Some Common Pathways Perturbed in Septic Shock and Cancer

bioRxiv preprint doi: https://doi.org/10.1101/635243; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Some common pathways perturbed in septic shock and cancer

Himanshu Tripathi+, Samanwoy Mukhopadhyay+, Saroj Kant Mohapatra*

National Institute of Biomedical Genomics, Kalyani, West Bengal, India

+ These authors contributed equally to this work. * [email protected]

Abstract

Sepsis and cancer are both leading causes of death, and occurrence of any one, cancer or sepsis, increases the likelihood of the other. While cancer patients are susceptible to sepsis, survivors of sepsis are also susceptible to develop certain cancers. This mutual dependence for susceptibility suggests shared biology between the two disease types. Earlier work in our laboratory had revealed cancer-related pathway to be up-regulated in Septic Shock (SS), an advanced stage of sepsis. In the present study, we performed comprehensive genome-scale analysis of published human transcriptome datasets from septic shock and 17 cancer types. We identified a total of 66 pathways perturbed in both septic shock and cancer. Based on enrichment scores of these pathways, some cancers were observed to be similar to sepsis (Sepsis Like Cancers - SLC group) but not others. SLC group consisted of malignancies of the gastrointestinal tract (liver, oesophagus, stomach, head and neck and biliary system) which are associated with infection. SLC group showed similar direction of change in gene expression as in septic shock while the malignancies of kidney, lung and uterus showed disagreement. The SLC group shared a large number of up-regulated pathways with SS, possibly representing the theme of dysregulated host response to infection. Notably, Galactose metabolism and p53 signalling pathways were significantly up-regulated in these cancer types along with septic shock group. This study highlights the complexity of the cancer transcriptome when viewed through the lens of septic shock.

Introduction

Sepsis is a potentially life-threatening complication caused by dysregulated host response to infection, often leading to organ failure and death. Estimated global burden of sepsis is more than 30 million people every year with 5 million deaths [1]. Newborns and children are more vulnerable with estimated incidence of 3 and 1.2 million, respectively [2]. Septic shock is the advanced stage of sepsis with metabolic dysregulation and uncontrolled hypotension. It is evident now from several epidemiological studies that sepsis and

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cancer are associated. Liu et al.(2018) [3] conducted an association study between sepsis and ensuing risk of cancer in elderly adult population of United States, and found that the former is significantly associated with increased risk for many cancers including chronic myeloid leukemia (CML), myelodysplastic syndrome, acute myeloid leukemia (AML), cancers of cervix, liver, lung, rectum, colon. Another association study revealed 2.5 fold increased risk of sepsis in survivors of cancer in community-dwelling adults (the risk is increased up to 10 times in hospitalized cancer patients) [4]. Co-occurrence of cancer with sepsis is associated with higher mortality than sepsis alone without cancer [5]. On the other hand, sepsis is a common cause of death in critically ill patients with cancer, with high ICU and hospital mortality [6], [7]. Analysis of data from the past two decades revealed increasing trend in the incidence of postoperative (related to major cancer surgeries) sepsis [8]. There are previous reports on molecular changes in sepsis and cancer. Bergenfelz et al. (2012) [9] reported that Wnt5a induces immunosuppressive phenotype of macrophages in sepsis and breast cancer patients. HMGB1, a key late inflammatory mediator of systemic inflammatory response syndrome associated with bacterial sepsis, is also implicated in tumorigenesis and disease progression [10]. Muscle wasting, observed in patients with cancer, severe injury and sepsis, is associated with increased expression of several genes, particularly transcription factors and nuclear cofactors, regulating different proteolytic pathways [11]. CD11b/CD18 expression in monocytes has been proposed as a biomarker of infection in cancer patients [12]. These studies focused on the gene as the functional unit of analysis. On the other hand, a gene set or pathway represents coordinated molecular activity and captures higher-order functional unit in a tissue or cell. Pathway-level analysis allows detection of a cumulative signal that is not accessible at the gene-level. We did not find any report in literature about pathway-level comparative analysis between sepsis and cancer. In the present study, we have performed unbiased analysis of SS and cancer datasets to discover shared patterns of pathway perturbation between cancer and SS.

Materials and methods

Gene expression data for 17 different human cancers were retrieved from TCGA database (https://portal.gdc.cancer.gov/) on July 5, 2018. For each tissue, TCGA project code was provided in the search field and RNA-seq data for paired samples (case-control) were downloaded. Likewise, transcriptome data of human septic shock (SS) were retrieved from Gene Expression Omnibus (GEO) database on April 10, 2019. Retrieved expression data were log2-transformed. Gene Set Enrichment Analysis (GSEA) on expression data of both cancer and SS was performed as published earlier [13]. Briefly, enrichment score (ES) for each pathway was calculated by dividing the sum of the gene-level log-fold changes with the square root of the pathway size. This provided a pathway-level score which was highly positive for up-regulated pathways and highly negative for the down-regulated pathways. Significance of the observed score (ES) was estimated in the following manner. By scrambling the case/control labels, new data were simulated and ES recalculated for each simulation. When repeated over

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many (i.e., 10000) iterations, this generated the null distribution of the simulated ES for each pathway. Fraction of the ES null distribution that was more extreme than the observed ES was estimated to be the p-value associated with the pathway [14]. Selection of the transcriptome data sets and analysis workflow leading to the final list of significant pathways are described in Figure 1. The study characteristics of the data sets are listed in Table 1. We performed network analysis using each pathway as node and shared genes between pathways as their connecting edges. Firstly, a background network was constructed by including all the pathways (KEGG [15]) with overlapping gene memberships, i.e., for a pathway to be included, it must share at least 5% of the total number of genes with another pathway. In this network, each pathway was considered a node, and the edge between two nodes suggested overlap between the two pathways. The nodes in the network were coloured according to the disease-group in which the node (pathway) was perturbed: SLC (sepsis-like cancer), CA-only (uniquely cancer) or both. All programming was done in the R programming language (R core team 2013) [16]. The code is available on request.

