRP31 PRE-MRNA PROCESSING FACTOR 31 HOMOLOG (YEAST) Functional Group 3 Median: 0.04428328014589371 Geo: 3.6950573190084184E-9 HIPK2 HOMEODOMAIN INTERACTING PROTEIN KINASE 2 CCT2 CHAPERONIN CONTAINING TCP1, SUBUNIT 2 (BETA) V-ERB-B2 ERYTHROBLASTIC LEUKEMIA VIRAL ONCOGENE HOMOLOG 2, ERBB2 NEURO/GLIOBLASTOMA DERIVED ONCOGENE HOMOLOG (AVIAN) V-AKT MURINE THYMOMA VIRAL ONCOGENE HOMOLOG 3 (PROTEIN KINASE B, AKT3 GAMMA) CDK4 CYCLIN-DEPENDENT KINASE 4 KIT V-KIT HARDY-ZUCKERMAN 4 FELINE SARCOMA VIRAL ONCOGENE HOMOLOG FIBROBLAST GROWTH FACTOR RECEPTOR 2 (BACTERIA-EXPRESSED KINASE, KERATINOCYTE GROWTH FACTOR RECEPTOR, CRANIOFACIAL DYSOSTOSIS 1, FGFR2 CROUZON SYNDROME, PFEIFFER SYNDROME, JACKSON-WEISS SYNDROME) TGFBR2 TRANSFORMING GROWTH FACTOR, BETA RECEPTOR II (70/80KDA) DAPK1 DEATH-ASSOCIATED PROTEIN KINASE 1 PTK7 PTK7 PROTEIN TYROSINE KINASE 7 Table 11 The DAVID output of gene function clustering of the genes with frequency of occurrences more

than 19 out of 32. DAVID identified 23 functional groups among the genes

continued

125

Table 11 continued

FMS-RELATED TYROSINE KINASE 1 (VASCULAR ENDOTHELIAL GROWTH FLT1 FACTOR/VASCULAR PERMEABILITY FACTOR RECEPTOR) AXL AXL RECEPTOR TYROSINE KINASE MYLK MYOSIN, LIGHT POLYPEPTIDE KINASE TRIO TRIPLE FUNCTIONAL DOMAIN (PTPRF INTERACTING) NTRK2 NEUROTROPHIC TYROSINE KINASE, RECEPTOR, TYPE 2 JAK1 JANUS KINASE 1 (A PROTEIN TYROSINE KINASE) PAK2 P21 (CDKN1A)-ACTIVATED KINASE 2 PAK1 P21/CDC42/RAC1-ACTIVATED KINASE 1 (STE20 HOMOLOG, YEAST) AURKB AURORA KINASE B PMS2L1 POSTMEIOTIC SEGREGATION INCREASED 2-LIKE 1 PDK4 PYRUVATE DEHYDROGENASE KINASE, ISOZYME 4 ROCK2 RHO-ASSOCIATED, COILED-COIL CONTAINING PROTEIN KINASE 2 MAPK13 MITOGEN-ACTIVATED PROTEIN KINASE 13 MAPK14 MITOGEN-ACTIVATED PROTEIN KINASE 14 PRKD3 PROTEIN KINASE D3 CDC42BPA CDC42 BINDING PROTEIN KINASE ALPHA (DMPK-LIKE) PRKD1 PROTEIN KINASE D1 BUB1B BUB1 BUDDING UNINHIBITED BY BENZIMIDAZOLES 1 HOMOLOG BETA (YEAST) BUB1 BUB1 BUDDING UNINHIBITED BY BENZIMIDAZOLES 1 HOMOLOG (YEAST) RPS6KA2 RIBOSOMAL PROTEIN S6 KINASE, 90KDA, POLYPEPTIDE 2 CDC2 CELL DIVISION CYCLE 2, G1 TO S AND G2 TO M SNRK SNF RELATED KINASE CHEK1 CHK1 CHECKPOINT HOMOLOG (S. POMBE) EPHB2 EPH RECEPTOR B2 DYRK2 DUAL-SPECIFICITY TYROSINE-(Y)-PHOSPHORYLATION REGULATED KINASE 2 PLK2 POLO-LIKE KINASE 2 (DROSOPHILA) DDR2 DISCOIDIN DOMAIN RECEPTOR FAMILY, MEMBER 2 TTK TTK PROTEIN KINASE ERBB3 V-ERB-B2 ERYTHROBLASTIC LEUKEMIA VIRAL ONCOGENE HOMOLOG 3 (AVIAN) EPHA4 EPH RECEPTOR A4 CSNK1A1 CASEIN KINASE 1, ALPHA 1 SEPHS1 SELENOPHOSPHATE SYNTHETASE 1 PLK1 POLO-LIKE KINASE 1 (DROSOPHILA) TEK TYROSINE KINASE, ENDOTHELIAL (VENOUS MALFORMATIONS, MULTIPLE TEK CUTANEOUS AND MUCOSAL) AURKA AURORA KINASE A continued

