Hindawi Publishing Corporation BioMed Research International Volume 2014, Article ID 159078, 23 pages http://dx.doi.org/10.1155/2014/159078

Research Article Evolution of Network Biomarkers from Early to Late Stage Bladder Cancer Samples

Yung-Hao Wong, Cheng-Wei Li, and Bor-Sen Chen

Lab of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan

Correspondence should be addressed to Bor-Sen Chen; [email protected]

Received 11 April 2014; Revised 9 July 2014; Accepted 10 July 2014; Published 18 September 2014

Academic Editor: Tzu-Hao Chang

Copyright © 2014 Yung-Hao Wong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

We use a systems biology approach to construct protein-protein interaction networks (PPINs) for early and late stage bladder cancer. By comparing the networks of these two stages, we find that both networks showed very significantly different mechanisms. To obtain the differential network structures between cancer and noncancer PPINs, we constructed cancer PPIN and noncancer PPIN network structures for the two bladder cancer stages using microarray data from cancer cells and their adjacent noncancer cells, respectively. With their carcinogenesis relevance values (CRVs), we identified 152 and 50 significant proteins and their PPI networks (network markers) for early and late stage bladder cancer by statistical assessment. To investigate the evolution of network biomarkers in the carcinogenesis process, primary pathway analysis showed that the significant pathways of early stage bladder cancer are related to ordinary cancer mechanisms, while the ribosome pathway and spliceosome pathway are most important for late stage bladder cancer. Their only intersection is the ubiquitin mediated proteolysis pathway in the whole stage of bladder cancer. The evolution of network biomarkers from early to late stage can reveal the carcinogenesis of bladder cancer. The findings inthis study are new clues specific to this study and give us a direction for targeted cancer therapy, and it should be validated in vivoor vitro in the future.

1. Introduction carcinogenesis and novel drug designs for cancer therapy. Several bioinformatics methods have been developed and Cancer is the leading cause of death worldwide and its applied to compare normal tissue with cancerous tissue etiology occurs at the DNA, RNA, or protein level. It is a very to determine what cancer driving genes can act as cancer complex disease involving cascades of spatial and temporal biomarkers [5–12]. changes in the genetic network and metabolic pathways Genes and proteins function cooperatively to regulate [1]. Various research studies have revealed that cancers are common biological cell processes by coregulating each other caused by multiple factors and intertwined events. Thus, in [13]. Generally, molecular regulation and interaction proceed cancer therapy, it is important to dissect the diverse molecular with time and vary in different tissues. There must exist great mechanisms of cancer to identify potential cancers. Bladder differences in these variations between cancer and normal cancer is amongst the 10 most common carcinomas in the tissue. Proteins mutually interact with each other in the USA, with 72,570 newly diagnosed cases, and it was the cause cell, and they form the PPI networks (PPINs). Currently, a of 15,120 deaths in 2013 [2]. In particular, Kaufman et al. lotoftheresearchhasfocusedontherelationshipbetween pointedouttoitasthesecondmostcommonformofcancer PPINs and cancer development. For example, analysis of the in 2008 [3]. In this study, we compared the early and late cancer-related PPINs of apoptosis has unraveled the molec- stages of bladder cancer to reveal additional mechanisms of ular mechanisms of cancer, which has helped to identify bladder cancer development [4]. potential novel drug targets [14]. Our previous work [14]had Biomarker discovery of various cancers is one of the key successfully identified the network markers of lung cancer. In topic areas of cancer research. It can aid investigations into this study, we modified our previous method and applied the 2 BioMed Research International novelconcepttostudytheevolutionofnetworkmarkersfrom 2. Materials and Methods early to late stage bladder cancer. Based on their PPI information and the gene expression 2.1. Overview of the Bladder Cancer Network Markers Con- profiles from cancer and surrounding normal samples, two struction Process. A flowchart representing the construction PPI networks with quantitative protein association abilities of network biomarkers for early and late stage bladder cancer (1) for each cancer stage (early stage and late stage) and the sur- is shown in Figure 1. We combined two data sources: rounding noncancerous tissue are constructed, respectively. microarray data of bladder cancer and noncancer samples For each stage, the network structure and protein association from the GEO database, while the cancer samples were abilities of the cancer and noncancer PPI networks are then divided into two groups: early stage and late stage bladder (2) compared to obtain sets of significant proteins which play cancer. The PPI database was required to construct important roles in the carcinogenesis process of bladder the PPINs for bladder cancer. This data was used for PPI cancer. pool selection and the selected PPIs and the microarray Recently, PPI targets seem to have become a paradigm data were then used for PPI network (PPIN) construction. for the drug discovery of cancer therapy and precision Through regression modeling and the maximum likelihood medicine [15]. Unlike conventional drug design focusing parameter estimation method, a cancer PPIN (CPPIN) and on the inhibition of a single protein, usually an enzyme a noncancer PPIN (NPPIN) was then obtained. The two or receptor, small-molecule inhibition of direct PPIs that constructed cancer and noncancer PPINs were compared mediate many important biological processes is an emerging to obtain the sets of significant proteins for bladder cancer and challenging concept in drug design, especially for cancer. basedonthecarcinogenesisrelevancevalue(CRV)for Extensive biological and clinical investigations have led to each protein and the statistical assessment. The signifi- the identification of PPI hubs and nodes that have been cant proteins and PPIs within these proteins were used to critical for the acquisition and maintenance of characteristics construct network markers at early and late stage bladder for cell transformation in cancer. Such cancer-enabling PPIs cancer. will become promising therapeutic targets in anticancer strategies as the technologies in PPI modulator discovery and 2.2. Data Selection and Preprocessing. The microarray gene validating agents in the clinical setting advance in the future expression dataset of bladder cancer was obtained from the [15]. NCBI gene expression omnibus (GEO) [19]. In this study, Therefore, future research directed at PPI target discov- we chose GSE13507 [20] and its corresponding platform ery, PPI interface characterization, and PPI-focused chemical GPL6012 as our research object. The same dataset contained libraries are expected to accelerate the development of the the early and late stage bladder cancer and noncancer next generation of PPI-based anticancer agents. However, samples. We only used the data derived from nonprocessed the PPI networks of cancer are very complex and quite primary biopsies to avoid the discrepancies in gene expres- differ between early and late stage cancer. In such circum- sion that are intrinsic to cell culture and fixation. Therefore, stances, we will focus on the PPI network markers with their the dataset utilized contained primary tumor samples of both significant carcinogenesis relevance value (CRV) to exploit stages from patients and adjacent nontumor tissue samples the important targets and their PPI interface for early and from the same cancer patients, which could be considered as late stage cancer characterization. Then, we will not only control samples. To describe the extent of a patient’s cancer, gain insight into the crucial common pathways involved the cancers were classified into four stages according to their in bladder carcinogenesis, but we will also obtain a highly degree of invasion and migration using the TNM staging promising PPI target for bladder cancers. If we are then system, as defined by the American Joint Committee on able to develop various combined anticancer strategies to Cancer (AJCC) and the International Union against Cancer target PPIs in the early and late stage network markers in the (UICC).Wethendividedthecancersamplesintotwogroups. future, it may provide emerging opportunities for anticancer In general, stages I and II described early stage cancers therapeutic approaches. that have higher curability rates with medical treatment, Chen et al. developed a dynamical network biomarker while stages III and IV described the late stages. However, (DNB) that can serve as a general early warning signal to there were no corresponding noncancer samples in the indicate an imminent bifurcation or sudden deterioration surrounding area for each stage and we had only one group before the critical transition occurs; that means it can identify of surrounding noncancer samples (Table 1). We built CPPIN predisease state by time series microarray data. We use and NPPIN for both early and late stage bladder cancer in different approach from their methods by sample microarray this study. We obtained 37 and 106 samples for the early data from bladder cancer patients of different stages. Our and late stage cancer, respectively, and 58 noncancer samples. approach could also be extended to predict some similar Toavoidoverfittinginnetworkconstruction,themaximum resultsastheirresearch.Thatis,inthisstudy,wesimply degree of the proteins in the PPI network should be less than divided the cancer into early and late stages, but there are the cancer/noncancer sample number [14]. In this dataset, more stages of cancer, such as stages I, II, III, and IV. If we we had a greater number of cancer and noncancer samples could observe the time evolution of the cancer biomarkers to overcome the sample size restriction on the size of the at these more different stages, we could also predict the network. Prior to further analysis, the gene expression value, predisease state by comparing it with these cancer biomarkers ℎ𝑖𝑗 ,wasnormalizedto𝑧-transformed scores, 𝑔𝑖𝑗 ,foreach at different stages [16–18]. gene, i, and then the normalized expression value resulting BioMed Research International 3

Table 1: Descriptive information on datasets extracted from the GEO database used in this study.

Cancer GEO accession number Early stage Late stage Adjacent normal Platform Bladder cancer GSE13507 106 37 58 GPL6102 Cases are grouped by cancer and surrounding normal tissues came from human patients of early stage and late bladder cancer.

BioGRID Early and late Noncancer The gene ontology bladder cancer protein-protein interactions microarray data microarray data

Selection of protein pool

Construction of candidate PPI network

Construction of PPI networks by identified parameter and AIC method

Early (or late) stage Early (or late) stage refined refined cancer PPI noncancer PPI networks networks (CPPINs) (NPPINs)

Two differential PPI networks for two stage bladder cancers by comparing refined CPPINs with NPPINs, respectively

Determination of significant proteins in two stages bladder cancers based on carcinogenesis relevance value (CRV) for each protein

Discuss the evolution of network biomarker from early to late stage bladder cancer

Figure 1: The flowchart of constructing both stages of network marker of bladder cancer and the investigation of the carcinogenesis mechanisms. We integrate microarray data, GO database, and PPI information to construct the PPI network. These data are used for pool selection, and then the selected proteins and the microarray data are used for the contribution of protein-protein interaction network (PPIN) by maximum likelihood estimation and model order detection method, resulting in bladder cancer PPIN (CPPIN) and noncancer PPIN (NPPIN) of early and late stage. The two constructed PPINs can be used for the determination of significant proteins of tumorigenesis bythe difference between two PPI matrices of two constructed PPINs. With the help of the differential PPI matrix (network) between CPPIN and NPPIN, carcinogenesis relevance value (CRV) is computed for each protein, and significant proteins in carcinogenesis are determined based on P value the CRVs of these proteins in the differential PPI matrix between CPPIN and NPPIN. These significant proteins are obtained for early and late stage bladder cancers.