Results

Out of 290 KEGG pathways, 90 were significantly perturbed in SS group (p < 0.01). Hierarchical clustering on combined cancer and sepsis data sets for these 90 pathways revealed two groups of cancers: which segregated with sepsis (sepsis-like cancer, or SLC) and which did not segregate with SS (uniquely cancer, or CA) (Figure 2). Out of 17 cancer types, 11 uniquely cancer studies were grouped (CA group) under the first clade of the heatmap. The CA group comprised of BLCA, BRCA, COAD, KICH, KIRP, LUAD, LUSC, PRAD, READ, THCA and UCEC. Second clade was formed with other cancer studies (LIHC, CHOL, KIRC, HNSC, ESCA and STAD, together termed the SLC group) and 6 SS studies. Of the 90 pathways, we further selected those pathways which were significantly perturbed in at least 80% of one or both of the cancer groups (SLC and CA) leading to retention of 66 pathways (Figure 3) which were subjected to further analysis. Many of the 66 SS-associated pathways were significantly perturbed in cancer including KICH (66), CHOL and LIHC (64 each), HNSC (63), STAD (60), ESCA (57), LUSC (51), KIRC (50). While the SLC group showed more than 75% of the selected pathways to be associated with SS, many of the CA group cancers showed less association with these pathways. For example, except KICH (66) and LUSC (51), most CA group cancers had either low number of associated pathways - THCA (21), PRAD (12), READ (10) – or showed intermediate association, such as BRCA (49), UCEC (45), COAD (39), KIRP (33), LUAD (33), BLCA (30) and THCA (21). In general, the pathways were up-regulated in the SLC group (in the same direction as SS, with more intensity) and down-regulated (in a direction opposite to SS) in the CA group. Two cancer associated KEGG pathways were found to be differentially perturbed in both cancer and SS such as Pathways in cancer (Supplementary Figure S1) and Transcriptional mis-regulation in cancer (Supplementary Figure S2). Interestingly, all three down-regulated pathways (Ribosome biogenesis in eukaryotes,

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Antigen processing and presentation, and Primary immunodeficiency) in SS group were up-regulated in SLC group. In order to ascertain relative importance of each pathway, we performed network analysis. We considered each pathway as a node and its connection with the next node based on sharing of genes between them. If two pathways shared at least 5% of genes common among themselves, then they were considered connected. In this way, we generated a network of KEGG pathways with 244 nodes (each node being a KEGG pathway) and 5304 edges. The overall property of the network was assessed by looking at the degree distribution of the nodes (Figure 4). As expected of all biological networks, there are many nodes with few edges between them and some nodes with many edges (Figure 5). By colouring the three principal groups of pathways with three different colours, it is revealed that the SLC-only nodes (coloured red) are located in the core of the network, while the other nodes are more peripheral. Of the pathways detected as significant in this analysis, some stand out for their biological and clinical relevance. These are described below. Lysosome: Lysosomal pathway was found to be differentially perturbed in both SS and SLC (Supplementary Figure S3). Lysosomes are membrane-bound catabolic organelles that maintain cellular homeostasis. Marked changes in composition and function of lysosomes can be observed in disease conditions. Sepsis and cancer induced changes in lysosomes are pronounced and also share commonality between them. Muscle wasting is one of the most common phenomenon in critically ill patients with sepsis. Apart from proteasome system, lysosome pathway also plays important role in protein degradation [17] and degradation of autophagosomes generated after the onset of sepsis, has been implicated in cancer cachexia leading to muscle wasting in cancer [18]. Interestingly cancer cells are known to have increased biogenesis of lysosomes with different membrane composition, enhanced expression, activity and secretion of lysosomal enzymes [19]. Besides, Lysosomes are now considered as hub of metabolic signaling [20]. In our study, all members of SLC (except KIRC with non-significant positive ES) and SS groups have positive ES along with the UCEC, KIRP, THCA, BLCA and BRCA of the CA group (Figure 3). Ma et al. 2015 [21], reported up- regulation of lysosome pathway in SS. Upon investigation of the pathway in the network of pathways it was revealed that lysosome pathway has the second highest (after Glycolysis/Gluconeogenesis pathway: 1599.07) betweenness value of 680.2, showing its hub-like nature in the network. Leukocyte transendothelial migration: Leukocyte transendothelial migration pathway was found to be differentially perturbed in both SS and SLC (Supplementary Figure S4). Migration of leukocytes from the blood into sites of tissue injury and infection, across the vascular endothelium, is a fundamental immune response to eliminate inflammatory trigger and help in tissue repair. Leukocyte transendothelial migration pathway is significantly up-regulated in SS and SLC groups as well as THCA of CA (Figure 3). However, most of cancers of CA group including BRCA, COAD, KICH, KIRP, LUAD, LUSC, PRAD and UCEC showed significant down-regulation of this pathway. Other pathways related to infection and inflammation, i.e., Toll like receptor pathway (Supplementary Figure S5), TNF signaling pathway (Supplementary Figure S6) and RIG-I-like signalling pathway (Supplementary Figure S7), Bacterial invasion of

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Epithelial cell (Supplementary Figure S8), Helicobacter pylori infection (Supplementary Figure S9), or movement of cells, i.e., Regulation of actin cytoskeleton (Supplementary Figure S10), were also up-regulated in SLC and SS. Galactose metabolism: Galactose metabolism pathway was found to be differentially perturbed in both SS and SLC (Supplementary Figure S11). In humans, d-galactose is generated after breakage of lactose or catabolic event of glycoproteins and glycolipids by glycoside hydrolase enzymes (Alpha-galactosidase (α-GAL, also known as α-GAL A; E.C. 3.2.1.22)). Finally, d-Galactose is converted into many intermediate metabolites and may enter any of the carbohydrate metabolism pathways (e.g. Fructose and mannose metabolism, glycolysis, pentose phosphate pathway). However, any imbalance in galactose metabolism that increases levels of galactose or galactose 1-phosphate may lead to galactosemia. Galactosemia has been implicated in neonatal sepsis [22], [23]. Further, high level of d-galactose is reported in cases of sepsis [24]. Galactose metabolism pathway is also up-regulated in SLC (except STAD) and SS groups. Likewise, we found this pathway up-regulated in BLCA, KIRP, LUAD, LUSC and UCEC while down-regulated in COAD, KICH, PRAD and READ in CA group. UDP-galactose (mainly derived from d-galactose via Leloir pathway) provide galactosyl entity for the biosynthesis of glycolipids and glycoproteins that play important role in receptor- mediated signalling, cell-cell recognition and metastasis in cancer cells [25]. Bladder cancer: Bladder cancer pathway was found to be differentially perturbed in both SS and SLC (Supplementary Figure S12). Notably, bladder cancer pathway is up-regulated in all the diseases of SLC and SS groups and many diseases of CA group including BLCA, BRCA, COAD, LUAD, LUSC, READ, THCA and UCEC as well. p53 signaling pathway: p53 signaling pathway was found to be differentially perturbed in both SS and SLC (Supplementary Figure S13). Stress signals (i.e. oxidative stress, DNA damage, oncogene activation etc.) induce p53 activation leading to enhanced transcription of p53-regulated genes, resulting in DNA damage repair, cell cycle arrest, cellular senescence or apoptosis. Like bladder cancer pathway, p53 signaling pathway is also significantly up-regulated in all disease types of SLC, SS and CA groups (except KICH and PRAD). Ribosome biogenesis in eukaryotes: Ribosome biogenesis in eukaryotes was found to be differentially perturbed in both SS and SLC (Supplementary Figure S14). Ribosomes are sites of protein synthesis in the cell, and their biogenesis is vital for the cell growth and division. Interestingly, we found down-regulation of ribosome biogenesis pathway in all SS datasets, but up-regulation in CA and SLC groups including BLCA, BRCA, COAD, KIRP, LUAD, LUSC, PRAD, READ and UCEC.