126

Table 11 continued

PKMYT1 PROTEIN KINASE, MEMBRANE ASSOCIATED TYROSINE/THREONINE 1 STK39 SERINE THREONINE KINASE 39 (STE20/SPS1 HOMOLOG, YEAST) MARK1 MAP/MICROTUBULE AFFINITY-REGULATING KINASE 1 PBK PDZ BINDING KINASE NEK2 NIMA (NEVER IN MITOSIS GENE A)-RELATED KINASE 2 STK24 SERINE/THREONINE KINASE 24 (STE20 HOMOLOG, YEAST) SRPK2 SFRS PROTEIN KINASE 2 WNK1 KINASE DEFICIENT PROTEIN PCTK1 PCTAIRE PROTEIN KINASE 1 MAP4K4 MITOGEN-ACTIVATED PROTEIN KINASE KINASE KINASE KINASE 4 CDC2L6 CELL DIVISION CYCLE 2-LIKE 6 (CDK8-LIKE) RIOK3 RIO KINASE 3 (YEAST) PLK4 POLO-LIKE KINASE 4 (DROSOPHILA) AAK1 AP2 ASSOCIATED KINASE 1 KIAA0999 KIAA0999 PROTEIN Functional Group 4 Median: 0.6119280466848981 Geo: 8.5190081938714E-8 MLF1IP MLF1 INTERACTING PROTEIN PCGF3 POLYCOMB GROUP RING FINGER 3 AOF2 AMINE OXIDASE (FLAVIN CONTAINING) DOMAIN 2 HLF HEPATIC LEUKEMIA FACTOR TATA BOX BINDING PROTEIN (TBP)-ASSOCIATED FACTOR, RNA POLYMERASE I, A, TAF1A 48KDA SOX4 SRY (SEX DETERMINING REGION Y)-BOX 4 DR1 DOWN-REGULATOR OF TRANSCRIPTION 1, TBP-BINDING (NEGATIVE COFACTOR 2) RFX3 REGULATORY FACTOR X, 3 (INFLUENCES HLA CLASS II EXPRESSION) GABPB2 GA BINDING PROTEIN TRANSCRIPTION FACTOR, BETA SUBUNIT 1, 53KDA FLI1 FRIEND LEUKEMIA VIRUS INTEGRATION 1 TSC22D1 TSC22 DOMAIN FAMILY, MEMBER 1 ERG V-ETS ERYTHROBLASTOSIS VIRUS E26 ONCOGENE LIKE (AVIAN) CAMTA1 CALMODULIN BINDING TRANSCRIPTION ACTIVATOR 1 TXNIP THIOREDOXIN INTERACTING PROTEIN NFIC NUCLEAR FACTOR I/C (CCAAT-BINDING TRANSCRIPTION FACTOR) CBX3 CHROMOBOX HOMOLOG 3 (HP1 GAMMA HOMOLOG, DROSOPHILA) TFDP2 TRANSCRIPTION FACTOR DP-2 (E2F DIMERIZATION PARTNER 2) SAP18 SIN3A-ASSOCIATED PROTEIN, 18KDA FUBP1 FAR UPSTREAM ELEMENT (FUSE) BINDING PROTEIN 1 continued