had a mean 𝜇𝑖 =0and standard deviation 𝜎𝑖 =1over sample from more than 30 model organisms [21]. The above two 𝑗 [11, 14]. databases were mined for bladder cancer and noncancer The PPI data for Homo sapiens were extracted from PPI networks using their corresponding microarray data. the Biological General Repository for Interaction Database These early and late stage bladder cancer and noncancer PPI (BioGRID, downloaded in October 2012). BioGRID is an networks were then compared to obtain network markers. open-access archive of genetic and protein interactions that are curated from the primary biomedical literature of all 2.3. Selection of Protein Pool and Identification of the Protein- major model organisms. As of September 2012, BioGRID Protein Interaction Networks (PPINs) for Cancerous and Non- houses more than 500,000 manually annotated interactions cancerous Cells. To integrate gene expression with PPI data 4 BioMed Research International to construct the corresponding CPPINs and NPPINs, we set After constructing1 ( ) for the PPI model of each protein up a protein pool containing differentially expressed proteins. in the candidate PPIN, we used the maximum likelihood The gene expression values were reasonably assumed to estimation method [23] to identify the association parameters correlate with protein expression levels. We used one-way in (1) by microarray data as follows (see Supplementary Mate- analysis of variance (ANOVA) to analyze the expression of rials S.1 available online at http://dx.doi.org/10.1155/2014/ each protein and select for proteins with differential expres- 159078): sion levels. This method allowed determination of significant 𝑀 differences between cancer and noncancer datasets. The null 𝑖 𝑥𝑖 (𝑛) = ∑𝛼̂𝑖𝑗 𝑥𝑗 (𝑛) +𝑤𝑖 (𝑛) , (2) hypothesis (Ho) was based on the assumption that the mean 𝑗=1 protein expression levels of cancer and noncancer sets are the same. Bonferroni adjustment [22], a type of multiple testing, where 𝛼̂𝑖𝑗 is identified using microarray data in accordance was used to detect and correct proteins with discrepancy. with the maximum likelihood estimation method (see Sup- 𝑃 Proteins with a value of less than 0.01 were included in plementary Materials). the protein pool. However, if the proteins in the protein Once the association parameters for all proteins in the pool did not have PPI information, they were eliminated. In candidate PPI network were identified for each protein, the addition, proteins that were not already in the protein pool significant protein associations were determined using the were included if their PPI information could determine that interaction model order detection method based on the esti- they had a tight relationship with proteins already in the mated association abilities. The Akaike information criterion pool. As a result, the protein pool contained proteins that had (AIC) [23] and Student’s 𝑡-test [24]wereemployedforboth certain differences in expression levels and proteins that had model order selection and significance determination of the tight relationships with the aforementioned proteins. In this protein associations in 𝛼̂𝑖𝑗 (see Supplementary Materials S.2). case, the protein pool in bladder cancer consisted of 2,245 proteins in the early stage and 1,101 proteins in the late stage. 2.4. Determination of Significant Proteins and Their Network On the strength of the significant pool and PPI informa- Structures in the Carcinogenesis of Four Types of Cancers. tion, candidate PPI networks for early and late stage bladder After 𝑃 values were determined using the AIC order detection cancer were constructed for bladder cancer and noncancer and Student’s 𝑡-test, spurious false positive PPIs 𝛼̂𝑖𝑗 in (2)were by linking the proteins that interacted with each other. In prunedawayandonlythesignificantPPIsthatremainedwere other words, the proteins that had PPI information through refined as follows: the pool were linked together, resulting in candidate PPI 󸀠 networks. 𝑀𝑖 󸀠 As the candidate PPIN included all possible PPIs under 𝑥𝑖 (𝑛) = ∑𝛼̂𝑖𝑗 𝑥𝑗 (𝑛) +𝑤𝑖 (𝑛) , 𝑖=1,2...𝑀, (3) various environments, different organisms, and experimen- 𝑗=1 tal conditions, the candidate PPIN needed to be further 󸀠 confirmed by microarray data to identify appropriate PPIs where 𝑀𝑖 ≤𝑀𝑖 denotes the number of significant PPIs of according to the biological processes that are relevant to PPIN, with the target protein 𝑖.Inotherwords,anumberof 󸀠 cancer. To remove false positive PPIs from each candidate 𝑀𝑖 −𝑀𝑖 (or false positives) are pruned in the PPIs of target PPIN for different biological conditions, we used both a PPI protein 𝑖. One protein by one protein (i.e., 𝑖=1,2,...,𝑀for model and a model order detection method to prune each all proteins in the refined PPIN in (3)) results in the following candidate PPIN using the corresponding microarray data to refined PPIN: approach the actual PPIN. Here, the PPIs of a target protein 𝑋 (𝑛) =𝐴𝑋(𝑛) +𝑤(𝑛) 𝑖 in the candidate PPIN can be depicted by the following protein association model: 𝑥1 (𝑛) [ ] 𝛼̂11 ... 𝛼̂1𝑀 𝑥2 (𝑛) 𝑀 [ ] [ ] 𝑖 𝑋 (𝑛) = [ ] ,𝐴=[ . . ] , [ . ] . d . 𝑥𝑖 [𝑛] = ∑𝛼𝑖𝑗 𝑥𝑗 [𝑛] +𝜔𝑖 [𝑛] , (1) . [𝛼̂𝑀1 ⋅⋅⋅ 𝛼̂𝑀𝑀] 𝑗=1 [𝑥𝑀 (𝑛)] 𝑤󸀠 (𝑛) (4) where 𝑥𝑖[𝑛] represents the expression levels of the target 1 [ ] protein 𝑖 for the sample 𝑛; 𝑥𝑗[𝑛] represents the expression [ ] [ 𝑤󸀠 (𝑛) ] level of the 𝑗th protein interacting with the target protein 𝑖 for [ 2 ] [ ] the sample 𝑛; 𝛼𝑖𝑗 denotes the association interaction ability 𝑤 (𝑛) = [ ] , [ . ] between the target protein 𝑖 and its 𝑗th interactive protein; [ . ] [ . ] 𝑀𝑖 represents the number of proteins interacting with the 𝑖 𝜔 [𝑛] 󸀠 target protein ;and 𝑖 represents the stochastic noise due [𝑤𝑀 (𝑛)] to other factors or model uncertainty. The biological meaning of (1) is that the expression levels of the target protein 𝑖 are where the interaction matrix 𝐴 denotes the PPIs. associated with the expression levels of the proteins interact- If there is no PPI between proteins 𝑖 and 𝑗 or it is pruned ing with it. Consequently, a protein association (interaction) away by AIC order detection due to insignificance in the model for each protein in the protein pool can be built as (1). refined PPIN then 𝛼̂𝑖𝑗 =0. In general, 𝛼̂𝑖𝑗 = 𝛼̂𝑗𝑖, but if this is BioMed Research International 5

not the case, the larger one will be chosen as 𝛼̂𝑖𝑗 = 𝛼̂𝑗𝑖 to avoid between CPPIN and NPPIN of the 𝑘th stage bladder cancer the situation where 𝛼̂𝑖𝑗 ≠ 𝛼̂𝑗𝑖. The above PPIN construction in (8), a score, which we named the carcinogenesis relevance methodwasemployedtoconstructtherefinedPPINsforeach value (CRV), was presented to quantify the correlation of 𝑘 stage of bladder cancer (early and late) and noncancer cells. each protein in 𝐷 with the significance of carcinogenesis as The interaction matrices 𝐴 of the refined PPINs in (4)for follows [14]: cancer and noncancer cells of both the early and late stages 𝑘 CRV of bladder cancer were constructed, respectively, as follows: [ 1 ] [ . ] [ . ] 𝛼̂𝑘 ... 𝛼̂𝑘 [ . ] [ 11,𝐶 1𝑀,𝐶 ] 𝑘 = [ 𝑘 ] , 𝑘 [ . . ] CRV [ CRV𝑖 ] (9) 𝐴𝐶 = . . , [ ] [ . d . ] . 𝑘 𝑘 [ . ] 𝛼̂ ⋅⋅⋅ 𝛼̂ 𝑘 [ 𝑀1,𝐶 𝑀𝑀,𝐶] CRV (5) [ 𝑀] 𝑘 𝑘 𝛼̂ ... 𝛼̂ 𝑘 𝑀 𝑘 [ 11,𝑁 1𝑀,𝑁 ] where CRV = ∑ |𝑑 |,andk = early and late stage bladder 𝑘 [ . . ] 𝑖 𝑗=1 𝑖𝑗 𝐴𝑁 = [ . d . ] , cancer. . . 𝑘 𝑘 𝑘 𝛼̂ ⋅⋅⋅ 𝛼̂ The CRV𝑖 in (9) quantifies the differential extent of [ 𝑀1,𝑁 𝑀𝑀,𝑁] protein associations of the 𝑖thprotein(theabsolutesumof 𝑘 𝑘 𝑘 𝑘 the 𝑖th row of 𝐷 in (8)) and the CRV can differentiate where 𝑘 = early and late stage bladder cancer; 𝐴𝐶 and 𝐴𝑁 𝑘 CPPIN from NPPIN in the 𝑘th stage bladder cancer. In denote the interaction matrices of refined PPIN of the th 𝑘 cancer and noncancer, respectively; 𝑀 is the number of pro- other words, the CRV𝑖 in (9) could represent the network 𝑖 teins in the refined PPIN. Therefore, the protein association structure difference of the th protein between the cancer and 𝑘 model for CPPIN and NPPIN in the 𝑘th stage bladder cancer noncancer networks in the th stage bladder cancer. and noncancer can be represented by the following equations In order to investigate what proteins are more likely 𝑘 according to (4)and(5): involved in the th stage bladder cancer, we needed to cal- culate the corresponding empirical 𝑃 value to determine the 𝑘 𝑘 𝑘 𝑘 𝑥𝐶 (𝑛) =𝐴𝐶𝑥𝐶 (𝑛) +𝑤𝐶 (𝑛) , statistical significance of CRV𝑖 . To determine the observed (6) 𝑃 𝑘 𝑘 𝑘 𝑘 value of each CRV𝑖 , we repeatedly permuted the network 𝑥𝑁 (𝑛) =𝐴𝑁𝑥𝑁 (𝑛) +𝑤𝑁 (𝑛) , structure of the candidate PPIN of the 𝑘th stage bladder cancer as a random network of the 𝑘th stage bladder cancer. 𝑘 where = early and late stage bladder cancer; Eachproteinintherandomnetworkofthe𝑘th stage bladder 𝑇 cancer will have its own CRV to generate a distribution of 𝑥𝑘 (𝑛) =[𝑥𝑘 𝑥𝑘 ⋅⋅⋅ 𝑥𝑘 ] , 𝑘 𝐶 1𝐶 2𝐶 𝑀𝐶 CRV𝑖 for 𝑘 = early and late stage bladder cancer. Although (7) there was random disarrangement of the network structure, 𝑘 𝑘 𝑘 𝑘 𝑇 𝑥𝑁 (𝑛) =[𝑥1𝑁 𝑥2𝑁 ⋅⋅⋅ 𝑥𝑀𝑁] the linkages of each protein were maintained. In other words, the proteins with which a particular protein interacted were 𝐾 𝐾 denote the vectors of expression levels; and 𝑤𝐶 (𝑛) and 𝑤𝑁(𝑛) permuted without changing the total number of protein indicate the noise vectors of PPINs in the 𝑘th cancer and interactions. This procedure was repeated 100,000 times and noncancer cells, respectively. the corresponding 𝑃 value was calculated as the fraction of 𝑘 𝑘 𝑘 The different matrix 𝐴𝐶 −𝐴𝑁 of the differential PPI random network structure in which the CRV𝑖 is at least as network between CPPIN and NPPIN in the 𝑘th cancer is large as the CRV of the real network structure. According 𝑘 defined as follows: to the distributions of the CRV𝑖 of the random networks, 𝑘 𝑘 𝑘 the CRV𝑖 in (9)witha𝑃 valueoflessthanorequalto0.01 𝑑11 ... 𝑑1𝑀 [ ] wasregardedasasignificantCRVandthecorresponding 𝐷𝑘 = [ . . ] . d . protein was determined to be a significant protein in the 𝑘 𝑘 𝑘 [𝑑 ⋅⋅⋅ 𝑑 ] carcinogenesis of the th stage bladder cancer: a protein with 𝑀1 𝑀𝑀 𝑃 (8) a value greater than 0.01 was removed from the list of 𝑘 𝑘 𝑘 𝑘 significant proteins in carcinogenesis (in other words, if the 𝛼̂11,𝐶 − 𝛼̂11,𝑁 ... 𝛼̂1𝑀,𝐶 − 𝛼̂1𝑀,𝑁 [ ] 𝑃 𝑘 𝑖 = [ . . ] , value of CRV𝑖 was greater than 0.01, then the th protein [ . d . ] 𝑘 𝑘 𝑘 𝑘 𝑘 was removed from the CRV𝑖 in (9) and the remainder in the 𝛼̂ − 𝛼̂ ⋅⋅⋅ 𝛼̂ − 𝛼̂ 𝑘 [ 𝑀1,𝐶 𝑀1,𝑁 𝑀𝑀,𝐶 𝑀𝑀,𝑁] CRV with 𝑃 values of CRVs less than 0.01 were considered significant proteins of the 𝑘th stage bladder cancer). 𝑘 𝑑𝑘 where = early and late stage bladder cancer; 𝑖𝑗 denotes Based on the 𝑃 value of the CRVs for all proteins (𝑖= the protein association ability difference between CPPIN 1,2,...,𝑀) and the two stages of bladder cancer (𝑘 =early and NPPIN in the 𝑘th stage bladder cancer; and the matrix and late stage bladder cancer), we generated two lists of 𝑘 𝐷 indicates the difference in network structure between significant proteins for each of the two stages according to CPPIN and NPPIN in the 𝑘th stage bladder cancer. In order theCRVandthestatisticalassessmentofeachsignificant 𝑘 𝑘 to investigate carcinogenesis from the difference matrix 𝐷 proteininCRV in (9). We found 152 significant proteins 6 BioMed Research International in early stage bladder cancer and 50 significant proteins in 2.6. The Contribution of Protein Interaction Network Will late stage bladder cancer. These proteins showed significant AffecttheResultsofBiomarkersandtheEvolutionofNetwork changes between the CPPIN and NPPIN in the carcinogenic Biomarkers. Our cancer PPI model is constructed from the process according to their corresponding stage of cancer and differential expression of cancer and noncancer microarray we suspected that these changes might play important roles in data and data mining of PPI information from BioGRID the carcinogenesis process of bladder cancer. These findings database. So, the early and late stage bladder cancer CPPINs warrant further investigation. (cancer PPI networks) and NPPINs (noncancer PPI net- The intersections of these significant proteins in the early works) are the results of our systems biology model using the and late stages of bladder cancer and their PPIs are known as original microarray data and PPI databases. There are three the core network markers appearing in all stages of bladder key factors that will affect the final results. cancer. In contrast, the unique significant proteins and their (i) The effect of different microarray data: we know PPIs in each stage of bladder cancers are known as the specific that the microarray data has the shortage of irre- network markers for each stage of cancer. We found that producible. That means even in the same case the there were 18 significant proteins that could be classified as microarray data does not promise to produce the a core network marker in the whole carcinogenesis process same result as the previous ones. Also, for the same of bladder cancer. We also found 134 significant proteins in cancers, patients of different ethnics, different age, or the specific network marker of early stage bladder cancer and different sex will give the different microarray data. 32 significant proteins in the specific network marker of late This is the first factor to affect the final results. stage bladder cancer. (ii) The effect of different original PPI databases: we know that PPI databases, such as BioGRID and 2.5. Pathway Analysis. Much valuable cellular information MIPS, are constructed from putative and validated by canbefoundintheknownpathways,whichareuseful wet-lab experiments. Due to the advances of many for describing most “normal” biological phenomena. All of high-throughput experimental skills, the original PPI these known pathways are the result of repeated testing and databases are evolved with time growing. The new verification and the entire pathway network has given defi- updated original PPI databases are the second factor nitions for most links. Therefore, the proteins we identified to affect the final results. to be significant in the above network markers were mapped (iii) The effect of systems biology model: microarray onto the known pathway networks (e.g., the KEGG or data, PPI databases, and PPI interaction model in PANTHER pathway) to investigate significant pathways with (1) are employed to construct the PPI networks of the network marker and to explore the relationships between normal and cancer cells by the maximum likeli- these pathways and the carcinogenesis of bladder cancer. hood parameter estimation method (see Supplemen- This approach supports the view that systems biology can tary Material S.1). The AIC system order detection help identify significant network biomarkers in both normal method (Supplementary Materials S.2) is employed and cancerous pathways to their roles in the pathogenesis of toprunethefalsepositivePPIstoobtainthereal cancer. PPI networks of normal and cancer cells; that is, Together with comprehensive pathway databases such as we use the so-called reverse engineering method to the Kyoto Encyclopedia of Genes and Genomes (KEGG), construct PPI networks of normal and cancer cells. we used a series of bioinformatics pathway analysis tools to Then the differential PPI network between cancer PPI identify biologically relevant pathway networks [25]. KEGG network and normal PPI network is obtained in (8) includes manually curated biological pathways that cover to investigate PPI variations of each protein in the three main categories: systems information (e.g., human differential PPI network due to the carcinogenesis. diseases and drugs), genomics information (e.g., gene cat- Finally, the carcinogenesis value (CRV) based on PPI alogs and sequence similarities), and chemical information variations is also proposed to evaluate the significance (e.g., metabolites and biochemical reactions). At present, of carcinogenesis for each protein of differential PPI KEGG contains 134,511 distinct pathways generated from 391 network. Proteins with significant CRV𝑃 ( value < original reference pathways [26]. Therefore, to investigate 0.01) are considered as significant proteins of the the pathways involved in carcinogenesis, the bioinformat- cancer. The significant proteins in Table 3 are these ics database DAVID [27, 28], which generates automatic significant proteins of early and late stage bladder outputs of the results from KEGG pathway analysis [27], cancers, and these proteins and their PPIs construct wasusedforthepathwayanalysisofsignificantproteins the interaction network in Figure 2.Finally,fromthe identified in network markers to determine their roles in early to late stage bladder cancer network markers, the pathogenesis of early and late stage bladder cancer. we investigate the mechanism of carcinogenesis pro- Our methodology does not contain the pathway analysis cess with the help of databases (e.g., GO database, and gene set enrichment analysis. To complete our research DAVID,andKEGGpathwaydatabase)andtryto results,weusedtheNOAsoftwaretodothepathway find multiple network target therapy of cancer. Unlike analysis and gene set enrichment analysis on biological the conventional theoretical methods, which always processes, cellular components, and molecular functions give a single mathematical model for cancer network [19, 29]. for a more detailed theoretical analysis, this study BioMed Research International 7