Discussion

Septic shock is a lethal condition with profound genome-scale change in expression. Earlier work in our laboratory had revealed cancer-associated pathways to be associated with septic shock [13], providing motivation for comparison of the transcriptomes between SS and cancer. By comprehensive transcriptome analysis of human septic

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shock at the pathway level, we sought to capture the higher-order signal in the two clinical entities (sepsis and cancer). We identified 90 pathways that were significantly perturbed and mostly up-regulated in SS. Hierarchical clustering of perturbation signals clearly segregated the cancer studies into two groups: those perturbed in the same direction as SS, i.e., up-regulated (called SLC group) and those that were perturbed in the reverse direction (called CA group). The CA group shows general down-regulation (with lesser significance) in most of the pathways compared to SLC. On the other hand, the SLC group showed similar (if somewhat accentuated) pathway gene expression profile as SS. Five out of six of the SLC group cancers belonged to that of the gastrointestinal system (head and neck, esophagus, stomach, liver and biliary system). These cancers are often associated with infection (i.e., human papilloma virus in “head and neck”, H. pylori in stomach, hepatitis viruses in liver). This is of biological significance as SS is caused by abnormal host response to infection leading to systemic inflammation and organ failure, and inflammation is also a common theme in many cancers. The similar pathway signature between SS and SLC suggests common biological processes involving dysregulated host response to infection at different stages of disease pathogenesis. This has implications for management of different cancers which needs further investigation. In conclusion, comparative transcriptomics of septic shock and cancer has revealed strong similarity of a groups of cancers (called SLC group here) with septic shock. These malignancies include many of the gastrointestinal tract that are often associated with infection. Robust up-regulation of a large number of pathways in septic shock is accentuated in the SLC cancers, which suggests a common biological theme running through these two distinct clinical entities. This is the first attempt to view the cancer transcriptome through the lens of septic shock. It is hoped that further work shall translate this result to actionable knowledge for clinical management of both cancer and septic shock.

Author contributions

SKM conceptualized the study and contributed to Funding Acquisition, Project Admin- istration, Resources, Supervision. HT contributed to Data Curation. SM contributed to Data Curation, Software, Validation, Visualization. All authors contributed to Formal Analysis, Investigation, Methodology, Manuscript writing and approved of the final manuscript.

Acknowledgement

This work was supported by an an extramural grant (No. BT/PR16536/BID/7/652/2016, duration of 3 years, sanctioned on 30-03-2017) from the Department of Biotechnology, Government of India.

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Fig 1. Selection of data and analysis workflow.

May 10, 2019 9/27 a 0 2019 10, May certified bypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailableunder bioRxiv preprint h 0KG pathways. KEGG 90 the 2. Fig doi: irrhclcutrn fte2 tde ae nteptwysoe ausfor values scores pathway the on based studies 23 the of clustering Hierarchical https://doi.org/10.1101/635243

KICH LUSC BRCA BLCA LUAD Hierarchical clustering ofdiseases a

KIRP CC-BY-NC-ND 4.0Internationallicense ; THCA this versionpostedMay10,2019. UCEC (with90pathways) COAD PRAD READ CHOL LIHC KIRC HNSC ESCA STAD The copyrightholderforthispreprint(whichwasnot . GSE9692 GSE26378 GSE26440 GSE13904 GSE4607 GSE8121 10/27 bioRxiv preprint doi: https://doi.org/10.1101/635243; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

CA SLC SS

Glycolysis / Gluconeogenesis Pentose phosphate pathway Ovarian steroidogenesis Vitamin digestion and absorption Morphine addiction Chemical carcinogenesis Galactose metabolism Ribosome biogenesis in eukaryotes p53 signaling pathway Lysosome Leukocyte transendothelial migration Bladder cancer Glycerophospholipid metabolism MAPK signaling pathway ErbB signaling pathway Rap1 signaling pathway HIF-1 signaling pathway FoxO signaling pathway Phagosome PI3K-Akt signaling pathway AMPK signaling pathway Dorso-ventral axis formation VEGF signaling pathway Osteoclast diferentiation Focal adhesion Adherens junction Platelet activation Antigen processing and presentation Toll-like receptor signaling pathway RIG-I-like receptor signaling pathway Jak-STAT signaling pathway Fc epsilon RI signaling pathway Fc gamma R-mediated phagocytosis TNF signaling pathway Synaptic vesicle cycle Regulation of actin cytoskeleton Insulin signaling pathway GnRH signaling pathway Progesterone-mediated oocyte maturation Prolactin signaling pathway Amyotrophic lateral sclerosis (ALS) Alcoholism Bacterial invasion of epithelial cells Vibrio cholerae infection Epithelial cell signaling in Helicobacter pylori infection Salmonella infection Pertussis Legionellosis Chagas disease (American trypanosomiasis) Amoebiasis Tuberculosis Hepatitis C CA-only Hepatitis B Pathways in cancer Transcriptional misregulation in cancer Proteoglycans in cancer CA and SLC Renal cell carcinoma Pancreatic cancer Glioma SLC-only Prostate cancer Melanoma Acute myeloid leukemia Non-small cell lung cancer Central carbon metabolism in cancer Choline metabolism in cancer Primary immunodefciency LIHC KIRC KIRP KICH STAD LUAD BLCA ESCA THCA BRCA LUSC CHOL HNSC PRAD READ UCEC COAD GSE9692 GSE8121 GSE4607 GSE26378 GSE26440 GSE13904

Fig 3. Pathway heat map for 66 selected KEGG pathways where each pathway is perturbed in at least 80% of the studies in one or both of the two groups of cancers. The column-side color bars suggest SS for purple, SLC for cyan and CA for blue. The row-side color bars suggest dark gray for CA-only pathways, gray for CA and SLC, light gray for SLC-only pathways.