127

Table 11 continued

ETV1 ETS VARIANT GENE 1 FRY FURRY HOMOLOG (DROSOPHILA) CBX7 CHROMOBOX HOMOLOG 7 EZH2 ENHANCER OF ZESTE HOMOLOG 2 (DROSOPHILA) KLF12 KRUPPEL-LIKE FACTOR 12 SUB1 SUB1 HOMOLOG (S. CEREVISIAE) NFIB NUCLEAR FACTOR I/B ETS2 V-ETS ERYTHROBLASTOSIS VIRUS E26 ONCOGENE HOMOLOG 2 (AVIAN) HMGB3 HIGH-MOBILITY GROUP BOX 3 NFRKB NUCLEAR FACTOR RELATED TO KAPPAB BINDING PROTEIN Functional Group 5 Median: 0.20286435090730917 Geo: 1.0047805802125355E-7 USP1 UBIQUITIN SPECIFIC PEPTIDASE 1 USP4 UBIQUITIN SPECIFIC PEPTIDASE 4 (PROTO-ONCOGENE) PROTEASOME (PROSOME, MACROPAIN) SUBUNIT, BETA TYPE, 8 (LARGE PSMB8 MULTIFUNCTIONAL PEPTIDASE 7) PSMB7 PROTEASOME (PROSOME, MACROPAIN) SUBUNIT, BETA TYPE, 7 UBIQUITIN PROTEIN LIGASE E3A (HUMAN PAPILLOMA VIRUS E6-ASSOCIATED UBE3A PROTEIN, ANGELMAN SYNDROME) PSMB2 PROTEASOME (PROSOME, MACROPAIN) SUBUNIT, BETA TYPE, 2 Functional Group 6 Median: 0.5006294147320106 Geo: 1.4986563084234836E-7 RPL31 RIBOSOMAL PROTEIN L31 RPL28 RIBOSOMAL PROTEIN L28 RPL18 RIBOSOMAL PROTEIN L18 RPL35 RIBOSOMAL PROTEIN L35 RPL11 RIBOSOMAL PROTEIN L11 RPL13 RIBOSOMAL PROTEIN L13 RPL37 RIBOSOMAL PROTEIN L37 RPS8 RIBOSOMAL PROTEIN S8 RPL10 RIBOSOMAL PROTEIN L10 RPS19 RIBOSOMAL PROTEIN S19 RPL15 RIBOSOMAL PROTEIN L15 Functional Group 7 Median: 1.0 Geo: 5.324923096838223E-7 H2AFX H2A HISTONE FAMILY, MEMBER X H2AFY H2A HISTONE FAMILY, MEMBER Y CENPA CENTROMERE PROTEIN A, 17KDA H3F3A, H3F3B H3 HISTONE, FAMILY 3A HIST1H4J H4 HISTONE, FAMILY 2 H2AFV H2A HISTONE FAMILY, MEMBER V continued 128

Table 11 continued

Functional Group 8 Median: 0.603832834644242 Geo: 5.883106337580538E-7 EPAS1 ENDOTHELIAL PAS DOMAIN PROTEIN 1 NFE2L2 NUCLEAR FACTOR (ERYTHROID-DERIVED 2)-LIKE 2 TFDP1 TRANSCRIPTION FACTOR DP-1 MADS BOX TRANSCRIPTION ENHANCER FACTOR 2, POLYPEPTIDE C (MYOCYTE MEF2C ENHANCER FACTOR 2C) TRANSCRIPTION FACTOR 3 (E2A IMMUNOGLOBULIN ENHANCER BINDING FACTORS TCF3 E12/E47) WWTR1 WW DOMAIN CONTAINING TRANSCRIPTION REGULATOR 1 MAF V-MAF MUSCULOAPONEUROTIC FIBROSARCOMA ONCOGENE HOMOLOG (AVIAN) TCF4 TRANSCRIPTION FACTOR 4 TEAD4 TEA DOMAIN FAMILY MEMBER 4 Functional Group 9 Median: 0.0552691399619904 Geo: 6.906271700788437E-7 TNPO1 TRANSPORTIN 1 KPNA1 KARYOPHERIN ALPHA 1 (IMPORTIN ALPHA 5) RANBP5 RAN BINDING PROTEIN 5 IPO9 IMPORTIN 9 CSE1L CSE1 CHROMOSOME SEGREGATION 1-LIKE (YEAST) XPO1 EXPORTIN 1 (CRM1 HOMOLOG, YEAST) TNPO2 TRANSPORTIN 2 (IMPORTIN 3, KARYOPHERIN BETA 2B) Functional Group 10 Median: 0.20780481476062512 Geo: 7.065569735839168E-7 TRIM28 TRIPARTITE MOTIF-CONTAINING 28 MBD2 METHYL-CPG BINDING DOMAIN PROTEIN 2 DR1 DOWN-REGULATOR OF TRANSCRIPTION 1, TBP-BINDING (NEGATIVE COFACTOR 2) MEIS2 MEIS1, MYELOID ECOTROPIC VIRAL INTEGRATION SITE 1 HOMOLOG 2 (MOUSE) ZMYND11 ZINC FINGER, MYND DOMAIN CONTAINING 11 KLF12 KRUPPEL-LIKE FACTOR 12 RYBP RING1 AND YY1 BINDING PROTEIN RFX3 REGULATORY FACTOR X, 3 (INFLUENCES HLA CLASS II EXPRESSION) NCOR2 NUCLEAR RECEPTOR CO-REPRESSOR 2 TATA BOX BINDING PROTEIN (TBP)-ASSOCIATED FACTOR, RNA POLYMERASE I, A, TAF1A 48KDA ID3 INHIBITOR OF DNA BINDING 3, DOMINANT NEGATIVE HELIX-LOOP-HELIX PROTEIN EGR1 EARLY GROWTH RESPONSE 1 FOSB FBJ MURINE OSTEOSARCOMA VIRAL ONCOGENE HOMOLOG B ID1 INHIBITOR OF DNA BINDING 1, DOMINANT NEGATIVE HELIX-LOOP-HELIX PROTEIN ILF3 INTERLEUKIN ENHANCER BINDING FACTOR 3, 90KDA TCF4 TRANSCRIPTION FACTOR 4 continued