Cancer PPI B ACAP1 ANG APBB1 C14orf169 C7 EME2 GATA5 JAM2 MGAT1 NTF3

Cancer PPI A IFFO1 MDM2 TSHZ3 KAT5 CLU FANCM KLF2 JAM3 SMAD9 NTRK3

PTGIS

ESM1 PTGS1

CYGB NTNG2 DENND2A ZNF200 MAOB ITGAL NFATC4

P2RX7 AGMAT VNN2 LDOC1 ATP13A2 NXPH3 SNRNP35 AQP1 Dlg4 NLGN1 ITGB2 TSPAN7 FAM50B LSR WFDC1 TCEAL2 TRRAP SERPINB6 DLG2 EFEMP2 TAB1 FAM189A2 HMOX2PPFIA4 UBE2J2 EIF2C3 PLCG1 ISCUCALCOCO2ADCY5 ASH2L IL16 MAPT PPFIA3 FBLN5 SIRT5TMEM173 KIAA0430 UBXN7 NT5DC2 HOXA1 UBE2D4 PITX2 GINS4 PYGO2 ARHGAP20 KRTCAP2SDC2 RPS20 PDPK1 ACIN1 TCEAL4 ACTG2FAU HIST1H3A B4GALT2 MACROD2 MSN ICAM1 SKA2 ZYX CNOT10 PLAT EZR PLK3 RPS19 GIT1 MYO1FNCOA2 SFN RWDD3 PPP2R2B NCDNMRPL46 PDZRN3 HLA-DMB NOP56 CRYM RPS9 CRK KCMF1 DHX15 IKZF2 ZNF606F10 HAAOEIF3H RPS28 TROAP SETD1A PRF1 EIF3E RPS27 EIF3KPSMC5 BRD4 ZBTB20 UBA5 RPS6 FAM122A NCOA6ADRBK1 RBBP5 PPP1R12B PIGRREM1 KRT19 ASNS KAT2A KCTD12 RNF123 EEF2 GINS2PPP1R12AEIF3APFDN1 FAM107A SYTL3 MME TSR1 PCP4 RPL27A GSG2 WDR5 ACTR3DDX49 IL33 UBN1 TRIM25 DDOSTPPP2CB ZNHIT2 RGS2 SNORD55 SRSF11 HIST2H3C MLL5 HCN3 AGPAT9 ARRB2 PSMC6 CC2D1APRKCB TOP3BBRI3BP VRK1 SH3BP5L FAM110B MLF1IPPRKACBSHISA5 CFL1 TP53RKPRDX6 MPGSKA3 PPFIA2 HOXA11 TERF2 RPS25 RPL19MEF2C CAPNS1 CTU1 R3HDM2 SNX1 C16orf59 PROS1SAMD3DNAJB1PKIG BAX BMI1 TRIM8CNOT6PDE4B EDIL3 PEX14 CHD7 ACACB HIF1A KRT8 SAMD1 GSTCD CASP1ERAL1 RPSA EIF2C4 DYNLL1 MCM6 Psma2 SYNE1 MYL9RPL14 EPAS1MYH9 PRDX2 SLC35E2 ANKRD5 RPL7 ASLDAB2 PABPC1 PLA2G4AC2orf44 BMPR2 ESR2 CSE1L RPL12 RPS13 UBR4IQGAP3 NCSTNSKA1 HMGA1 CTSG SF3B14 CNOT3 GTPBP2EMILIN1 TFIP11 CCNDBP1KIAA1377 SYNMEIF3L SPTBN1HCFC1 RBCK1 CA9 RPS23 SMARCD1 HSPB1 WIBG ATP1A2 RPL11RAB27A CALR KIAA1737 Ndc80MAPK14LIMS2 PPP1R2 MAP3K7PPP5C RNASEH2A TSPAN4DNAJA3 STK32C ME2 YEATS2 GPT2 SNRPD2 HIST1H3D RPL6LRRK2CDK5RPL9 NFIA VPS33A RAVER1YWHAH ARID1A RAD23ASLMAP GNB2L1 DHX9 KATNB1PFDN6 RPS18 SF3B2RPS19BP1 SARS ILF2 EIF1BRPA3 STRAP FBXO25 ASF1ASAMD4BRBBP4 F8 HLA-DRB3 ATF2RBM4HLA-DRASHMT2 RPL23APOLR1CHTRA2 TERF1TERF2IP RPAP3 GNL2SAP18 PRKCD UTP14A GTF2I ITGB1 CEBPB TADA2A ADRB2ECD SRSF7 CDC23 HIST1H2BO RPL10A NDEL1P4HB SLC39A7 SPAG5 RAD50 HIST1H3F EFEMP1 SAFBOTUB1 RECQL4PALLD RPL4 RPL15 LIMK1UTRN CD4 PAK4 G3BP1 COL5A1 MNAT1 MAP2K3 MTOR CACNA1ACAPN1 RAD21 CDAN1 SF3A3 RPL29 CDCA4CASP8 TCF3 HNRNPKRPL36RPS11 SNRNP200FERMT2 ACTG1ZEB1 RPL37A RPS8 IKZF1 CDC42SE2 ACTR2GABARAP IPO5 SCRIBARID3B CCNT1RPS3 RPL5 BRD7 DNMT3B TJP1TUBB2B UBAP2LPSMC1 GLI1CRMP1 YPEL5 RPL3 MMS19 CAPZA1 TSTA3 DISC1 RNPS1RPL7A HNRNPAB NSUN2MEPCE AHR CTBP1ARNTL ELAVL1TRIM24 KDM1A CENPARPS5 RPL22 USP39RPL30 DPCD TSPYL1 TMEM54MYOD1 XRCC5HLA-DPB1 KIAA1967 OTUB2ACD SPP1YWHAG SMARCA2POF1B RAB7AHIST1H4A PPIL1RPS17PRKAA1NKX3-1 EIF3F RPL23 ITGA4 PPP1CBMTA3NCAPGRCC2 ITCH ZNF521 PIK3CD SRGNCTBP2 CHEK2 STK25 KIF20A ITSN1 LNX1 PHKB SRSF9 CNTN2C1R MST4 VAPA POLR2H MORC2EIF3DCAMK1CUL4A TUBB HNRNPA3POLD1HDAC5 ASF1B PID1 GADD45G RPL8 STK24 RPS15A PA2G4 EIF6 GRASP PCNP CUL3PFDN5 SIRT1NR2F1PRDM4 PDCD10 AZI1 BTF3 Csnk1e PPP1CAHSPA4HSPA1A FBXO6CENPFPAFAH1B1 SOX2 FAM65B CDKN1BHLF ABI3BP SPARC PCBP2SDHBBCL7CPGM5UBD RUVBL2 PRPF19 KIF4A SIX3 LGALS3 ASPMCD81 SORBS2ASNA1EIF3B EIF2C2 TOR1AIP1STOM ING1KLF13 PTMA MED12 PTEN TRAF3IP1 VCP PHB RB1 DSTNDSN1RPS4X SUMO3 MAPRE1 SMARCC2 UHRF2 RANNCOR1 BAG1 ATP5J2RPS27L KCTD5 PARK2 PRKCSHPTBP1 SRRM2 SFPQ SMARCE1 DCHS1 MFAP4 CLN8 IGSF8 SMURF1 ACAT1 RPL17RPLP1 CUL7PSMD2DDX24CLIP3BIN1 PDIA4 TAOK2SMARCA4SRSF2DDX5 RNF2 UBL4ATRIP6 HSPA8SF3B4 BANF1 SMARCAD1FBL AURKB PLK1SRRM1 HSPA5 PTGDS ZNF787 FAM161B NID1 PRUNE2 PPME1C1QACNTN1 HDAC3 BIN2 MYL6 SLIT2 PUS1 ESR1 VDAC2LSAMP SGTA DYNC1H1C1QBPCDC37 DESAIMP2 EGR1EIF4A3 ACTN1 ZFPM2 BMP4 RARB CAT GABARAPL2 RPS26 AKT1S1NARFIKTUBA1C HDAC4 NXF1 TFAP4 DACT3 GRK5 Prpf8 HNRNPL PSMG3 STK3 RIOK2RPS27A RUVBL1DPYSL2RPS14INPPL1BUB1BRBMXWBP4 SF3A2 PRDM1CDC42 ZHX1 C1S NR4A3 RPS24 RTN4VDAC1 SSSCA1 VCAM1 UBA2 RPS21 TRA2B LMNB2 SNRPD1PRNP AR FBLN2 GPKOWDYNC1I2 CRYAB HJURPPCM1EEF1A1 PRDX3HNRPDLCOPS5OXSR1 NUP153LRP1TADA3 LAMP2 UCHL5 NCOA1NRIP1 ESRRA FZR1 POT1 TINF2 PHKG2PAFAH1B3 MYC SMARCC1 DDX17DDX21NUMA1CRY2 AXIN1 MED13 SPAG4 VDAC3JUP PIK3R2 FOXM1 LONP1EIF3GPINK1PRKCDBPCALD1HNRNPF TOP2AMAGOHUBE2OAP2A1 TOP2B HDAC2 HDAC1EHMT2 KIAA0494 STARD9 PPP2R5D YARS TARSUBE3APPP2R1A TCEB1HSP90AA1HNRNPM VIM SIRT7THOC2 TP53 APPIDH1LEF1 XPO5 ZC3H18KAT2BTTC1 PIP4K2A MYO1G UBAP2 BCAS2 RPS6KA1 SORBS1RPL13 ZNF687 CCNH ETV4 COL3A1 PSMD3QARS HNRNPA0ANKRD35 PIAS1 CHTF18 HDAC7 TCERG1 TGFBI MRPS27 TPM4 TUBB6YWHAQKPNB1 UBE2MSIN3A FOXO3 AATF ISL1 SF1 SERPINH1 PLD2PAXIP1 CDKN2A MIFMBD2 HSP90AB1AKT1UBQLN1 CDK9 CBX5 TUBA1BSYNCRIPPCNA ANP32ARABEP1 FN1 HSPB2 MRPL12NACC1 GSN GHRCUL5VCL U2AF1 U2AF2 CHAF1ACDC25CSRSF3ROCK1DACT1CDK7 SMAD2 BACH2 MYH10 TSC22D3NMISPTAN1 NUDT1 ILK PSMD11 ZEB2 TOP1 CSNK2A2FDPS NCOA3 PPP2R4 MPL AXLMRPS26SUMO4DPYSL3GBAS PRKDCBIRC5 PSMA3 PBKSUV39H1DDX3XPAN2 SMAD3 ARPC3USF2 SERPING1 GAS6 CTNNB1DAZAP1 ATAD3AFYCO1SNRPA SHC1 LMNB1ACTC1KLHL13 GAPDH EPB41L2 NR3C2ETS1 SOX4Htt WDR70 SP100 MED16 HSPB6 TOMM40 ISG15 RING1 CUL2 TUFM CDK2 PSMD8 SUMO2 SNRPA1 PER1HNRNPUL1 PCBP1 WDR48 MFF SNCA SETDB1SQSTM1 NONO NFE2L2RBM14 HDAC11 USHBP1 MYOZ3RPS6KA2 AXIN2 EIF2AK1 CCNE1STUB1 UBE2CTUBG1 COPS6MAD2L1TPT1 EIF4G1SF3B3EWSR1 EFTUD2AP2M1 ATXN1CDH1 CDK8NCOR2HIST1H1B NDUFA6 ILF3 CMBLCSNK2BMYH11AHCYL1 UPF1 CNN1 SRSF1ATN1 HNRNPC RARA BOC CLEC4F CCT4 TAOK1 PPP4C ANXA7 SUMO1HLA-DRB4 NEDD8CAND1WDR18 IQGAP1PLEC KANK2 PRC1CBX7WIPF1 LETM1 ICT1NR1H2RAE1 EEF1B2TNPO1 TNIK FKBP4 E2F1PMM2 NOTCH1 MED25 CUL1 ERBB3 TELO2 FASN HNRNPH1UNC119IMMT MCM7CEBPA THRAKPNA6 RPUSD2 FILIP1 MED31 PDLIM3 DPH2 RPS12 TPX2 KHSRP