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Degree distribution of KEGG pathway overlap network 1.0 0.8 0.6 0.4 Cumulative Frequency Cumulative 0.2 0.0

0 20 40 60 80 100

Degree Fig 4. This is the degree distribution of the network generated by overlapping KEGG pathways. This shows that there are many nodes with very less connectivity but a few nodes with very high connectivity with other nodes.

CA−only

SLC−only

Both

Fig 5. KEGG overlap network, with color-coding of the disease-specific pathways as listed on the top left. The SLC group of cancers are seen to be occupying a more central location of the entire KEGG network. This might suggest that key biological processes are shared between SS and SLC.

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Number of samples Study Code Study Name Data source Disease Tissue Paired control Technology in the study doi: Bladder Urothelial BLCA TCGA Cancer Bladder 38 Yes RNA-seq (Illumina)

Carcinoma https://doi.org/10.1101/635243 Breast invasive BRCA TCGA Cancer Breast 220 Yes RNA-seq (Illumina) carcinoma Gallbladder, liver CHOL Cholangiocarcinoma TCGA Cancer 18 Yes RNA-seq (Illumina) and parts of biliary tract Colon and rectosigmoid COAD Colon adenocarcinoma TCGA Cancer 82 Yes RNA-seq (Illumina) junction Esophageal ESCA TCGA Cancer Esophagus 16 Yes RNA-seq (Illumina) carcinoma Base of tongue, floor of Head and Neck HNSC TCGA Cancer mouth, gum, hypo- and 86 Yes RNA-seq (Illumina) squamous cell carcinoma oro-pharynx, larynx, etc. a

Kidney CC-BY-NC-ND 4.0Internationallicense KICH TCGA Cancer Kidney 48 Yes RNA-seq (Illumina) ; Chromophobe this versionpostedMay10,2019. KIRC Kidney renal clear cell carcinoma TCGA Cancer Kidney 144 Yes RNA-seq (Illumina) Kidney renal KIRP TCGA Cancer Kidney 64 Yes RNA-seq (Illumina) papillary cell carcinoma Liver hepatocellular LIHC TCGA Cancer Liver and intrahepatic bile ducts 100 Yes RNA-seq (Illumina) carcinoma Lung LUAD TCGA Cancer Bronchus and lung 114 Yes RNA-seq (Illumina) adenocarcinoma Lung squamous LUSC TCGA Cancer Bronchus and lung 98 Yes RNA-seq (Illumina) cell carcinoma Prostate PRAD TCGA Cancer Prostate gland 104 Yes RNA-seq (Illumina) adenocarcinoma Rectum READ TCGA Cancer Rectum rectosigmoid junction 20 Yes RNA-seq (Illumina) adenocarcinoma Stomach The copyrightholderforthispreprint(whichwasnot

STAD TCGA Cancer Stomach 62 Yes RNA-seq (Illumina) . adenocarcinoma Thyroid THCA TCGA Cancer Thyroid gland 116 Yes RNA-seq (Illumina) carcinoma Uterine Corpus UCEC Endometrial TCGA Cancer Corpus uteri 46 Yes RNA-seq (Illumina) Carcinoma Microarray (Affymetrix HGU GSE4607 Septic Shock GEO Septic Shock Whole Blood 84 No 133 Plus 2.0) Microarray (Affymetrix HGU GSE8121 Septic Shock GEO Septic Shock Whole Blood 75 No 133 Plus 2.0) Microarray (Affymetrix HGU GSE9692 Septic Shock GEO Septic Shock Whole Blood 45 No 133 Plus 2.0) Microarray (Affymetrix HGU

13/27 GSE13904 Septic Shock GEO Septic Shock Whole Blood 124 No 133 Plus 2.0) Microarray (Affymetrix HGU GSE26378 Septic Shock GEO Septic Shock Whole Blood 103 No 133 Plus 2.0) Microarray (Affymetrix HGU GSE26440 Septic Shock GEO Septic Shock Whole Blood 130 No 133 Plus 2.0) Table 1. Study characteristics of the 23 datasets selected for this study. bioRxiv preprint doi: https://doi.org/10.1101/635243; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Pathways in cancer hsa05200