129

Table 11 continued

Functional Group 11 Median: 0.5263824243062789 Geo: 1.2478173605556937E-6 COLLAGEN, TYPE III, ALPHA 1 (EHLERS-DANLOS SYNDROME TYPE IV, AUTOSOMAL COL3A1 DOMINANT) COL1A1 COLLAGEN, TYPE I, ALPHA 1 COL10A1 COLLAGEN, TYPE X, ALPHA 1(SCHMID METAPHYSEAL CHONDRODYSPLASIA) COL4A2 COLLAGEN, TYPE IV, ALPHA 2 COL11A1 COLLAGEN, TYPE XI, ALPHA 1 COL6A1 COLLAGEN, TYPE VI, ALPHA 1 COL1A2 COLLAGEN, TYPE I, ALPHA 2 COL5A2 COLLAGEN, TYPE V, ALPHA 2 COL6A2 COLLAGEN, TYPE VI, ALPHA 2 Functional Group 12 Median: 0.5 Geo: 1.3908139882672798E-6 SEC61G SEC61 GAMMA SUBUNIT SSR1 SIGNAL SEQUENCE RECEPTOR, ALPHA (TRANSLOCON-ASSOCIATED PROTEIN ALPHA) TRAM1 TRANSLOCATION ASSOCIATED MEMBRANE PROTEIN 1 TLOC1 TRANSLOCATION PROTEIN 1 KDEL (LYS-ASP-GLU-LEU) ENDOPLASMIC RETICULUM PROTEIN RETENTION KDELR3 RECEPTOR 3 BCAP29 B-CELL RECEPTOR-ASSOCIATED PROTEIN 29 Functional Group 13 Median: 0.012482289118489242 Geo: 2.637391436610185E-6 RFC4 REPLICATION FACTOR C (ACTIVATOR 1) 4, 37KDA MCM4 MCM4 MINICHROMOSOME MAINTENANCE DEFICIENT 4 (S. CEREVISIAE) MCM7 MCM7 MINICHROMOSOME MAINTENANCE DEFICIENT 7 (S. CEREVISIAE) MCM3 MCM3 MINICHROMOSOME MAINTENANCE DEFICIENT 3 (S. CEREVISIAE) MCM6 MINICHROMOSOME MAINTENANCE DEFICIENT 6 (MIS5 HOMOLOG, S. POMBE) MCM6 (S. CEREVISIAE) RAD51L1 RAD51-LIKE 1 (S. CEREVISIAE) TRIP13 THYROID HORMONE RECEPTOR INTERACTOR 13 RFC5 REPLICATION FACTOR C (ACTIVATOR 1) 5, 36.5KDA Functional Group 14 Median: 0.4988341369165909 Geo: 3.3783026336030595E-6 ETV1 ETS VARIANT GENE 1 CREM CAMP RESPONSIVE ELEMENT MODULATOR HLF HEPATIC LEUKEMIA FACTOR NFE2L2 NUCLEAR FACTOR (ERYTHROID-DERIVED 2)-LIKE 2 ATF2 ACTIVATING TRANSCRIPTION FACTOR 2 Functional Group 15 Median: 0.13328614664996918 Geo: 7.978511155755202E-6 USP14 UBIQUITIN SPECIFIC PEPTIDASE 14 (TRNA-GUANINE TRANSGLYCOSYLASE) USP1 UBIQUITIN SPECIFIC PEPTIDASE 1 continued