RBM10NR3C1 STIP1 EP300 DIXDC1 GMEB2 RAI2 IGFBP5 THBS1 CCT5SGK1FLNCKDM5B STRN4 NCL GANABSLC25A4ETF1SLC25A11 HNRNPA1LMNAPDE4DHNRNPA2B1LITAFRPA1 ZBTB16 PPARGC1A Sf3a1 ANXA5HEATR2 CORO2B GADD45GIP1DCUN1D1MTA2 YWHAZADRM1 TP53BP1TPD52L2 LGALS1MSL2 CSK XAB2 CCND1 LANCL1 MED17 MED14 DNM1L ATF4 CLIC1 SMAD7TLE1CARHSP1TPM1 MAP1SRASSF1 CSNK2A1 ESPL1 SREBF1 HDAC8 MED15 ALOX5AP AURKA METTL1LPP IQCB1KEAP1 ENO1 C6orf211APIP PTK2 ZFYVE9 IFT20 SFXN1 S100A9 UFD1L KLHDC3 PML NEDD4 CIRBPTP63 CBX3 EMD CDK1 CREBBPYY1 SETBOP1FLNATIMELESS GTF2E1E2F3BTK FANCBSVIL MRPL17 BAG3PFDN2 CSNK1A1RPL31 POLEUBE2E1 PIN1IL7R SKAP2 TRIM28 MITF ATXN7L2WDR83 ACTA2 CDC20SPRY1 ABL1HSPD1RPS7 STAT3TTK EIF3I HNRNPRFANCA ATM DUS3LNAA10 SIN3B NUP188 AP3S1 Mapk13 THOC4NCBP1PKM2 CD2AP PIAS4 SMU1 NFIX FNBP1 SMC1A ELANE TMEM14C ARPC4 TSPAN2 CPVL TIMM13 SMARCB1 TUBB3CDC5LYWHABRAD23B SRGAP3 SP1 LRIG3 EPN1 CCNA1PARP1 Pax6 SDHAKIF2C Ranbp9FOS ACTL6AKLHDC2 CBL RBL2MAPK1KIF24 Tsg101NPAT UBE2E3 GLI2 NDUFS4 SLC25A22C1QTNF7 TCP1 NKIRAS2 CYR61 SNRPN MBD3CLTC EDNRA CCNB1 PPP6R1 FANCL C14orf1PRKAR1A TK1TXNRD1RPL36ALRPA2 CCND2 SF3B1 PDGFBTHRAP3 GTF2H4 TXNDC12 PRDX4 ZNF259 ARRB1 RANGAP1 PTRF CHEK1TRIM55FHL1 KHDRBS2 CDC27PIP5K1ATUT1TACC3 MYOCSHBG ZNF480PUF60 KPNA2PPHLN1 SKP2 EIF2AK2STAT6HMGB3 PER3 SYNPO2 CRTAP PLAGL1 HSPE1 TSC22D1 MGMT BRCA1 KHDRBS1CDT1PSMA6NIF3L1 RHOADAZAP2CDK6 DLL1 PSMD13 POLA2 MLH1RBBP7FIBP STAT5BTNIP2NCOA7 MCM3 GADD45AKLF10GFI1 FKBP2 KCNE4 EBP PPP3CCECE2 ACVRL1MRPL2 CNN3CBX8 GPS1 CLEC3B MDC1FXYD6ATXN1L EEF2K STAT4 FBXO17 PLS3 ARPC2 SSFA2SAE1RELAJUN CHD3 CDCA5CSRP1RUNX1T1 C19orf40 EPHA3 PACSIN1 SUCLA2RAP1A CPSF1 BRCA2IGF2BP1 CREB5TRAF6 DECR2 CALM1TUBA1A TARBP2STAT1 TLR6 DNM2 TOM1L1 ADH1B IARS THOP1CLNS1ATGFBR2 NME1 LGMN CDKN1AFZD7 FHL2SRC SEPP1 CDC6 ASCC2 FAF2 ANXA1 GTF2F1 TMED10 ATRIP GAB3 RBL1 MED21 IGSF9 THBS2 MMP2 PSRC1 TENC1 BTRC RDBP SMAD4RXRA SNF8 PSMC3RBM39 GAS7 SERPINA3 TAGLN DNAJA1 FLAD1UBXN1Nhp2l1ADAM15 TGFB1I1 GGA2 LY9 RNF11 ASPSCR1 MYOT OSBPL10 MCM2 DNM1 VKORC1AIF1 RGS1 RUSC1 MRPS15CHMP4BTPRPTOV1SMG7HNRNPD BTBD1PRMT6EZH2GULP1CCNA2 GSK3B PCGF2 CXCL12CD2BP2 FANCG TRAF3IP3PIM1 NOP2 VPS4B DOCK2CDK4 FAM175ARORB FANCD2 UBE2I USF1 EED CPSF3 RRAGA Dctn1 BCL2L1 CHUK SAP30BP IKBKG GATAD2AITIH5 EPDR1 DCN STRA13 PDPN ITPR1TIMP2 CCDC90B RAF1 IL10RA ACVR1 PPP1CC CD247 RHOJ LIN9 NDUFB5 WNK1GABARAPL1CUTC POLR2AIKBKB CNKSR1RORA CYB5R3KBTBD10CAB39 CBX2 TRPC3 NIT2 FAF1 RABAC1 MCRS1PPP2CA HDAC6 ANKRD44RBPJ DTL CNOT4WAS FBXW11 SERPINF1 SERINC1 OLFML3ANKMY2 GRB2 SPI1 CLINT1BSGGNA11RP9MSH2POU2F1WDR12 VHL TNFRSF1A SH3KBP1 CHD4TPM2C16orf57 JAK1 LDB2 UBQLN4 CDC25A EFNA4 TALDO1 ZAK DUSP1 ECT2CAV2 JAK2EGFR PPP3CB SH3GL2 CBX4 PRKCI PDGFC SLC5A3 A2MCOL1A2INPP1 KPNA1Sirt2 OSGEPBARD1NKRF STAT5APPARG SLC25A3 AICDA RRASTBP GCN1L1 NBEA GPRASP2TGFBR1 CYBB PES1 BTG1FYN COL4A6 MRVI1 ATP5F1 PDCD6IPCPNE8 LAS1LDDB1ARVCFCCNF PRKAR2A MED7 WEE1 BCL2 COPS2 COPS4 HNRNPUF3 HSPA9 UBE2D2 CCDC120RBPMSGLIPR2 BLK MAPRE2TLE4 MNDA IGBP1 IRF8 TMEM44 ARHGAP9 HIF3APACSIN3 TBC1D10B TGFB3 MEN1 DDAH2PRR12 CUL4B TRIM33 CCL5 CCDC85B KCNIP3 SLC37A4CGNL1 LIG4DCTN6 ALG12PJA2DAXX LMBRD1NGDN SOCS3 MCM4 HCK SNX6 SPTBN2 ADAMTS1RBP1WRAP53 HDGF SNAI2 ZFP36 PLXNA1 DDR2 XRCC6 CRKL HOOK1 RUNX3TAL1 ITPA TRIP13 HGS COL6A1 TRPC1 MRPS18A MAGED1 TMEM11SH3BGRL TDP1 RRAD PTPN11MED1 NFKB1 CSF1R PTPRN2 CCL2 LAMC3 CAV1 NUP214 ITK NFKBIB TOMM20 HOMER3DVL2 WDR77 PDGFRAPPP1R16B PCGF5 VPS26B MED24 SLAUBE2D1 USP9X LCP2ROR2 NDUFS8 MAD2L2UHRF1 EDNRB ZNF212 UBE2UUSP7 NFIB PFKFB4 B2MMAPK8 NR4A1RBX1CNRIP1 UNG SKIL SMAD5 VAV1 C9orf100 PXN NCK1 FXR2 FASLG DARC CPE PAM PSME3 RBPMS2 COL14A1 CLN3 ATG7 ESD ZFP106 CGRRF1 PCYT1A TPM3TNS1 JAZF1 ROBO2 PRMT5 PDGFRBKLF9PCGF1SGCE MAPK6GRWD1 STAMBP SPTBN4 VDR CD2 TRPC6 LTBP4 SNW1 SNX2 ITM2A ABCB8 C19orf46 ZNF768 LCK MAP4K1 SERPINE2 TOR1A UBE2E2 DKK3 LEPR CCPG1 LRRC8EPEX10 GJA1 UBC STAMBPL1TSC2 STX8 SIRPAUBR1 UBE2N CD1D ATF6BWDR74TBC1D7 ODF2 PELP1 MBIP PIAS2 CREB1 SPNS1 MCOLN3 PLXNA3 PLEK SMUG1 TCF4KLBZNF205 RNF150ZAP70 SLAMF1 CCR2 TRPV2 CCL19 WLS UBLCP1 PIK3R1 DNMT1HIST1H2AB PDIA3 PSEN1 GNG12NAT9 COPS3GTF2IRD1 GRAP2 GIMAP1 PHC2TMUB1 ELK1 DOCK11 ERBB2PPP1R16A GSTM5 FYB BPNT1PTP4A1 DOHH RHBDD3PXK DSE SKP1 IL6ST EPS15 DDA1 PELI2 LOC401010 DSCR3 CHN1COL15A1 UBE2D3 ZNF213PLSCR4 RNF38 HOXA9 SOCS1 GNPDA2 SDAD1 MTA1ZNF25 ZNF646 LAT ZMAT3 APCTMX4STYXL1 ALDH1A3BRP44L DPYDMXRA7 GTSE1 LYN CD53 CLN6 C21orf59 RCBTB2 APBB2IRS1 CD48 TRIM11 PGR ZNF799AP1S2 C14orf21ABCB1MID2 STAM2 VPS4A PDX1 FAM134CRNF145SNX19 SLC6A8 KIF2A CD6 MEIS1 TRIM22 CYTIP GTF2H5LOC729852 TMEM180 MAPK3 TRAF2 CD5 PPP1R14B DNAJC5 ZNF317 EXOSC4 LIFR LTBP3 VPS29 MTX1RCCD1ATAD1CHST15 ZNF777 C19orf53FMNL2 STK17BSSNA1RNF169 ZBTB4 NAV3 AUH ERGIC3 C9orf5 DAG1 DHCR7 SYPL1 ZFP14 SNX11 TRADD NDN GPR183 PPP2R3C FBXO8 HIST2H2BE ACTN4 MDFI TYROBP SRM POP5 C10orf32 SPARCL1 FLOT1 TTYH3 ARMCX1COMMD6ZC3H3ZNF324BNEDD4L PARVG LAX1 USP11 PTPN6 CLN5 SNX5 ATP8B2 NMRAL1 MARVELD3ANTXR2ACOT1EEPD1 IGJ VEGFA ZSWIM1 KIAA0247COL6A2SLC25A25C10orf12 SLC7A2 ATP6V1B1 CD86 FLVCR1 TMEM223 FAM46C CDCA3 TNF ALKBH2 CYTH4 SLC35C2 FBXL6 ERICH1IER5LNAPG SDPRKIAA0922 ACTB NRG1 HIC1 RPUSD1 LIPA PSTK CTGF DENND5B RBM24 TMEM39BHLA-F DOK6 GLT8D2 PIWIL4DNAJC17 TIGD5PDK4 HIST3H3 C7orf58 SCARF2 GFOD2EGR2PDDC1 PDGFD EFR3A ZNF629 LRRC33 TNC APOA1BP DOCK10 C8orf4 CBX1 REEP3 TMEM66 STX2PPAP2B TNFRSF19 TIPARPTBRG4C12orf48 HLA-DPA1 C1orf190 HCST PPAP2A MOXD1 LAYN TNFSF12 C16orf13MPDU1 RPP21 TMEM131 EPHX2 GINS1PODXL2 SIPA1L3 NSMCE4A TNFRSF13B ZNF282 LAMA2 CLIP4 HIC2 ALDH1A1 NAA40 CYBRD1 ZNF713 PRR16 ADCY9 HDAC9 MPST ZNF821 KLRK1 RPP40