SLC

RASGRP1AKT3 CDK4CDK2LPAR6 SS CDKN1ARASGRP2CDK6 APC2CDKN2ACDKN1B TFGLAMC3CDKN2B GNB5GNA13CEBPA ADCY3ADCY2ADCY1 RASSF1ADCY5RALBP1 EGLN2FZD10ADCY6 ADCY8ADCY7EGLN3 RASGRP4ADCY9CHUK COL4A1CKS2CKS1B COL4A4COL4A3COL4A2 CREBBPCOL4A6COL4A5 CSF1RCRKLCRK CTBP1CSF3RCSF2RA CTNNA2CTNNA1CTBP2 DAPK3DAPK1CTNNB1 DVL1AGTR1DCC E2F1DVL3DVL2 LPAR1E2F3E2F2 EGFEDNRBEDNRA EP300ADCY4EGFR AKT1ERBB2EPAS1 MECOMETS1AKT2 FGF2FGF1F2R FGF5FGF4FGF3 FGF8FGF7FGF6 FGF11FGF10FGF9 FGF14FGF13FGF12 FGFR2FGFR3FGFR1 FOXO1LAMB4FH PLCB1FLT3LGFLT3 FOSARHGEF12FN1 DAPK2LPAR3PIK3R5 ABL1MTORCBLC APPL1RASGRP3FZD2 FGF22FGF21FGF20 GLI2GLI1STK36 GNA12GNA11GLI3 GNAI3GNAI2GNAI1 GNB1GNASGNAQ GNG3GNB3GNB2 GNG7GNG5GNG4 GNGT2GNGT1GNG11 GRB2LPAR4LAMA1 GSTP1GSK3BCTNNA3 HDAC2HDAC1MSH6 APCHIF1AHGF BIRC3BIRC2HRAS HSP90AA1BIRC5XIAP IGF1RIGF1HSP90AB1 IKBKBFASKLK3 CXCL8IL6FASLG ITGA2ARITGA6 ITGAVITGA3ITGA2B JAK1ARAFITGB1 KITJUPJUN LAMA2RHOAKRAS LAMA5LAMA4LAMA3 LAMB3LAMB2LAMB1 ARNTLAMC2LAMC1 SMAD4SMAD3SMAD2 METMDM2MAX MLH1MITFKITLG MMP9MMP2MMP1 MYCMSH3MSH2 NFKBIANFKB2NFKB1 NRASNOS2NKX3-1 WNT16LEF1NTRK1 PDGFRAPDGFBPDGFA GNG13SUFUPDGFRB PIK3CBPIK3CAPGF PIK3R1PIK3CGPIK3CD PLCB3PLCB2PIK3R2 PLCG2PLCG1PLCB4 CYCSPMLPLD1 EGLN1WNT4GNG2 PRKACAPPARGPPARD PRKCAPRKACGPRKACB MAPK1PRKCGPRKCB MAPK8GNG12MAPK3 MAP2K1MAPK10MAPK9 LPAR5PRKXMAP2K2 PTENPTCH1BAD PTGER3PTGER2PTGER1 PTK2PTGS2PTGER4 RAC2RAC1BAX RAF1RAD51RAC3 RALGDSRALBRALA RB1RARBRARA BCL2CCND1GNB4 BCL2L1RETRELA BDKRB1BCRROCK1 RXRBRXRABDKRB2 CXCL12BIDRXRG BMP2SHHHHIP BMP4SLC2A1SKP2 SOS2SOS1SMO BRCA2BRAFSPI1 STAT5ASTAT3STAT1 ELOCSTK4STAT5B TCF7L2TCF7ELOB TGFB2TGFB1TGFA TGFBR2TGFBR1TGFB3 TPRTPM3TP53 TRAF2TRAF1HSP90B1 TRAF6TRAF5TRAF3 VEGFCVEGFBVEGFA WNT2WNT1VHL WNT7AWNT6WNT5A WNT10BWNT8BWNT7B WNT9AWNT2BWNT11 PAX8ZBTB16WNT9B BIRC7FZD5CXCR4 NCOA4CCDC6FZD3 WNT5BFGF23WNT10A FZD1AXIN2AXIN1 FZD7FZD6FZD4 TCF7L1FZD9FZD8 CASP8CASP3RASSF5 PIK3R3CUL2CASP9 PTCH2RUNX1T1IKBKG FADDCBLBCBL FGF16FGF17FGF18 F2RL3CCNE1CCNA1 LPAR2CCNE2PIAS2 TRAF4ROCK2GNG8 FGF19ARNT2ARHGEF11 CDH1CDC42RBX1 LIHC KIRC STAD ESCA CHOL HNSC GSE4607 GSE8121 GSE9692 GSE13904 GSE26378 GSE26440

Supplementary Figure S1: [Pathways in cancer hsa05200] was observed to be up-regulated in both SS and SLC.

May 10, 2019 14/27 bioRxiv preprint doi: https://doi.org/10.1101/635243; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Transcriptional misregulation in cancer hsa05202

SLC

CDK9 CDKN1BCDKN1A SS CEBPACDKN2C CEBPECEBPB CSF1RCCR7 DDIT3CSF2 PTCRADDX5 ELANEDUSP6 ERGELK4 ETV4ETV1 ETV6ETV5 EYA1EWSR1 JMJD1CFCGR1A FLI1FOXO1 FLT3FLT1 FUT8FUS NUPR1SIN3A TLX3GRIA3 H3F3AGZMB HDAC2HDAC1 TLX1HHEX HOXA11HOXA10 BIRC3HPGD IGF1ID2 IGFBP3IGF1R IL3IL2RB CXCL8IL6 ITGB7ITGAM LMO2JUP SMAD1LYL1 MAXMAF MEF2CMDM2 MEN1MEIS1 MLF1MET MLLT1KMT2A MLLT3AFF1 MMP9MMP3 MYCMPO ATF1MYCN NFKB1ATM NTRK1NGFR PAX5PAX3 PBX1PAX7 KLF3PBX3 ETV7WNT16 SIX4PDGFA PLATCDK14 PMLPLAU FEVPPARG PRCCGOLPH3L PTK2BMP2K RELRARA RELABCL2A1 BCL6BCL2L1 RXRBRXRA GOLPH3RXRG SIX1NFKBIZ SPI1SP1 SSX1SPINT1 TCF3SS18 TFE3ZEB1 TSPAN7TGFBR2 TP53TMPRSS2 KDM6ATRAF1 NSD2UTY ZBTB16WT1 PAX8ZBTB17 ASPSCR1IL1R2 HMGA2NR4A3 HIST3H3TAF15 HIST1H3HHIST1H3E SUPT3HDOT1L RUNX2SLC45A3 PROM1RUNX1T1 PER2LDB1 BAIAP3CCNA1 CCNT1CCND2 CD14CCNT2 CD40CD86 ARNT2NCOR1 LIHC KIRC STAD ESCA CHOL HNSC GSE4607 GSE8121 GSE9692 GSE13904 GSE26378 GSE26440

Supplementary Figure S2: [Transcriptional misregulation in cancer hsa05202] was observed to be up-regulated in both SS and SLC.