130

Table 11 continued

CYLD CYLINDROMATOSIS (TURBAN TUMOR SYNDROME) USP4 UBIQUITIN SPECIFIC PEPTIDASE 4 (PROTO-ONCOGENE) USP8 UBIQUITIN SPECIFIC PEPTIDASE 8 USP9X UBIQUITIN SPECIFIC PEPTIDASE 9, X-LINKED Functional Group 16 Median: 0.536154546452184 Geo: 1.0676012505563277E-5 ESR1 ESTROGEN RECEPTOR 1 NR2F1 NUCLEAR RECEPTOR SUBFAMILY 2, GROUP F, MEMBER 1 PPARG PEROXISOME PROLIFERATIVE ACTIVATED RECEPTOR, GAMMA NR2F2 NUCLEAR RECEPTOR SUBFAMILY 2, GROUP F, MEMBER 2 RARA RETINOIC ACID RECEPTOR, ALPHA NUCLEAR RECEPTOR SUBFAMILY 3, GROUP C, MEMBER 1 (GLUCOCORTICOID NR3C1 RECEPTOR) NR3C2 NUCLEAR RECEPTOR SUBFAMILY 3, GROUP C, MEMBER 2 VDR VITAMIN D (1,25- DIHYDROXYVITAMIN D3) RECEPTOR Functional Group 17 Median: 5.244362629481108E-6 Geo: 2.177602827372946E-5 PTPRF PROTEIN TYROSINE PHOSPHATASE, RECEPTOR TYPE, F PTPRS PROTEIN TYROSINE PHOSPHATASE, RECEPTOR TYPE, D PTPRB PROTEIN TYROSINE PHOSPHATASE, RECEPTOR TYPE, B PTPRM PROTEIN TYROSINE PHOSPHATASE, RECEPTOR TYPE, M PTPRE PROTEIN TYROSINE PHOSPHATASE, RECEPTOR TYPE, E PTPRA PROTEIN TYROSINE PHOSPHATASE, RECEPTOR TYPE, A PTPRN2 PROTEIN TYROSINE PHOSPHATASE, RECEPTOR TYPE, N POLYPEPTIDE 2 Functional Group 18 Median: 0.5 Geo: 5.241076659116898E-5 ST3GAL5 ST3 BETA-GALACTOSIDE ALPHA-2,3-SIALYLTRANSFERASE 5 MFNG MANIC FRINGE HOMOLOG (DROSOPHILA) ST6 (ALPHA-N-ACETYL-NEURAMINYL-2,3-BETA-GALACTOSYL-1,3)-N- ST6GALNAC2 ACETYLGALACTOSAMINIDE ALPHA-2,6-SIALYLTRANSFERASE 2 FUCOSYLTRANSFERASE 1 (GALACTOSIDE 2-ALPHA-L-FUCOSYLTRANSFERASE, H FUT1 BLOOD GROUP) UDP-N-ACETYL-ALPHA-D-GALACTOSAMINE:POLYPEPTIDE N- GALNT1 ACETYLGALACTOSAMINYLTRANSFERASE 1 (GALNAC-T1) UST URONYL-2-SULFOTRANSFERASE ST8SIA4 ST8 ALPHA-N-ACETYL-NEURAMINIDE ALPHA-2,8-SIALYLTRANSFERASE 4 RPN2 RIBOPHORIN II TSPAN7 TETRASPANIN 7 Functional Group 19 Median: 0.4192779498082234 Geo: 1.5318186014436015E-4 COX7A1 CYTOCHROME C OXIDASE SUBUNIT VIIA POLYPEPTIDE 1 (MUSCLE) COX6A1 CYTOCHROME C OXIDASE SUBUNIT VIA POLYPEPTIDE 1 continued