PKN1 FMNL1

ALDH2

TMEM189

RPL13A

SEC22B

NXN PTPN12 SCARA3

USP3 THAP2 XIAP

(A) CPPIN for early stage bladder cancer

Figure 2: Continued. 8 BioMed Research International

CENPF COL5A1 EFNA4 RUNX3

TOP3B SPARC EPHA3 TLE1

FBLN5 COL3A1 EFNA3 HES6

GFI1B

ELANE

SERPING1

C1R

C1S

NID1

RPP21 C1QA FBLN2 NUDT18 CD1D CD86 LTBP4 USP3 QARS

RPP40 DAG1 TBRG4 PDDC1 FMNL2 COL14A1COL6A1 TAOK2 ECE2 LEPREL1TIPARP RPL13A GNALPDE2ASDPR B2M ATN1 ALDH1A1 TAOK1 C9orf5 DHCR7 CKAP2L SLC39A4 ADCY9 C9orf100TIGD5 MARVELD3 DPYD ASL NXNARVCF THBS1 CCPG1 PPDPF NSMCE4A ZNF713 POLR3GL B3GALT6LOC729852 ARID2 ZDHHC9PSTK PPAP2B IER5LABCB8 MYL6 CASK UBLCP1EEPD1ZNF213 LMBRD1 Psma2 LAMA2 ALDH1A3CYP1B1LAYN LEPREL2RIC3 SPTAN1 RPL29 TMEM14C STYXL1STX2 LRP6 TIMM17B LRRC8E ACTR3 GINS1 VPS26B SLC25A39ZNF200 LOC401010 WRAP53PPP1R14B DCNNDUFA6 CGNL1DSE SDAD1 ZFP14 NAV3 PRDX2 PTH1R NMRAL1 MID2 FAM129A TPM4 RGS9 CADM3ZSWIM1KIAA0247SLC35C2ITGA8 SPARCL1SLC7A2PRICKLE1 THBS2STXBP6ZNF606 ACOT1 TFAP4 CCDC71COL6A2DOHH HOXA13SERPINB6 MXRA7 ALDH2 NDN MAGED1 ZCCHC24 CLIP4 SNX11 PRICKLE2 TRIM22 GFOD2ZNF317 MAPRE2 BPNT1 AP1S2 RHBDD3 ALG12NAT14TMEM39BADPRHL2RNF169COL15A1DECR2 ADCY4SCARF2 TMX4ZNF799 GPR183CLPTM1 CFD PRKCDBP CPVL ZNF282 SLC25A25C16orf13C21orf63 C10orf32TMEM100NFIBTNPO1 NSUN2 GINS2 ZC3H3 ZNF25 RBM24 SNX19C19orf53 CLDN5NGDN ACAP1ZNF35 GBAS DDI2 METTL7BC8orf4HLA-DPA1 SLC19A1 RNF112 CPNE8 RNASEH2AF3 DES S100A9 FBXL6C17orf53RCCD1ATP8B2 ANTXR2 APC ZNF259 SMG7EFR3A ADRB2ATP5J2 SFXN1 HSPA5 NR1H2 VKORC1SLC25A3 ALG3SPP1WDR48 RNF145 PVR ITM2A TMEM66 TMEM131 KBTBD10TMEM106CPPP2R3C TMUB1ZFHX4ALKBH2 C7orf58 TELO2 CFTR ESM1 DPH2 METTL1 LIN9 SLIT2RAD54L ARMCX1AUH HAAO SNX5 PLSCR4SSNA1CHST15 FLVCR1SLC38A10ZNF629DDX49ZNF536 GLT8D2 RASL12 MED16 HIST1H3D DNAJC17 ZBTB39 DENND5BSTIL ENPP2 USF2 RGS1 PPAP2ALAMA3MMP15MMP2 NAA40 PRDM4 PDLIM3 GATC TIMP2SMARCE1 WLS ATAD1NAPG SCARA3NCDN KRT8 BRD7HIST1H3F ACVRL1 MPL CNOT10 ERGIC3EFEMP1 IFFO1 CDC6 TSPAN7 FOSB UBE2J2 CBX5 RPUSD1 C15orf42 BRP44L PUS1NFIX GINS4 CHD7 CAB39 TMEM223 CHMP4B CTGF FBXW11TMEM54 GCN1L1 ZNF324B HOOK1 MCM4 SNORD55LPP CMBLITGAL UBCYPEL5GNPDA2 CCDC43VPS33ARAC3 HSPB2 HCST GNA11 EIF2C4 ISOC2 PODXL2 IL7 GLI2 CORIN CORO2B DKK1 NT5DC2 PDK4 REEP3 CREB1XIAP HNRNPUL1 SAE1 VNN2 PPP2R2B C16orf89 FAM134C ADAMTS1 VCP PDX1 AGPAT9 PIWIL4GAS1 CGRRF1LYN PEX10 BRCA2 STRA13 CYGB SKA2 Prkca DUSP1 E2F1 YARS KIAA0430 CERK MTX1 CTSKEXOSC4NKX3-1 MTOR SMAD7 HCN3 CDCA4SARS RDBP TMEM180LSR PPP2R4KIF20A CAP2 UBE2D1 PLEKHH2 BTG1NELL2 TRIM24 OSGEP CD53 TSC22D1 DCHS1 RASGRP2 KPNA1PSMG3 NIF3L1 EBP HEATR2NDUFB5 LIFR LIPA TIMM13 NARFBCL7C ISCU MRPL2 ZNF787 KLRK1 SDC2PRKCBPPFIA3 SPTBN2EPAS1 MTA3 HDAC7 APH1B BAG3 SAMD4B ATP1A2 SOX2 RUSC1 TROAPGNB2L1 NAT9 CLN3 TGFB3 ERBB3 CD6 ZNF707 GULP1 ARID1A ATP5F1IQGAP3 PJA2 RRAGA KCTD12BRI3BP ATM FANCB ZDHHC14APOA1BP MAD2L1 ZSCAN2 CDT1 MED25 FUT4 HIF3A CLNS1ATPD52L2 SNX1 PSEN1 UBE2D3 NACC1 FXYD6 CRTAPGPT2 PKM2 ADCY5RBPJ CD48 CD2 SKAP2ITGB2 TPT1KLHL13UNC119 ISG15DTL NCOA7 RBPMS2 TGFBR2 SLC35E2SLC25A22 SKA1FAM189A2 ZBTB20 TAGLNNBEA LMNB2 C6orf211LONP1 EWSR1 CYTIP LEPR C14orf169 FBXO8ATP13A2LRCH2 KIAA0494 MACROD2 RAN INPP1ODF2PRKDCFANCAAXIN2 FANCL PPP1R16B TENC1 UBE2U CATSPER1 CDC42SE2 RBP1 SRPX DDR2 PPP1R2CNOT4TDP1 CYB5R3SPI1 FBXO6 PGR COL4A6 LDB2 C21orf59 POLE FGFR4EED POT1ERICH1 COPS2TBP TOP2A STAT5AB4GALT2ANKRD44 EME2 PTK2B KRTCAP2DDB1 PAN2CNN3ZNHIT2 FKBP4 UBD EDNRB CYR61Ndc80 ACACBZNF668EZRZNF687 HMOX2SYNM IRF8 HDAC1 ITIH5FANCG GRASP PDGFC MAOB CSRP1 SMUG1MAPRE1ELAVL1 STAMBP KIF2A CCNT1 KAT2A PDGFRA GAS6SNX6 TOMM20DYNC1H1 RNF150 IRS1 SIRPA CRYAB KLF9 ISL1 CUTC POF1BMAPK8BIRC5 CNRIP1RPL23 CD5MAGEA11 MAP1SGNG7 ASPM APPZFP36MRPS18ASERINC1 ALG8 CRYM C7 TRIM11TBX1 DOCK11 RANGAP1KCNIP3RRASMED1 CCNA1 TLE4 PRDX1 APLP1 GLIPR2 DCUN1D1PPFIA4SFN STK17B TINF2 WNT2BMED24SOCS1 DAB2 MDFI HLA-DPB1 ARID3BCPSF3 CNOT3 SKA3PTP4A1SHMT2UBE2NLIG4 HDAC9 MYH10 TNS1 PPP6R1 C19orf40TMEM44 AXL HSPB6 PSMC3 PPP3CC Sf3a1 TBC1D10BFANCD2 MBD2 CDKN1A PFKFB4SNAI2 ZHX1 LCKDSCR3 FYN SLAMF1 TERF2PTOV1TNC GHR A2MPFDN6 HIC2 EIF2AK1 TRPC6 KIF14 NUSAP1AURKBEEF2K GSG2 C16orf59TOP2BMSH2 GTF2IRD1 PID1 ITCH KCMF1GAB3TTC1 CELF2 ARHGAP20RPS18 CUL3 IL18RAP ANGNFKB1 SMARCC1 RPL23A DBH CLU Pax6 PTP4A3 GTSE1SLC37A4HCFC1NDUFS8RAB11FIP4 NR3C2SKP2 GANABGFI1 SP1 C19orf46 COMMD6CLN5 P2RX7 PLEC ESR1VHL RAE1 PRKCSH RELA VPS29 UBAP2 PIAS1 NDUFS4 PHB2 C14orf21 CIT POLR2H PPP1CB ASNA1RPAP3SSFA2 ANKRD5NR2F1 PFDN2 IDH1 TGFBR1PDZRN3MMS19MLF1IPHSPA4 ANXA5 WIBGBMPR2ROR2 CSNK2A2OSBPL10STAT5B RWDD3 BTF3CAPZB LANCL1 HOMER3 TRPC3 CTU1 PPP1R12B FLOT1 JAK2 TSHZ3 PFDN1 TP53RKCDCA5 GSK3B C14orf1 HIC1 RBBP5 GLI1 SNX2 MAPK8IP2 UBE2C SERPINE2RTN4 STUB1 GTF2F1 KLF10 BRCA1 NUP153 MEN1 CDC37 PYGO2 NCOA2 LRP1 DDOST SP100 ZBTB4PTPN11 WDR18 PUSL1 RGS2 SIN3A RPS16 CHEK1 SLC9A3R2 LGMN PDGFRB MITF ZAP70 DSN1 Sirt2SF3B1 PTPN6 COL1A2 UBE2O SUMO2DLL1 TSPYL1 TARBP2AXIN1 RPS19BP1 PYHIN1CD2BP2 ASH2L TRPC1 SLA GSTK1CCDC90B AATF UNGTP73 ZBTB16XPO5ING1BUB1BLMNAGIT1SORBS2 MPST ME2JAK1PTENNCL CCDC80TCEB1 GADD45AHIST2H3CRBBP4 ARHGAP9CC2D1A MPDU1ATXN7L2 MYC PRKCD RCBTB2CHTF18EIF3I IL10RA TIMELESSUTP14A SPNS1 TNFRSF1A ITPR1 TOMM40 SMU1 ETS1 PALLD MED13 LMNB1 PPP5C ESR2 USF1 FASLG PLAT CAV1SPTBN4PPFIA2SLAMF6 PRDX6 PXNNR3C1 SDHA CRY2 STAT3 SAP18 ZNF205 UBE2D2PPP1R12ATOR1AIP1MAP2K3 SYNPO2PELP1 ECD TPX2 CPSF1 BCL2RPL36AL Ranbp9EGR1 SETD1A ICAM1MAPK6 DPCD DPYSL2SMARCC2PLK1GPS1PES1 RBL2YWHAHSOCS3 CDK2SIRT7 RASSF1CLN8CAND1PSMA3NCOR1 BRD4ATRIPSH3BGRL PPP2CB SYTL3 MRVI1 GTF2E1 TMEM11 PRC1 DNAJB1 AGMAT KCTD5EIF3G GIMAP4 SLC25A4 SIX3 SH3GL2 TYROBP ROBO2 LNX1 SMURF1LAS1L IL6STPLCG1 ATG7TUBG1 SPAG5PML SNF8SHBGTRIP13 SNCA ENC1 ACTN1DNMT3BCRK NCSTN CCNE1NR4A1RAF1 IKBKG TRAF6 MDM2 DLG2 LAMC3 PIN1 CRMP1 TSR1 GNL2 EDNRA MFAP4 MGMT TSPAN2 IGSF9 FAM64A CDAN1PKN1 PSMD13 YWHAB ANXA7POLR2A RAB27A TSC22D3 TCF4 SGK1 MPG