May 10, 2019 15/27 bioRxiv preprint doi: https://doi.org/10.1101/635243; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Lysosome hsa04142

SLC

AP1M2 TCIRG1 SS NPC2 AP4B1 CTSC AP3M2 AP4S1 AP1S1 AP3S1 TPP1 CLN3 CLN5 CLTA CLTB CLTC AP1S3 HGSNAT CTNS CTSB CTSD CTSE CTSG CTSH CTSK CTSL CTSV CTSO CTSS CTSW CTSZ AP1B1 AP1G1 AGA DNASE2 ABCA2 ARSG GGA2 GGA3 AP4E1 ABCB9 ATP6V0A2 PLA2G15 ATP6V0D2 FUCA1 GAA MFSD8 GALC GALNS GGA1 SLC17A5 AP3M1 LAMP3 GLA GLB1 GM2A GNS SUMF1 GUSB HEXA HEXB HYAL1 IDS IDUA IGF2R LAMP1 LAMP2 LIPA M6PR ARSA ARSB MAN2B1 MANBA ASAH1 NAGA NAGLU NEU1 NPC1 SLC11A2 ATP6V0A4 NAGPA ATP6V1H ATP6V0C ACP2 ATP6V0B ATP6V0A1 ATP6AP1 ACP5 CTSA LAPTM4B PPT1 LGMN PSAP MCOLN1 DNASE2B SORT1 SGSH SLC11A1 SMPD1 PSAPL1 LAPTM5 GNPTAB AP3B2 CLTCL1 GNPTG AP3B1 CTSF CD164 AP1S2 AP1G2 AP1M1 AP3D1 ATP6V0D1 AP4M1 NAPSA SCARB2 LITAF ENTPD4 CD63 CD68 LAPTM4A LIHC KIRC STAD ESCA CHOL HNSC GSE4607 GSE8121 GSE9692 GSE13904 GSE26378 GSE26440

Supplementary Figure S3: [Lysosome pathway hsa04142] was observed to be up- regulated in both SS and SLC.

May 10, 2019 16/27 bioRxiv preprint doi: https://doi.org/10.1101/635243; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Leukocyte transendothelial migration hsa04670

SLC

CDH5 OCLN SS MYL12B MYL9 RAPGEF3 VAV3 MYL12A CLDN16 RAPGEF4 CLDN4 CLDN3 CLDN7 CLDN23 MAPK14 CLDN19 CTNNA1 CTNNA2 CTNNB1 CYBA CYBB PTK2B PIK3R5 CLDN14 CLDN15 NOX1 GNAI1 GNAI2 GNAI3 ARHGAP35 CTNNA3 MYLPF ICAM1 ITGA4 ITGAL ITGAM ITGB1 ITGB2 ITK RHOA ARHGAP5 RHOH CD99 AFDN MMP2 MMP9 MSN MYL2 MYL5 NCF2 NCF4 CLDN20 NOX3 F11R CLDN18 PECAM1 PIK3CA PIK3CB PIK3CD PIK3CG PIK3R1 PIK3R2 PLCG1 PLCG2 PRKCA PRKCB PRKCG MAPK11 MAPK13 PTK2 PTPN11 PXN JAM2 MYL7 RAC1 RAC2 RAP1A RAP1B ACTB ROCK1 MAPK12 CXCL12 SIPA1 THY1 ACTG1 CLDN5 TXK VASP VAV1 VAV2 VCAM1 VCL EZR CXCR4 ACTN4 RASSF5 JAM3 PIK3R3 ACTN1 ACTN2 ACTN3 CLDN10 CLDN8 CLDN6 CLDN2 CLDN1 CLDN9 ESAM MYL10 ROCK2 BCAR1 CDC42 LIHC KIRC STAD ESCA CHOL HNSC GSE4607 GSE8121 GSE9692 GSE13904 GSE26378 GSE26440

Supplementary Figure S4: [Leukocyte transendothelial migration hsa04670] was observed to be up-regulated in both SS and SLC.

May 10, 2019 17/27 bioRxiv preprint doi: https://doi.org/10.1101/635243; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Toll-like receptor signaling pathway hsa04620

SLC

AKT3 TLR6 SS TAB1 TIRAP CHUK MAP3K8 MAPK14 TICAM1 CTSK AKT1 AKT2 TAB2 FOS PIK3R5 LY96 TBK1 IFNA5 IFNA21 IFNAR1 IFNAR2 IFNB1 IKBKB IL1B IL6 CXCL8 IL12A IL12B CXCL10 IRAK1 IRF3 IRF5 IRF7 JUN LBP CXCL9 MYD88 NFKB1 NFKBIA IRAK4 TLR7 TLR8 PIK3CA PIK3CB PIK3CD PIK3CG PIK3R1 PIK3R2 TLR9 TOLLIP MAPK1 MAPK3 MAPK8 MAPK11 MAPK9 MAPK10 MAPK13 MAP2K1 MAP2K2 MAP2K3 MAP2K6 MAP2K7 RAC1 RELA MAPK12 CCL4 CCL5 CXCL11 MAP2K4 SPP1 STAT1 MAP3K7 TLR1 TLR2 TLR3 TLR4 TLR5 TNF TRAF3 TRAF6 CASP8 PIK3R3 IKBKG RIPK1 FADD CD14 CD80 CD86 CD40 IKBKE LIHC KIRC STAD ESCA CHOL HNSC GSE4607 GSE8121 GSE9692 GSE13904 GSE26378 GSE26440

Supplementary Figure S5: [Toll like receptor pathway hsa04620] was observed to be up-regulated in both SS and SLC.

May 10, 2019 18/27 bioRxiv preprint doi: https://doi.org/10.1101/635243; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

TNF signaling pathway hsa04668

SLC

AKT3 DNM1L SS TAB1 CREB3 CEBPB RIPK3 CHUK MAP3K8 CREB1 ATF2 ATF6B MAPK14 CSF1 CSF2 CREB3L4 JAG1 EDN1 PGAM5 MLKL AKT1 AKT2 TAB2 FOS PIK3R5 TAB3 CXCL1 CXCL2 CXCL3 BIRC2 BIRC3 ICAM1 FAS IKBKB IL1B IL6 IL15 CXCL10 JUN JUNB LIF LTA MAP3K5 MMP3 MMP9 MMP14 ATF4 NFKB1 NFKBIA PIK3CA PIK3CB PIK3CD PIK3CG PIK3R1 PIK3R2 MAPK1 MAPK3 MAPK8 MAPK11 MAPK9 MAPK10 MAPK13 MAP2K1 MAP2K3 MAP2K6 MAP2K7 PTGS2 RELA BCL3 MAPK12 CCL2 CCL5 CCL20 CXCL5 CX3CL1 SELE NOD2 MAP2K4 CREB3L2 MAP3K7 TNF TNFAIP3 TNFRSF1A TNFRSF1B TRAF1 TRAF2 TRAF3 TRAF5 VCAM1 VEGFC CASP3 ITCH CASP7 CASP8 CASP10 CREB3L3 PIK3R3 IKBKG TRADD RIPK1 FADD IL18R1 CFLAR RPS6KA4 MAP3K14 SOCS3 CREB3L1 RPS6KA5 BAG4 CREB5 MAGI2 LIHC KIRC STAD ESCA CHOL HNSC GSE4607 GSE8121 GSE9692 GSE13904 GSE26378 GSE26440

Supplementary Figure S6: [TNF signaling pathway hsa04668] was observed to be up-regulated in SS and SLC.