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Table 11 continued

MRS2L MRS2-LIKE, MAGNESIUM HOMEOSTASIS FACTOR (S. CEREVISIAE) COX5A CYTOCHROME C OXIDASE SUBUNIT VA COX6C CYTOCHROME C OXIDASE SUBUNIT VIC COX5B CYTOCHROME C OXIDASE SUBUNIT VB COX7C CYTOCHROME C OXIDASE SUBUNIT VIIC Functional Group 20 Median: 0.08778804927729682 Geo: 1.6021817332447918E-4 ARF3 ADP-RIBOSYLATION FACTOR 3 RAB5A RAB5A, MEMBER RAS ONCOGENE FAMILY RAB31 RAB31, MEMBER RAS ONCOGENE FAMILY RAB27A RAB27A, MEMBER RAS ONCOGENE FAMILY RAB1A RAB1A, MEMBER RAS ONCOGENE FAMILY Functional Group 21 Median: 0.18831273050840067 Geo: 4.648198634079436E-4 UBE4B UBIQUITINATION FACTOR E4B (UFD2 HOMOLOG, YEAST) ITCH ATROPHIN-1 INTERACTING PROTEIN 4 UBE2C UBIQUITIN-CONJUGATING ENZYME E2C UBE2J1 UBIQUITIN-CONJUGATING ENZYME E2, J1 (UBC6 HOMOLOG, YEAST) UBE2I UBIQUITIN-CONJUGATING ENZYME E2I (UBC9 HOMOLOG, YEAST) UBE2G1 UBIQUITIN-CONJUGATING ENZYME E2G 1 (UBC7 HOMOLOG, YEAST) CDC20 CDC20 CELL DIVISION CYCLE 20 HOMOLOG (S. CEREVISIAE) UBE2D2 UBIQUITIN-CONJUGATING ENZYME E2D 2 (UBC4/5 HOMOLOG, YEAST) UBIQUITIN PROTEIN LIGASE E3A (HUMAN PAPILLOMA VIRUS E6-ASSOCIATED UBE3A PROTEIN, ANGELMAN SYNDROME) UBE2D3 UBIQUITIN-CONJUGATING ENZYME E2D 3 (UBC4/5 HOMOLOG, YEAST) UBE3B UBIQUITIN PROTEIN LIGASE E3B UBE2S UBIQUITIN-CONJUGATING ENZYME E2S Functional Group 22 Median: 0.3746554602037391 Geo: 0.003257070447889412 RUNT-RELATED TRANSCRIPTION FACTOR 1; TRANSLOCATED TO, 1 (CYCLIN D- RUNX1T1 RELATED) DR1 DOWN-REGULATOR OF TRANSCRIPTION 1, TBP-BINDING (NEGATIVE COFACTOR 2) ASXL1 KIAA0978 PROTEIN ZHX3 ZINC FINGERS AND HOMEOBOXES 3 PCGF3 POLYCOMB GROUP RING FINGER 3 TRIM33 KIAA1113 PROTEIN BCL11A B-CELL CLL/LYMPHOMA 11A (ZINC FINGER PROTEIN) ZMYND11 ZINC FINGER, MYND DOMAIN CONTAINING 11 KLF12 KRUPPEL-LIKE FACTOR 12 RYBP RING1 AND YY1 BINDING PROTEIN continued

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Table 11 continued

BAZ1B WILLIAMS-BEUREN SYNDROME CHROMOSOME REGION 10 TCEA2 TRANSCRIPTION ELONGATION FACTOR A (SII), 2 PRDM2 PR DOMAIN CONTAINING 2, WITH ZNF DOMAIN TATA BOX BINDING PROTEIN (TBP)-ASSOCIATED FACTOR, RNA POLYMERASE I, A, TAF1A 48KDA MYELOID/LYMPHOID OR MIXED-LINEAGE LEUKEMIA (TRITHORAX HOMOLOG, MLLT10 DROSOPHILA); TRANSLOCATED TO, 10 EGR1 EARLY GROWTH RESPONSE 1 TSHZ2 CHROMOSOME 20 OPEN READING FRAME 17 KLF4 KRUPPEL-LIKE FACTOR 4 (GUT) KLF7 KRUPPEL-LIKE FACTOR 7 (UBIQUITOUS) MYC-ASSOCIATED ZINC FINGER PROTEIN (PURINE-BINDING TRANSCRIPTION MAZ FACTOR) WHSC1 WOLF-HIRSCHHORN SYNDROME CANDIDATE 1 ATF2 ACTIVATING TRANSCRIPTION FACTOR 2 ZBTB20 ZINC FINGER AND BTB DOMAIN CONTAINING 20 Functional Group 23 Median: 0.3694972783697392 Geo: 0.016106086340149508 GPR126 G PROTEIN-COUPLED RECEPTOR 126 PTGER3 PROSTAGLANDIN E RECEPTOR 3 (SUBTYPE EP3) CX3CR1 CHEMOKINE (C-X3-C MOTIF) RECEPTOR 1 VIPR1 VASOACTIVE INTESTINAL PEPTIDE RECEPTOR 1 FZD4 FRIZZLED HOMOLOG 4 (DROSOPHILA) GPR116 G PROTEIN-COUPLED RECEPTOR 116 DARC DUFFY BLOOD GROUP, CHEMOKINE RECEPTOR LPHN2 LATROPHILIN 2 GPR161 G PROTEIN-COUPLED RECEPTOR 161