ADAM15LRRC3B WDR12 AP3S1DDAH2 FDPS SMAD3 PDPK1NCBP1TOR1A TNFTUT1 CHUK EIF2AK2E2F3RPL7A RPS6 TCF3 ASCC2 DENND2AMYOZ3C18orf56 VDAC3 BIN1 HDAC8 AKT1S1 CAMK1TP53PLAGL1 RBM14ANP32A UBR1 HLF TRIM33 KIF4A TRADDMAPT FAM175A FHL1 PCM1 ANKMY2 CHEK2RCC2 FBLHIF1A PHKG2C1QBPRPS10 CEBPB HDAC3 NLGN1 CD247 PIK3R2 STAM2 RABEP1RIOK2 CLIP3 CUL4B SOX4 GRWD1 IGBP1PRDX4GTF2H4 PPHLN1 FOXP2 Dlg4FLNCFNBP1 BIN2 TXNDC12CBX2VAV1NUMA1WEE1 ZFPM2MDC1VAMP2 PRKAR1ASIRT5 TPM3POLR1CICT1 HIST1H2AM STAMBPL1SMARCA2 TFIP11 PRNP SUV39H1 EIF6 FAM50BNRIP1 CREB5 SERPINH1 SLMAP CAV2 KIAA1967 HSPA9 CHD3PCNA NOTCH1DAZAP1ZYX KHSRPSUMO3KIAA1377MSL2CNOT6 CTBP1 NTNG2 ESD PPP1CATMEM189 FERMT2 RPS19TPR SMARCD1 ZEB1SIRT1 FOS PCBP2SMARCB1 STAB1 RPUSD2 MME ACTL6A CD4 STAT4TUBB SNRPACUL7EPN1SRCHSPB1KAT5TUBA1CATF2 VIM RUNX1T1 VDR SOBP HHIP DNAJA1 ITSN1IGSF8 FN1 NFATC4CCNA2LRRK2 MLL5 PRDX3NKRF EEF1B2SUMO4EEF1A1 LDOC1 SMARCA4MST4PPP2R5D PBK HJURP CENPA CCDC120 TERF1 Htt CDKN1B SIPA1L3RABAC1 CIRBP CHAF1AFIBP NONO Tsg101ECT2 SSSCA1 RAD21HSPA8ARRB1 SRSF5 EIF3HSTRN4P4HBRPS7 CCT4 PTGIS DNM1 HDAC11 PLD2 SRSF11CPE WNK1PPP3CBCALD1 CDC20ZAK KAT2BMEPCEBOP1ANXA1POU2F1ACAT1UBE2D4PIK3R1JUN ANKRD35RPL27A PTGS1 PAMLEF1 CDK7CNTN1MYOCDNM1L LAMP2 CSE1L GRB2CDK8 DCTN6 HTRA2CALR STK24 GGA2 OLFML3RNF38 POLA2 ACTN4 UBE2MPLA2G4A EIF1BHNRNPA2B1GADD45GIP1ACTA2ERAL1 OTUB1RPL14 CDK1RECQL4FAM107AAPIP CYP27A1 HIST1H2AB CBX3 PCP4 CALM1ACTBSEC22B MAGOHRB1 XAB2 CDK6 TPM1KLHDC2TMED10YWHAQ HDGF EIF3L RPS27ATACC3 UBE2E2 MTA1 PIM1 CNTN2PACSIN3 ITK PAFAH1B3 RUVBL2JAZF1 COPS6 SNRPD1RPL11CCNH STK25 ERBB2 FYB TXNRD1PDE4DEIF4A3 MIF PIAS4TUBA1B CREBBPHNRNPA1SAP30BPMED14 RPS11 SRRM2 HTR2B CSNK1A1 PTRF ENO1 RBL1IKZF2 HNRNPDMRPL12 Dctn1 Dctn2 STRAP PRKACB DDA1TP53BP1 FAM122ANCK1 MAD2L2 ZFP106STK3SF3A3EFTUD2 NIT2VDAC1 COPS4 ARNTLRPS8 RPS14 SRGAP3 PEX14 TUBA1A HLA-DRB3TERF2IP RNPS1 DDX3X NOP2CUL1 AURKA RPS3 RPL6 MED17 RPA1 PELI2MYOT LCP2 NUP188GJA1 CNN1 AKT1 AP2A1MED7XRCC6SF3B3 WDR83CASP1LAX1RBMX CTNNB1 MYH11 MOXD1 CHN1 SETDB1 PAXIP1ABL1 UHRF1 PRPF19CUL5 NEDD8 ATAD3A ADRBK1 KIF24 ZFYVE9 ARPC4 BARD1Gnb2 RPS24 UBE2ICSF1RCDKN2ASMARCAD1CDC25CTRIM28EMD EIF3D REM1EP300 UBN1RPS6KA2 PPP2R1A MCM3 DPYSL3 MGAT1 PLXNA1 UCHL5SDHB HSP90AB1ILF3 MFF TRAF3IP3 FBXO25MRPL46 THRAP3 VPS4B MCRS1 IL16 EHMT2ALDOAEPDR1 CLTC MRPL17RPS13 RPS23 FAURPL22 IK DAZAP2 CDCA3 PTK2 SKP1 HDAC2ASF1BKPNB1PRKAA1CUL2CLINT1 BCL2L1 PIK3CD DISC1 KHDRBS1 ELK1SERPINF1 DNMT1 FLNA TUBB6EZH2DNAJC5CBLEIF2C2 SF3B4 OXSR1 RPS28 DUS3L AZI1 SNRPNSLC25A11MLH1 RPL5 ACTC1 GABARAPL1RPL19 RPLP1 ESPL1 RPL30AIMP2 Csnk1e RNF123 GSNSRSF1CSNK2A1U2AF2 UPF1HNRNPF SUCLA2 IKZF1EIF3F TNIP2 STAT6THRAMCM7 PDCD6IP KLHDC3WDR74 ARPC3 PPP1R16A SCRIB COPS3 MAPK14LETM1 TADA3HDAC5 RPS9ZNF212 TAB1 NFE2L2RPSASNRPD2RPS12 CDK5 LTBP3 SPAG4 GPRASP2 DVL2MYL9 SHC1 APBB1 C2orf44 YEATS2COPS5EIF3A MEF2C CALCOCO2 EIF3E RAB7A WAS TRDN DDX24 UBQLN4DAXX PPP6R3TCERG1 G3BP1 MRPS15 WDR5 F8 PRR16 HGSBCAS2SNRNP200 FLAD1PITX2 NOP56UBR4 GABARAPL2KDM1A SMC1A MED6 IGFBP6 CXCL12 GATAD2APINK1 MRPS27AP2M1HDAC4VCAM1CUL4ARARB RPS15A MEIS1 RPL4 UBL4ARPL15 NCOR2NRG1MNAT1 JUP SIM2 KIF2C ATF6BSF3A2 RYKFBXO17 HNRNPH1 RPL37A STX8 RBM10 DOCK2 DNM2MBIP SF3B2 ACTG2 TADA2ARPS4XCCNB1 TCP1 KCNE4 CLIC1 USP7 TTK SRSF2CHD4 PSMC5 HNRNPLEBF1 FAM46C TP63 RNF11 LITAF TUFM ITPAPPARGC1APDIA4KPNA6 RPL10ASQSTM1 RPS27PPP4CRPS26RPL9UBE2E3RPS25 ACVR1 PROS1 FAM110B CCND2 WBP4 SUMO1MBP TPM2 HNRNPA0MAP1LC3APICALM KEAP1CDH11 SEPP1 CCDC85BGREM2 DKK3 CDC5L IQGAP1NFIA GAPDHILF2ITGA4 CCNF SGTA EGFR DOK6 ASF1A SYNE1 SF3B14 ILK HMGB3MSN SORBS1 MCM2 MRPS26HNRNPKCSNK2B RPL17 AHCYL1 RPL13 ALOX5AP PRDM1RBBP7 FXR2 SLC5A3 NME1 HSP90AA1STOM DSTNPUF60 SNRNP35CCT5RPLP0PSMA6 RPS17IPO5 BACH2 GPKOW FZD7 SFPQ PAK1RBM4SAFB CAT CA9 TBC1D16 ATP6V1B1 F10 ARPC2 ATXN1EPB41L2 GRAP2FASNMapk13 PRKCI RPL7MAP4K1 PABPC1 SAMD3SMN1 IARS PRMT5 ZNF768 BTBD1PPIL1 CASP8 HDAC6 SMAD2PA2G4 PTGDS THOP1 PPP1CCSCAF1BTK WDR77RUVBL1 CDK9 SNRPA1RAD23A UBAP2LDHX15RXRAIMMT RPS21 PPME1 C1orf190MED31 PCBP1 PTBP1 Nhp2l1 EIF3K NBR1NCAPG RPL12 RPS27L PER1 LGALS1 SYK VCL MCM6HNRNPMPTMAVAPARPS6KA1RAD23B PSMD8 ZNF480 ACTR2 PTPRN2RAD50CDK4MED12 TRA2B DDX5 PAK4 U2AF1POLD1 GTPBP2 KDM5B CCL2 HSPE1 NUDT1 VPS4A TRRAP ACD EEF2 DDX17CD2AP RPS5 FOXM1 TNNT1 PAFAH1B1 RRAD YWHAGSMAD9IQCB1ZC3H18SRSF9 ARRB2 CBX8 EPS15 SH3KBP1CAPN1RPL10L KLHDC1 Prpf8 HCK SRRM1 LATPCYT1A CACNA1A HNRNPR MBD3 SRGN PSMC6 HOXA1 C11orf84 DACT1SVIL KPNA2HLA-DRA PPARGYWHAZ FYCO1 PSMD3 TBC1D7 SRSF7 SMAD4CCND1TK1 UBXN7KHDRBS2RPA3 IKBKB ETF1 EIF4G1 EGR2 TRAF2 PARK2 CDH1 MAPK3ADRM1 PSRC1 MEX3A DARC PER3 TNIK RORA SYNCRIP FILIP1CYBB PCGF1 TUBB2B CEBPA BOCEIF3B PARP1RPS20 KRT19 NR4A3 KATNB1 VEGFA PCNP FKBP2 PHKB HNRPDLPCGF5ANGPT2TRIM25 SRMNEDD4 PLS3 WIPF1 UBQLN1 INPPL1 KLF13 PDCD10 GTF2I GSTCD EDIL3 BSG RAVER1 ASNSNAA10FLNB CD81GADD45G C6orf136 HIST1H2BOPDE4B STARD9 USP39 LIMK1 GNG10 SRSF3 MTA2 TOP1 NCOA3HMGA1 UFD1L CCL5 SGCE GAS7 TSHZ1 C12orf68RBPMS SET HIST3H3 GMEB2 AR MORC2 FZR1 AQP1 NDEL1 LIMS2UHRF2C16orf57 MAPK7 ROCK1 GABARAP CASKIN2 GSTM5 SH3BP5L HLA-DRB4 PHBFAM13B CDC25A FHL2 HIST1H1BDYNLL1 RP9 NINL CLN6 STK32C DDX21PPP2CA AIF1 PSME3 ETV4 HNRNPUSPRY1 NFKBIB ZNF821 R3HDM2 CARHSP1 TSTA3 PAPPA DIXDC1 HOXA9 TTYH3 MYO1F PSMD2 PRKACA CBX7 NPATCCNDBP1 MYH9 TRAF3IP1 CDC27 WDR70 CDC42 THOC4 RBX1 SREBF1 OTUB2 ASPSCR1 TALDO1 MYO1G SETBP1 UBE3ACAPNS1 PKIG ACTG1 FAM127B ZEB2 BLKDYNC1I2PCGF2 TNFRSF13BRAI2 NMITGFB1I1 DACT3 ZNF646 HLA-DMBRHOJ THOC5 CLEC3B IL7R CTSG GZMM FAF2 TNFRSF19 GZMK PRR12 ATP5B IGF2BP1 TLR6 PGM5 NKIRAS2 RBCK1 ATXN1L TNFSF12EDAR FCER1A PLG PLXNA3 OLFM1 NTRK3 DACH1 TUBB3 KANK2 RPL31 PACSIN1 HNRNPC USP9X FAF1 THOC2 YY1 EMILIN1 EFEMP2 CNKSR1SMAD5APBB2 ESRRA RHOAPSMD11 DHX9 ZNF521 PRUNE2 HSPA1A CFL1 PHC2 ITGB1 TSPAN4 IGFBP5 BMI1 NCOA1 E2F2 PCDH7 VDAC2 GNB1 TRIP6 SCYL1 CDC23 STIP1 MED15 ABCB1 UTRN GNG12 ZMAT3 CSK NTF3 SMAD1 HOXA11 GRK5