May 10, 2019 19/27 bioRxiv preprint doi: https://doi.org/10.1101/635243; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

RIG-I-like receptor signaling pathway hsa04622

SLC

TANK SS CHUK MAPK14 CYLD DDX3X DDX58 TKFC TBK1 IFNE TMEM173 IFNA5 IFNA21 IFNB1 IFNW1 IKBKB CXCL8 IL12A IL12B CXCL10 IRF3 IRF7 MAP3K1 NFKB1 NFKBIA NFKBIB PIN1 RNF125 OTUD5 MAPK8 MAPK11 MAPK9 MAPK10 MAPK13 IFNK MAVS RELA MAPK12 IFIH1 AZI2 MAP3K7 TNF TRAF2 TRAF3 TRAF6 TRIM25 DHX58 NLRX1 SIKE1 CASP8 CASP10 IKBKG TRADD RIPK1 FADD ATG12 ATG5 ISG15 IKBKE TBKBP1 LIHC KIRC STAD ESCA CHOL HNSC GSE4607 GSE8121 GSE9692 GSE13904 GSE26378 GSE26440

Supplementary Figure S7: [RIG-I- like pathway hsa04622] was observed to be uniformly up-regulated in both SS and SLC.

May 10, 2019 20/27 bioRxiv preprint doi: https://doi.org/10.1101/635243; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Bacterial invasion of epithelial cells hsa05100

SLC ARPC5 SS ARPC4 ARPC3 ARPC1B ARPC2 WASF2 MAD2L2 ARPC1A SEPT9 CLTA CLTB CLTC SEPT12 CRK CRKL CTNNA1 CTNNA2 CTNNB1 SEPT1 DNM1 DNM2 DOCK1 CTTN SEPT6 SEPT8 FN1 PIK3R5 CD2AP CBLC GAB1 SHC2 DNM3 ARHGEF26 CTNNA3 HCLS1 ILK ITGA5 ITGB1 RHOA RHOG SHC4 MET SEPT2 PIK3CA PIK3CB PIK3CD PIK3CG PIK3R1 PIK3R2 SHC3 SEPT11 SEPT3 PTK2 PXN RAC1 ACTB ELMO2 SHC1 SRC ACTG1 VCL WAS ARHGAP10 ELMO3 ARPC5L CLTCL1 PIK3R3 CAV1 CAV2 CAV3 CBL CBLB WASF1 WASL BCAR1 ELMO1 CDC42 CDH1 LIHC KIRC STAD ESCA CHOL HNSC GSE4607 GSE8121 GSE9692 GSE13904 GSE26378 GSE26440

Supplementary Figure S8: [Bacterial invasion of epithelial cells hsa05100] Many pathogenic bacteria can invade phagocytic and non-phagocytic cells and colonize them intracellularly, then become disseminated to other cells. These bacteria use type III secretion systems to inject protein effectors that interact with the actin cytoskeleton.

May 10, 2019 21/27 bioRxiv preprint doi: https://doi.org/10.1101/635243; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Epithelial cell signaling in Helicobacter pylori infection hsa05120

SLC

ADAM10 SS TCIRG1 NOD1 CHUK MAPK14 CSK IGSF5 ATP6V0E2 HBEGF EGFR ATP6V0A2 ATP6V0D2 ATP6V1C2 GIT1 CXCL1 IKBKB CXCL8 CXCR1 CXCR2 JUN LYN MET NFKB1 NFKBIA PAK1 ATP6V0A4 F11R ATP6V1D ATP6V1H ATP6V1A ATP6V1B1 ATP6V1B2 ATP6V0C ATP6V1C1 ATP6V1E1 ATP6V0B PLCG1 PLCG2 ATP6V1G2 ATP6V0A1 ATP6AP1 MAPK8 MAPK11 MAPK9 MAPK10 MAPK13 PTPN11 PTPRZ1 JAM2 RAC1 RELA MAPK12 CCL5 MAP2K4 SRC ADAM17 TJP1 CASP3 JAM3 IKBKG ATP6V0E1 MAP3K14 ATP6V0D1 ATP6V1F ATP6V1G1 CDC42 LIHC KIRC STAD ESCA CHOL HNSC GSE4607 GSE8121 GSE9692 GSE13904 GSE26378 GSE26440

Supplementary Figure S9: [Epithelial cell signaling in Helicobacter pylori infection hsa05120] was observed to be up-regulated in both SS and SLC.

May 10, 2019 22/27 bioRxiv preprint doi: https://doi.org/10.1101/635243; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Regulation of actin cytoskeleton hsa04810