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Pathway -Log(B-H P-value) Molecules CDKN2A,GSTP1,GSTM5,CHEK1,NR2F1,CCNA2 Aryl ,ALDH1A1,RARA,TGFB2,NFE2L2,ALDH6A1,A Hydrocarbon 1.97E+00 LDH7A1,NFIC,TFDP1,GSTM3 (includes Receptor EG:2947),CDK6,SLC35A2,CCND2,MGST2,CDK4 Signaling ,ALDH1A2,IL1B,NFIB,NCOR2,ESR1,MCM7 JAK1,YWHAH,NFATC3,PBX1,KRAS,MAPK13,S LPI,NR3C1,FCGR1A,TGFBR2,HSPA4,SHC1,HM GB1 (includes Glucocorticoid EG:3146),VIPR1,ANXA1,TGFB2,AKT3,SUMO1, Receptor 1.76E+00 STAT1,CDKN1C,STAT5B,IL8,PIK3C2B,STAT3, Signaling MED14,MAPK14,SMARCA2,FKBP4,NCOA1,IL1 B,NR3C2,NCOR2,ELK1,HLTF,ESR1,TRA2, UBE2I MYH10,IL8,FN1,EDNRB,FLT1,ACTA2 (includes Hepatic EG:59),FGFR2,MYH11,TGFBR2,COL1A2,VEGF Fibrosis / 1.76E+00 A,MYL9 (includes Hepatic Stellate EG:10398),COL1A1,TGFA,TGFB2,IL1B,FIGF,ST Cell Activation AT1,MMP9,TIMP2,COL3A1,EGFR MYH10,ABI2,FN1,ARPC1B,F2R,ACTA2 (includes EG:59),MYH11,KRAS,IQGAP1 (includes Actin EG:8826),MYLK,ROCK2,SHC1,IQGAP2,PAK1,E Cytoskeleton 1.76E+00 ZR,ITGB1,PIK3C2B,TIAM1,PAK2,ARHGEF12,IT Signaling GA2,RDX,TTN,APC,MYL9 (includes EG:10398),PIP5K1A,RHOA,ARHGEF6, PPP1R12B,GRLF1,MSN YWHAH,PPP1R3C,DUSP6,H3F3B,ETS2,KRAS,E IF4EBP1,SHC1,PAK1,STAT1,PRKCA,ITGB1,PP ERK/MAPK ARG,PIK3C2B,PAK2,PPP2R5C,ITGA2,YWHAZ, 1.76E+00 Signaling RAPGEF3,STAT3,ATF2,PPP2CB,H3F3A, PPP2R3A (includes EG:5523), ELK1,ESR1,PPP2R1B,PRKAR1A MMP7,MMP14,ACTA2 (includes EG:59),MAPK13,ROCK2,EZR,MMP11,CTNNB1, Leukocyte PRKD1,PRKCA,TIMP2,ITGB1,PIK3C2B,TIMP3, Extravasation 1.76E+00 RDX,THY1,RAPGEF3,ARHGAP5,WIPF1,MAPK Signaling 14,RHOA,CXCL12,CD44,PECAM1,GRLF1,CTTN ,MMP9,CTNND1,MSN Table 12 The top six signaling networks from Ingenuity Pathway Analysis classification of the genes with frequency of occurrences more than 18 out of 32. The signaling networks correspond to various characteristics of cancer such as extravasion/invasion, and self sufficiency in growth signals.

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Pathway -Log(P-value) Molecules Cell Cycle: G2/M DNA Damage Checkpoint 2.38E+00 TOP2A,MDM2 (includes EG:4193),SFN,ATR,CDC2 Regulation LPS/IL-1 Mediated CYP2C9,SREBF1,CYP3A7,CAT,CHST11,FABP4,S 1.82E+00 Inhibition of RXR Function LC35A2,ALDH3B1,ACSL1,MAOA,IRAK1 VEGF Signaling 1.61E+00 EIF1AX,EIF1,AKT3,EIF2S1,SFN,ACTN1 LDLR,SREBF1,AKT3,MDM2 (includes TR/RXR Activation 1.56E+00 EG:4193),PCK1,NCOR2 LXR/RXR Activation 1.50E+00 LDLR,SREBF1,NCOR2,NOS2A,MMP9 Aryl Hydrocarbon Receptor CCNE1,SLC35A2,ALDH3B1,MDM2 (includes 1.47E+00 Signaling EG:4193),NCOR2,ATR,ESR2,SMARCA4 Hypoxia Signaling in the UBE2B,MDM2 (includes 1.42E+00 Cardiovascular System EG:4193),UBE2S,UBE2D1,LDHA Table 13 The top six signaling networks from Ingenuity Pathway Analysis classification of the genes that correlated with MAO-A. The signaling networks correspond to various characteristics of cancer such as sustained angiogenesis, and insensitivity of anti-growth signals.