ITGA10

RAP1A ABI3BP PIP4K2A

IFT20 JAM2 TCEAL2 NC PPI B ADH1B GATA5 PTPN12 JAM3 TCEAL4

THAP2 TJP1 USP11 NC PPI A SERPINA3 KLF2

(B) NPPIN for early stage bladder cancer

(a)

Figure 2: Continued. BioMed Research International 9

POU2F2

TMEM188ECM1

GADD45G COPS6 RPS23 SP1

GNG10 SUMO2 NCOA6 STAT3 RPL7

UBIAD1

MRPL54

NUBP2

RAN

CRK

ST6GALNAC2

MAGEA3

MPP2 UBR5

SLC39A10 TIMM9

Pax3 MRPS22 SAAL1

MPHOSPH9 EPS8

SIRT7 MAGOH CDK5RAP2 ACTB TRAF2 DCLRE1B MPHOSPH10RPL14 SEC23B AP3S1 SPG21 SRSF11 HIST1H2BL RPL10A PTPRU HIST2H2AC CUL4B HNRNPA2B1 RPS4X WASH1 SMAD3 COPS4 QARS U2AF1 SETDB1 VAV3 RB1 TBL3 ARHGEF11 RPS27 HRASLS ZMAT3 APEX2 C1QA YTHDF3 GALT MIA2 CSTF2 COX4I1EFNB1GRB2 FLNA UTP14A SYNCRIP CDC5L USP7 CARD8 GFPT2 TRPT1 HDGF UBE2W ECT2 TUBG1 PPP1R14B RPS19 TERF2IP CDKN1A TMEM159 LOC401010 RANBP2 PLXDC1 KRTCAP2 HNRNPUPES1 RPS28 SQSTM1 UCHL5 UBE2E3 RPL22 FCHO1 CENPA U2AF2 WDR53 MBTD1 EEF1A1 CCNE2 SART1 DAGLB PRKCD LMNA SERPINE2 CREBBP CDK4 SNCA ELF4 SNRPB ZXDB RASL11A MAPK14 HIST1H4A RPS27A RPL5 CSNK2A1 GSK3B PRKDC SSSCA1 RAD54L TRPA1 MBTPS1 PTBP1 LRRC58 EBP IL10RA ARRB2 RPS27L ARIH2 L3MBTL3 SNRPD3 SLC35E2 RPL7A PSMD2 FANCI VHL CSNK2BLBR RPS21 ATP9B C2CD2 EFEMP2 GTF2E1YWHAG ZNF259 ERP27 FAM86B1 TUBB3 CBX7 RBBP4 RPS7 HNRNPA0 PPP2R1A TMEM120A CCT3 TTC8 PARP6 STMN1 STK35 B4GALT5 KPNA2 UBE2K SRSF2 ISG15 MYLIP SOX2 YWHAQ STAU1 HNRNPA1 RPS17 SF3A2 RIPK1 SCARF2 ZNF254 CEBPA HNRNPR SFPQ Prpf8 RPS14 HDAC3 KIF7 PCNA TUBA1C NUCB2 AQP3 RPL19 NOTCH1 BRCA1 SIKE1 RUVBL1 POT1 VCAM1 ZYG11B HNRNPC ZNF302 FILIP1 SARS2 PRKD2 RAD23B CDK9 MSH4 NAP1L1 KIAA1683 RIOK2 ORAI2 TSKS MYLK2IGBP1 DAG1 SIRT1 KBTBD6 BACH2 RGS5 POLD1 MT1M POGZ MCM7 ZNF428 SPP1 CRTC2 RPS12 DNAJC10 GLIPR2 RANGAP1 PLAU PDE12 MCM4 NACC1 MED6 KRT9 POLR2C PBK DDB1 RPS15A SYTL2 EXOC6 ZNF24 LEPREL1 SRRM2 E2F6 TNFRSF1A ARHGAP8 CDT1 KHNYNSNRPAPABPC1ZNF251 HDAC5HDAC6BIRC2 PSMC1 RPS5 PHKG2 SRSF1 LRCH4 MDH1 EIF4B Cdk1 KLC1 SLC22A17SNRPD2 DYNC1H1 ASF1A KDM6A CUL5 ARMCX3 CTNNB1 CHKA ILF3 RPL27 ILK CASP8 RPAP3 TERF2 RPA1 VPS13D SKAP2 LRRK2 SMAD4 ITGA4 CUL3 CIB1 PSMA3 SELV CACNA1A MRE11AGMIP TSR1 NEK3ATXN1 GBA2 RPL11 ACTN2 SNRNP200 ZBTB7A SMARCA2 EIF2AK2 TK1 MLL4 HN1 HIST1H1B DRG1 LIN37 EZH2 SMPD4 FAM131B MVD BRF2 RFC2 UBE2E2 TPM4 REPS2 DDX1 NUP93 UBQLN4 FAM172A BASP1 SAR1A CBX5 ATAD1 VIM KLC2 RAE1 ZNF461 CCT4 ITGB2 SNX19 ABCB6 ZNF418 HDAC2 IRS4 DTX3 CAV1 KDM2B MAP3K10 HIST1H1A ZZZ3 SULT1A1 CNOT7 UNC119 LRBA DAZ2 Eps15 BRPF3SF3A1 ATRIP CSNK1A1 COPS5 PTPN13 PRDM1 CHD1 HIST1H2BJ ATP11B FBXO44 DISC1 FHOD1 NOS2 RGL1 SH3D19 RPS6 EPO WDR91 PSMD3 EFTUD2 PAXIP1 MEX3C SNRPN PSMA2 NSFL1C BARD1 MLEC POP4 SNW1 SH3KBP1 RPL23 KCNMB1 BNC2 AP1G1 PPIP5K1 DYNLRB1 ACTN1 HMGCR DUSP5 PAX8 SFN UBE2U RPL12 MYOT PCBD2 PTP4A3 CHUK MYH9 DCUN1D1 SIRT4 MAPK1 CDC42ALB PIAS2 SSBP1 RPL17 CDK6 XRCC6CLK1 ATP2A2 UBE2D2 FANCD2 NRF1 TULP2 PRDM2 HNRNPK NEDD8 SMC1A SLC35A3 NIF3L1 IGSF1 DYNLL1 UBE2V1 RFWD2 SHMT2

NUP153 TMED2 APP TP53RK TSHZ2 FAM174B MRPS18C POLR2J ASNA1 ZFPM1 RASD2 TSEN54 ECD TRIP11 FAM116A CUL4A KDELC2 CSNK2A2

RPL9 GPR126 HIBCH HNRNPH1 HSPA8 LIG4 MAGEL2 RPL30 CLTCMLH1 HNRNPUL1 RPL31 HSP90AA1 SMC6 NCL PML DNM1 ENY2 COX6C AIDAAHDC1 TBP HES4 DNAJA1

TNFAIP1

MEX3A

CD81

TRIM27

FBXO6 ASB13 TUBB6 SAMD1HNRNPMCECR5 BMI1 KCNA4 IQSEC2SRRM1RNF151Htt FN3KRPEP300CORO1BCABLES1 ZMYM3 EIF4A3 RNF138 NHP2L1 TRPC6 ZBTB25 SHH PPP1CC RPLP2SYT17 PSMC6 CASK ZER1 MAPK6DHX15CUL2 MFN2 IKBKB FBXL5 CDY2A CBL GPATCH1 ZNF804A PRICKLE3 EHD1 CCM2 LGALS3BPSLC23A2 HSD3B7 RBP1 CPSF1 LMBRD1 HSPB11 RAD21 MAPK9HSPA6 TGOLN2 RPS3 PRKCZ RPF1 PCYT1A ESR2 METTL7A UTRN XRCC2RPL15S100A1PRKRIRTNPO2VBP1UBE2D4TUBB ZMYM6 SCYL3 COPS2 ACTR3 Dlg4 PURGPEX19 LZTR1 WDR77 UBC SRSF3 TRIM32 RUVBL2 PDHX BCKDHB CUL1 AAGAB RBM39 Tceb1 GZMB NUMBL PDCD6IP KHDRBS1 ZFYVE16 ERF ZC3H18 GOLPH3L RHOA ATN1ALOX5DCD CCT8ELK1ACTR5ASB12 SKP1 UBE2D3 Rbx1 SECTM1 SMARCA4 RHEB RRP7A HNRNPH3

PSMD1 PFKFB4 S1PR1 YWHAH

SAFB SEC24D AHNAK2 PKM2 FIBP SMARCA5 SRSF7 STK10

HOPX

BRAF

HNRNPD

SIN3A RSU1 TRIO ZNF483

ILF2 GPN1EWSR1NXF1FLYWCH1E2F3 GJB3 ESR1 ELL2 PSMC5DAB2ANKRD42ZFYVE1XPO4TNKSSTON1-GTF2A1LUBE2NRPS20SNAI2RPS13TARDBPRNF181SRC PRPS1L1RPL4 PLK1RPS25MYO7BNOP56MURCUBE2D1MLC1RBM11MKL1USP2 METTL3TERF1KRT10RPS6KA1IPO13PAN2 ICT1 RPS8 ICAM1PPP1CAHSP90AB1ZFHX4HDAC4UBL4AGPI MTMR9GIMAP6MAP3K11FOXJ3PRKACBEPC2TAF1ADDX5TGFBR1CYR61KRT1CYP2B6HSPA1LCALM1RPS26C14orf166USP51C14orf1HBE1 BAG3OAT APC TFG AP2M1CASP8AP2ADCY9RASA3ADAM33YY1 ACTC1PAAF1ACO2TMCO3ACIN1USP11ABLIM3Cancer_PPI_ACancer_PPI_B

(A) CPPIN for late stage bladder cancer

Figure 2: Continued. 10 BioMed Research International

EDEM3 SHH

ZFYVE16 NUP160 MTOR

RRP7A KHNYN HMOX2 SALL1 CPSF1 MSH4 IPO13

FAM172A CSNK2A1 KDM1A PABPC1 GJA1 PARD6A MYH10 KIAA1683 MRPS31 KAT5 VHL KLC2 EZH2 XPO4 HAUS6 COPS6 TAF1A EFEMP2 ENY2 ZXDB SAMD1 TNFSF11 TNKS XRCC5 TUBA3E SNRPE TRPT1 RPS8 HSPA1L MURC LARP1 PTBP1 SMC1A ATXN1 PAAF1 GOT1 DCLRE1B HIC2 S1PR1 ILF2 MGC16703 DDX5 HSP90AA1 RPSA RHOF HNRNPC SIN3A SH3RF1 SRSF1 XRCC2 RUVBL2 MDC1 MRPL43 ADCY7 CLK1 CENPI TESK2 TGFBR1 SPRED3 PURG KIF3A TMEM25 SNRNP200 CALM1KDM6A USP51 TK1 CAV1 U2AF1 PEX19 FAM53A ADAM33 TRIM40 HNRNPD DHX9ACO2 CA1 RPLP0 SFN POU2F2 SEMA5A SNCG SLC39A10 Pax3 GBA2 KATNA1 GPN1 RASA3 VCAM1 SLC38A2 SMARCA4 PODXL2 RPS6 RPS27 TP53 SMAD2 AKIRIN1 KRT10 STK35 C14orf1 PSMC1 BASP1 ANXA7 RAE1 ZNF461 UBE2I PSMA2 BRF2 ECM1 SLC35E2 CAPG CDY2A ZBTB25 RPL9 RPS5 PARP1HIST1H1B ABHD5 RB1 NME4 SMC6 ORAI2 STK10 SHMT2YWHAQELK1 CDKN2C GJB3 WDR77 ATXN7 CBX6 SNIP1 DTX4 BRAF ZMYM3 NUTF2 KBTBD6 ACTN2ZBTB16 PRKAA1CDK5RAP2 HOPX TUBB6 GOLPH3L TLE1 CECR5 IKBKB LRCH4 TUBB UBQLN1 LMX1B PRKRIR MKL1 FKBP3 EP300 RAN TERF1 FAM116ACEBPA TRIM34 NSFL1C ATP8B3 ZNF502 POU5F1TNPO2 GRB2 LOC401010 SCYL3 VAV3 SSSCA1MCM7 CRK SRSF7 TTC8 PSMD2 PHKG2 RPS15A DAB2 ILK MBTPS1 RIPK1 GZMB Tceb2 MFN2 RBP1 TMEM159 FANCD2ZMYM6 SIRT1 RPS28 PRDM2CYP2B6 HDGF SNRPD3 SQSTM1 PRKCZ TRAF2 SRC ASB13 YTHDF3 ZNF483 SUMO3C14orf166 NRF1 YWHAB SLC1A1 CTNNB1 AP3S1 CDK9 ATN1 NACC1 TSEN54 TUBG1 PPP2CA CDK6 STK24 FBXO25 SPATA7 ZSCAN18 EEF2K APH1A IP6K1 DNAJC10 SH3KBP1 ZFHX4 ESR1 MRPL54 STAT3 PFKFB4 RBM11 DNM1 ZNF428HSD3B7 SKAP2 MIA2 NUF2 CENPA ZNF259 PRKACB UBQLN4 UBE2D3 FIS1 GFM1 BMI1 SKP1 CDC42EP2TBL3 CLTC ITGA11 NUBP2 TFDP3 AAGAB HDAC1 RPL27A TSNAXIP1 FOXJ3 PCBP2 RBBP4 LRRC58 TERF2 TRPC6 HRASLS SOX3 RPS29 NHP2L1 ATP2A2 DYNLL1 COPS5 UBL7 SLC1A6 MAGEA3SMARCA2 PTPRSFBXL5 RPS3 QARS SMAD4 BNC2 PES1 ARHGEF5 HNRNPH1 STK4 RPL6 U2AF2 ERP27 UBE2E1Prpf8 CCT6A RSU1 RPAP3 LIN37 ACTR3 WDR54 KPNA2 RPL27 CRTC2 BRPF3 RPS9 PKM2 NMT2 SAR1A ALDH4A1SNRPB SYT17 PRKCD NCOA6 MYOT MDH1 HDAC8 SNW1 IGBP1 MT1M PANK2 DRG1 RPL26L1 SMARCA5 SULT1A1 PLEKHG2 FIBP IQSEC2CREBBP CDKN1A DNAJB14 LMNAPPIP5K1 FHOD1 RPS14 RPL31 VIM GTF2E1 NOTCH1 MYC MAP3K11 RPL19 FAM86B1 TERF2IP OSBPL2 PCYT1A ZNF254 CNOT7 RPL30 MICA ABCB6 GIMAP6 RGS1 COPS4 TRIM28 SRSF11 RASD2RPL14 PRDM1 LGALS3BP EPS8 TRAF3IP1 RPS24 MID1 ELF4 PAN2 PRKACA AIP UBE2V1 PPP1CC UCHL5 ESR2 MEX3C KCNMB1 Tuba1a USP19 HDAC2 ZFAND2B COX6C USP7 HDAC7 RPS10 SHC1 APBB1 E2F6 CHFRMYLIP DUSP22 SMAD1RPL37A BAIAP2 AHNAK2 CDK2SIRT4 PDCD6IP ECT2 EBP POLR2ELGALS7B CDK4 KLC1 YWHAGPSMD1FANCI KCTD9PDE12 KCNQ1 ACTBHIBCH FCHO1 TCP1 CDC42 LRBA POLH RPAP2 ZFPM1B4GALT5 RNF208 CCNE2 SUCLG2UBE2U ATP5A1 HNRNPA0RPL12 RPL13 ABCD2 RPL3 SUMO2CORO1B PRICKLE3 CSNK2BTIMM17ASEC23B MLH1MRPS18CMPHOSPH10 CBX5 ISG15 GPR161 L3MBTL3 MAGOH XRCC6 WASH1 CASP8AP2 ZNF501 ICT1 FAM134A GABRB1 HNRNPR TADA2A LPP CNNM4SYTL2 SPG21 LRRK2 REPS2CCT3 CDC25B RPS27L POT1 HDAC4 TNFAIP1 HDAC11IL7R CASZ1 RPL5 PML HSPA8 SF3A2 CSTF2 PSD3 ANKRD42PARD3B PCNA HNRNPM FLYWCH1 CDC5L GMIP DNAJA1 SART1 HNRNPA1ZNF302 E2F3 ALKBH5 AHCYL1 ZMAT3 UNC119ITGA4 GADD45GPOLD1 ECD SP1 ZNF24 UTRN HIST1H2BL HMGCR RANGAP1 SSBP1 GNG10 BAG3 HN1 RPS4X SEC24D CCT7 ARHGAP8 CBL TRA2A HERC2P2 CUL4B RPL23 TMEM188 ATP11B EXOC6RPS7 FAM136ARGL1 PER1 SP140 UBASH3B NXF1 FBXO44 USP11 YWHAH TEX10 HDAC9 VPS13D RPS18 MLL4 RASL11A GNB1 SCARF2 RPL15 TRIO ITGB4 SIRT7 AR IDO1 RBMX ITGB2 UBE2E3 KDM2B PRKDC KIF5B MED6TUBA1C ZBTB7A CHUK ZC3H18 PSMD3 POLR2A ITSN2 ECH1 DTX3 WDR5 MBTD1 ATP9B CIB1 PSMA3 FGFR1OP2 SH3D19 PTP4A3 PI4KAP2 KIF7 KHDRBS2 NEK2 GRK5 TAF12 EHD1 STMN1 RAD23BHIST2H2AC MAPK1 HIST1H2BJ RPL7 USP9X TMED2 AQP3CSDA MLC1 CCM2 HDAC6 WDR91 ATRIP TMEM120A SMPD4HNRNPA2B1WTAP EIF4B SFPQ SLC35A3 OXSR1 C2CD2 RPL17 NUP153ZFYVE1 UBE2D1 MEPCE RPS21 HGS POLR2B UBE2W RFWD2 UBE2E2 ADCY9 KIAA0586 UBE2D2 SNCA FN1 VBP1 NUP93PSMC6 TUFMMDM2 UBR5 HNRNPUL1 HSPA5 ZNF251 CHKA PPIE RPS23 CRABP1 RPS17 OAT MAP1LC3B2 DCD TPM4RAD54L CASK SNAI2 METTL10 CUL4A BRDT FAM174B MC1R SNRPN NOP56 KRTCAP2EFTUD2 BICD2 TBP ZMYND8 ZMPSTE24 RPL4 RPS12 KRT1 TFG C1QA HNRNPF CSNK1ESETDB1 DYNC1H1 KLHL18 LDLR BAG2 LOH12CR1 MVD LIMD1 SARS2 AFAP1 SNRPD2 HNRNPK COL4A1 METTL7A ATP10D BCKDHB HDAC3 Rbx1 PSMC5 POP4 RGS12 ALOX5 CD81 TUBB3 SLC1A4 DDB1 PPP2R1A MAP3K10 KDELC2 DAGLB CAMK2D RPS26 MKNK2 RPS27A RPA1 NOS2 tat ACTR5 DNM2 EIF2AK2 ANKRD35 RANBP2 BPHL TNFRSF1A MAGEL2 MDM1 RPS13 RPL23AMLEC MEX3A CHD1 RPS25 TIMM9FILIP1 CAV2 RPS6KA1 RORCSNRPA ERI1 YWHAZRPL8 RPS19 ROR1TRIM27 PDHX HSPA6 TSPAN10 MYL12B CABLES1 NEDD8 RPL22 ATAD1ATF2 RFXAP GSC PAXIP1 PTPRU SNX19 ELL2 FN3KRP MPV17L CUL5 MAPK9 FLNA UTP14A HIST1H2BDPPP1R14B RBM39 ARMCX3 HDAC5 KRT9 MTMR9 ILF3 LAMB1 CLGN TRPA1 AIDA ERBB2IP PRKD2 SKIV2L2 HNRNPH3 RASAL2 ARHGEF11 UBL4A Eps15 PGGT1B HSPA4 FBXO6 HNRNPU SOX2 GALT PTPN13 COPS2 BRE MAPK6 SLC23A2 GSTM4 UBXN6 IKBKG SRSF2 SRSF3RIOK2 DYNLRB1 RAB2A HSPB11 RHOA CCL18 CYR61 SNRPD1 CSNK2A2 LZTR1TULP2 DHX15 ZER1 CUL3 LEPREL1RPS11 LMBRD1 ST6GALNAC2 EEF1A1 RHEB SMAD3 TXN2 MPP2 CLEC7A DCUN1D1 RPF1 SYNCRIP AHDC1 SCAMP2 MPHOSPH9 TRAF6 IQCB1 PPP1CA CSNK1A1 GFPT2 Htt SRRM2 GPI SERPINE2 RPL7A UBE2K PSEN2 TLX2 BIRC2 ECSCR TRIP11 COX4I1 HIST3H3 STK25 RAD21 GPATCH1 SF3A1 USP2 TRIM32 HES4 ZNF418 HEYL ZZZ3 PIAS2 NAP1L1 CDT1 MRPS22 RNF138 MME LBR DCK SIKE1 HSP90AB1 CAMK1 TMCO3 ASF1A PBK GSK3B PIAS1 SPRY1 TGOLN2 SOX10 EFNB1 WDR53 ASNA1 CCDC85B MCM4 DDX1 NUMBL HSPA1B BRCA1 LENG8 RFC2 IL10RA ICAM1 MYO15B CBX7 LIG4 MYLK2 CUL1 MYH9 NUCB2 PLAU POLR2J RELA HIST1H4A SETD8 UBD MRE11A FAM171B GLIPR2 POLR2C NCL DIAPH2 UBE2D4