SLC

ARPC5 ARPC3ARPC4 SS ARPC2ARPC1B WASF2ABI2 PAK4APC2 MYL9MYL12B BAIAP2VAV3 MYL12AARPC1A CFL1GNA13 NCKAP1CFL2 CHRM1IQGAP2 CHRM3CHRM2 CHRM5CHRM4 CRKIQGAP3 DIAPH1CRKL DOCK1DIAPH2 EGFREGF F2PIKFYVE FGD1F2R FGF2FGF1 FGF4FGF3 FGF6FGF5 FGF8FGF7 FGF10FGF9 FGF12FGF11 FGF14FGF13 FGFR3FGFR1 FGFR4FGFR2 ITGA11RRAS2 CYFIP1MRAS ARHGEF12FN1 PIK3R5PIP5K1C FGF21FGF20 FGF22CYFIP2 GIT1GNA12 GSNARHGAP35 NCKAP1LMYLPF HRASAPC INSMYLK4 ITGA6INSRR ITGA2ITGA1 ITGA3ITGA2B ITGA5ITGA4 ITGA9ITGA7 ITGAEITGAD ITGAMITGAL ITGAXITGAV ITGB2ITGB1 ITGB3ARAF ITGB5ITGB4 ITGB8ITGB7 KRASPFN4 LIMK1RHOA MSNLIMK2 MYH10MYH9 MYL5MYL2 PPP1R12AMYLK NRASPPP1R12B PAK2PAK1 ARHGEF4PAK3 PDGFBPDGFA PDGFRBPDGFRA PFN2PFN1 PIK3CBPIK3CA PIK3CGPIK3CD PIK3R2PIK3R1 SSH1PIP4K2A SSH3PPP1R12C PPP1CBPPP1CA ENAHPPP1CC MAPK1BRK1 GNG12MAPK3 MAP2K1PDGFC PAK5MAP2K2 PXNPTK2 RAC1MYL7 RAC3RAC2 RDXRAF1 ROCK1ACTB RRASBDKRB1 SLC9A1BDKRB2 SOS2SOS1 BRAFSRC ACTG1TIAM1 VAV1TMSB4X VCLVAV2 WASEZR PIP4K2CMYH14 FGF23PDGFD DIAPH3ACTN4 PIP5K1AARPC5L PIP4K2BPIP5K1B ITGA10PIK3R3 MYLK2ITGA8 SCINSSH2 ACTN2ACTN1 FGF17FGF18 IQGAP1FGF16 ACTN3ARHGEF7 WASLWASF1 TMSB4YFGD3 CD14MYLK3 ARHGEF6MYL10 BCAR1ROCK2 CDC42FGF19 LIHC KIRC STAD ESCA CHOL HNSC GSE4607 GSE8121 GSE9692 GSE13904 GSE26378 GSE26440

Supplementary Figure S10: [Regulation of Actin cytoskeleton pathway hsa04810] was observed to be altered in same direction in SLC as in SS. In leukocyte transendothelial migration the cells need to rearrange their cellular structure in order to squeeze through the transendothelial barrier. In cancer the cells need to rearrange themselves to relocate during metastasis.

May 10, 2019 23/27 bioRxiv preprint doi: https://doi.org/10.1101/635243; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Galactose metabolism hsa00052

SLC

GALM SS AKR1B1 G6PC GAA GALE GALK1 GALT GANC GCK B4GALT1 GLA GLB1 HK1 HK2 HK3 LCT PFKL PFKM PFKP PGM1 PGM2 AKR1B10 G6PC2 SI UGP2 HKDC1 B4GALT2 MGAM G6PC3 LIHC KIRC STAD ESCA CHOL HNSC GSE4607 GSE8121 GSE9692 GSE13904 GSE26378 GSE26440

Supplementary Figure S11: [Galactose metabolism pathway hsa00052] was observed to be up-regulated in both SS and SLC.

May 10, 2019 24/27 bioRxiv preprint doi: https://doi.org/10.1101/635243; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Bladder cancer hsa05219

SLC

CDK4 SS CDKN1A CDKN2A RASSF1 DAPK1 DAPK3 E2F1 E2F2 E2F3 TYMP EGF EGFR ERBB2 FGFR3 DAPK2 HRAS CXCL8 ARAF KRAS MDM2 MMP1 MMP2 MMP9 MYC NRAS MAPK1 MAPK3 MAP2K1 MAP2K2 RAF1 RB1 CCND1 BRAF THBS1 TP53 VEGFA RPS6KA5 CDH1 LIHC KIRC STAD ESCA CHOL HNSC GSE4607 GSE8121 GSE9692 GSE13904 GSE26378 GSE26440

Supplementary Figure S12: [Bladder cancer pathway hsa05219] was observed to be up-regulated in both SS and SLC.

May 10, 2019 25/27 bioRxiv preprint doi: https://doi.org/10.1101/635243; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

p53 signaling pathway hsa04115

SLC

CDK2 SS CDK4 CDK6 CDKN1A CDKN2A GADD45G CHEK1 CHEK2 SESN3 DDB2 GADD45A RCHY1 BBC3 SESN1 SFN APAF1 IGF1 IGFBP3 FAS CD82 MDM2 MDM4 GADD45B ATM RRM2B SERPINE1 SHISA5 GTSE1 SERPINB5 PMAIP1 CYCS ATR STEAP3 PIDD1 RPRM PTEN ADGRB1 BAX CCND1 RRM2 BID TP53AIP1 PERP RFWD2 ZMAT3 SIAH1 THBS1 TP53 TP73 TSC2 CASP3 SESN2 CASP8 CASP9 PPM1D CCNB3 TNFRSF10B CCNB1 CCND2 CCND3 CCNE1 CCNG1 CCNG2 CCNB2 CCNE2 EI24 TP53I3 CDK1 LIHC KIRC STAD ESCA CHOL HNSC GSE4607 GSE8121 GSE9692 GSE13904 GSE26378 GSE26440

Supplementary Figure S13: [p53 signaling pathway hsa04115] was observed to be up-regulated in both SS and SLC.

May 10, 2019 26/27 bioRxiv preprint doi: https://doi.org/10.1101/635243; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Ribosome biogenesis in eukaryotes hsa03008

SLC

RCL1 SS MPHOSPH10 POP7 EMG1 NXF1 RPP30 RPP38 TBL3 POP4 RPP40 UTP14A WDR3 POP1 WDR36 RPP25L CSNK2A1 CSNK2A2 CSNK2B SPATA5 FBL XRN2 WDR43 MDN1 GTPBP4 REXO2 RRP7A NOB1 DROSHA NXT1 GNL2 EIF6 SNU13 NVL NMD3 FCF1 UTP18 POP5 NOP58 GAR1 XRN1 GNL3L RPP25 HEATR1 RBM28 NAT10 IMP3 LSG1 NOP10 NHP2 RIOK2 UTP6 NXT2 NXF5 NXF3 REXO1 RAN NOL6 TCOF1 XPO1 EFL1 REXO5 RIOK1 WDR75 UTP15 UTP4 IMP4 UTP14C BMS1 LIHC KIRC STAD ESCA CHOL HNSC GSE4607 GSE8121 GSE9692 GSE13904 GSE26378 GSE26440

Supplementary Figure S14: [Ribosome biogenesis in eukaryotes pathway hsa03008] was observed to be in different direction in SS and SLC. This pathway was found uniformly up-regulated in SLCs but is uniformly down-regulated in SS.

May 10, 2019 27/27