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APPENDIX B

FIGURES

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Figure 1. A flow chart representing the analytical technique used. First normalized genechip data is analyzed. Then the analyzed data is merged followed by a comparative analysis. Finally after the comparative analysis common features are identified that can then be further studied.

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MAO-A Expression in Independent Samples 1.400

1.200

1.000

Normal Tissue 0.800 Cancerous 0.600 Tissue

0.400

Percentage of Normal 0.200

0.000

(M) (H)

Colon Cancer (M)

Liver CancerLH OverexpressingModel (Z) (M) NMNU Breast Cancer (R) HypopharyngealBasal-like Cancer Breast (H) Cancer

Cutaneous MalignantMalignant Melanoma BreastPleural (H) TumorMesothelioma Characterization (H) (H) Patched Model of Medulloblastoma (M)

Lung Neuroendocrine Tumor Classification (H) Mammary Tumorigenesis in MMTV-neu Model

Urethane-induced Model of Pulmonary Adenocarcinoma (M) Cancer Type Figure 2. Expression of MAO-A in normal and cancer tissue samples. Tissues are from humans (H), mice (Mus musculus, M), rats (Rattus norvegicus, R), or zebrafish (Danio rerio, Z). Values are included for each dataset with independent samples in both human and animal models. Although all analyses were conducted on raw MAO-A expression levels, here we show the expression levels in both normal (white bars) and cancerous tissue (black bars) as percents of the mean expression based on normal tissue for that data set. Control and cancerous MAO-A expression levels are significantly different for all of the cancer types shown. Error bars indicate standard error of the mean.

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( , + 1) Figure 3. Cumulative Distribution Function (CDF) of Beta 2 2 for L=19 𝐿𝐿 𝐿𝐿 datasets. The intersection of the horizontal line and CDF specifies𝑁𝑁 − the frequency of

occurrence for 95% confidence of differential expression in at least 2 datasets 𝐿𝐿

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( , + 1) Figure 4. Cumulative Distribution Function (CDF) of Beta 2 2 for L=40 𝐿𝐿 𝐿𝐿 datasets. The intersection of the horizontal line and CDF specifies𝑁𝑁 − the frequency of

occurrence for 95% confidence of differential expression in at least 2 datasets 𝐿𝐿

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( , + 1) Figure 5. Cumulative Distribution Function (CDF) of Beta 2 2 for L=32 𝐿𝐿 𝐿𝐿 datasets. The intersection of the horizontal line and CDF specifies𝑁𝑁 − the frequency of

occurrence for 95% confidence of differential expression in at least 2 datasets 𝐿𝐿

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Figure 6. A histogram of the frequencies of common differentially expressed genes for the 19 datasets (Group A). The histogram shows a reversed sigmoidal shape. The sigmoidal shape for the histogram is due to similar gene expression profiles.

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Figure 7. A histogram of the frequencies for 40 datasets (Group B). The histogram shows a reversed sigmoidal shape. The sigmoidal shape for the histogram is due to similar gene expression profiles.

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Figure 8. A graph representing the significance of the various pathways for 40 datasets based on an Ingenuity Pathway Analysis

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Figure 9. The distribution of significant genes in humans. It is notable that the curve is no longer strictly sigmoidal.

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Figure 10. A graph representing the significance of the various pathways for 32 human datasets based on an Ingenuity Pathway Analysis. It is important to note that

Arylhydrocarbon signaling is highly differentially expressed

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Figure 11. A graph representing the significance of the various pathways for genes correlated with MAO-A based on an Ingenuity Pathway Analysis. It is important to note that Arylhydrocarbon signaling is highly differentially expressed along with the G2/M checkpoint.

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Figure 12 A representation of the G2/M check point. Green indicates a negative correlation. Genes that suppress G2/M checkpoint were negatively correlated (upregulated) with

MAO-A.

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Figure 13 The glycolitic/gluconeogeneic pathway generated by IPA. Notice that genes involved in the first step of gluconegenesis are negatively correlated (green) with MAO-

A downregulation

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Figure 14. An enlargement of the portion of the gluconeogeneic pathway that negatively correlated with MAO-A

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