MYO7B CLCA2 AP1G1 POGZ RNF151 LRIG2 LTBP1 XPO7 PYROXD1 RGS5

ABLIM3

PAX8

APEX2 UBE2N HIST2H2BE ACTN1 ELAVL1 HSFX1 NEDD4 HRAS NC_PPI_B BARD1 CASP8 CFTR HMGB2 KHDRBS1 PRPS1L1 PLAC8 MEOX1 RPL10A STAU1 CSN1S1 HLA-G STON1-GTF2A1L RPS20 PHAX TSR1 YY1 USP21 Tceb1 SLC22A17 NC_PPI_A EPO NEK3 SCMH1 KCNA4 MYCN ZNF479 DHCR7 PARP6 CAND1 Cdk1 UBC

SPP1

IGSF8 METTL3

SLC27A4 METTL8

(B) CPPIN for late stage bladder cancer (b)

Figure 2: The constructed cancer PPIN (CPPIN) and noncancer PPIN (NPPIN) for early and late stage bladder cancer. The protein association numbers of CPPIN and NPPIN with respect to early and late bladder cancers are listed below (CPPIN/NPPIN): early stage bladder cancer (3388/3151) and late stage bladder cancer (634/1185). The figures are created using Cytoscape. BioMed Research International 11

Table 2: The 18 identified significant proteins of core network marker in both early and late stage bladder cancers.

Common network marker of early and late stage bladder cancer Protein CRV-early P value-early CRV-late P value-late UBC 29.91709 <1e − 5 158.5321 <1e − 5 CUL3 27.96694 <1e − 513.0117 <1e − 5 CUL5 14.97713 <1e − 5 4.834916 0.002872 RPL22 10.47367 <1e − 5 8.110447 <1e − 5 SUMO2 8.391421 <1e − 5 10.34113 <1e − 5 APP 6.933807 <1e − 5 11.47363 <1e − 5 SH3KBP1 6.765387 <1e − 5 4.447911 0.00619 PTBP1 6.740458 <1e − 5 4.511016 0.005506 ELAVL1 6.635085 <1e − 5 10.77056 <1e − 5 SIRT7 5.55441 3.13E − 05 7.656515 <1e − 5 MYC 4.6109 0.00072 13.0423 <1e − 5 HSP90AA1 4.60136 0.00072 6.513345 0.000123 COPS5 3.898548 0.003163 6.601779 9.22E − 05 ESR1 3.873735 0.003383 5.6189 0.000614 BRCA1 3.788256 0.00415 11.5863 <1e − 5 TERF2IP 3.680287 0.00487 5.202998 0.00149 SOX2 3.534024 0.007219 5.495338 0.00076 CUL1 3.521039 0.00736 13.2669 <1e − 5

is to introduce a systems biology approach to can- CPPIN and NPPIN for early and late stage bladder cancer cer network markers based on real microarray data (Figure 2). From the differential networks between CPPIN through the so-called reverse engineering, theoret- and NPPIN of early stage and late stage bladder cancer, we ical statistical method and data mining method in then calculated the CRV of each protein in the network combination with big databases. These are the novelty structure. Screening in accordance with the 𝑃 value of CRV, and significance of our paper. Although we described we determined the significant proteins of network markers the novelty of our systems biology model, we have for the two stages of bladder cancer. In the following, we will validated our results by literature surveying in the discuss the significant proteins identified in both stages and research. In the future, our results will be validated their intersection to reveal the carcinogenesis mechanisms by other researchers’ wet-lab experiments, and we will from early to late stage bladder cancer. modify our mathematical model again and again. This is the third key factor to affect the results. Although 3.2. Network Marker of Early and Late Stage Bladder Cancer. not directly, it will also have the influence on protein After 𝑃 value (0.01) screening, we found that there were interaction network. 152 and 50 significant proteins for early and late stage We also know that the biosystems are evolved with time. bladder cancer, respectively. In addition, their corresponding It is obvious that the early stage and late stage patients have CRV values ranged between 4.1 and 158.5 and 3.4–29.9, very different symptoms; they are the key features for us to respectively. These significant proteins and their PPIs were classify early and late stage bladder cancers. Since the two used to construct the network markers at early and late stage stage bladder cancer patients have great different symptoms, bladder cancer. The intersection network marker of both it is undoubted that the microarray data of these two stage stages was a core feature that contained 18 significant proteins patients will show to be quite different. As described above, in carcinogenesis. We listed the 18 significant proteins and the protein expression from microarray data is one of the key their corresponding CRV and 𝑃 value in both stages of factors of our systems biology model to give the final CPPINs bladder cancer (Table 2). From this, we separately identified and NPPINs. And the CPPINs and NPPINs give the final the 10 most significant proteins in early and late stage bladder network biomarkers from our systems biology model. So, the cancer (Table 3). The full list of the 152 and 50 significant most important thing for the network biomarkers evolving proteins for the two stages of bladder cancer is detailed in is due to the evolution of microarray data at both stages of supplementary tables (Tables S1 and S2). bladder cancer, which is inherent in the exhibition of cancer- related genes due to DNA mutations in the carcinogenesis 3.3. Pathway Analysis of Early Stage Bladder Cancer. We process. analyzed the pathway of early stage bladder cancer using the 3. Results and Discussion DAVID database. Our initial observation revealed that sev- eral cancer pathways were hit by the 152 key proteins, includ- 3.1. Time Evolution of the Network Biomarker from Early to ing 11 genes in hsa05200: pathways in cancer (Figure 3(a)), Late Stage Bladder Cancer. In the first instance, we built the 7 genes involved in prostate cancer, 6 genes involved in 12 BioMed Research International

Table 3: The identified top 20 significant proteins in both early and late stage bladder cancer individually.

Early stage bladder cancer (𝑁 = 107) Late stage bladder cancer (𝑁 =107) CRV Name P value CRV Name P value UBC 29.91709 <1e − 5 UBC 158.5321 <1e − 5 CUL3 27.96694 <1e − 5 VCAM1 20.98798103 <1e − 5 RIOK2 16.02326 <1e − 5 RPS13 20.09693015 <1e − 5 CUL5 14.97713 <1e − 5TP5319.5883<1e − 5 RPS23 12.13218 <1e − 5HDAC119.2879<1e − 5 RPL12 10.87102 <1e − 5 HSPA8 17.24137906 <1e − 5 RPL22 10.47367 <1e − 5RPS27A17.23738059<1e − 5 RANBP2 9.8086 <1e − 5TUBB17.03734405<1e − 5 PAN2 9.521207 <1e − 5 CDK2 16.7366 <1e − 5 DHX9 9.47832 <1e − 5 VIM 15.89214155 <1e − 5 RPS8 8.722495 <1e − 5 KIAA0101 15.8188 <1e − 5 RPL27 8.641642 <1e − 5ITGA415.69058519<1e − 5 SUMO2 8.391421 <1e − 5 GSK3B 15.44597966 <1e − 5 HNRNPH3 8.011681 <1e − 5 EEF1A1 14.21690842 <1e − 5 CDC5L 7.950851 <1e − 5 RUVBL2 13.63207486 <1e − 5 RUVBL1 7.887244 <1e − 5PCNA13.3217<1e − 5 SF3A1 7.468209 <1e − 5 CUL1 13.2669 <1e − 5 APP 6.933807 <1e − 5MYC13.0423<1e − 5 CCT3 6.860228 <1e − 5 CUL3 13.0117 <1e − 5 SH3KBP1 6.765387 <1e − 5 HNRNPA0 12.15264603 <1e − 5 chronic myeloid leukemia, 5 genes involved in small cell cancer [32, 33]. Dysregulation of the cell cycle governs deviant lung cancer, 4 genes involved in bladder cancer, and 3 genes cell proliferation in cancer. Losing the ability to control cell involved in thyroid cancer, respectively (Table 3). The four cycle checkpoints induces abnormal genetic instability. This genes of hsa05219 involved in bladder cancer (TP53, MDM2, may be due to the activation of tumorigenic mutations, which RN1, and MYC) are principal genes altered in urothelial have been recognized in various tumors at different levels carcinoma, which is highly related to metastatic bladder in the mitogenic signal transduction pathways: (1) ligands cancer and are significant targets of metastatic bladder cancer and receptors (receptor mutations of HER2/neu [ErB2] or therapies [30](Figure 3(b)). Thus, we now note that the 152 the amplification of the HER2 gene), (2) downstream sig- candidate proteins are not only related to bladder cancer, but nal transduction networks (Raf/Ras/MAPK or PI3K-AKT- also to other cancers and chronic myeloid leukemia. This mTOR), and (3) regulatory genes of the cell cycle (cyclin would mean that common mechanisms exist between the D1/CDK4, CDK6, and cyclin E/CDK2) [34]. Increasing development of the different cancers in the early stage of evidence convincingly implicates aberrant expression of cell carcinogenesis. cycle regulators in multiple cancers. Especially the restriction Next, we proceeded to analyze the important pathways point (R) is the so-called G1 checkpoint. It separates the related to early stage bladder cancer (Table 4). Firstly, the cell cell cycle into a mitogen-dependent phase and a growth cycle is composed of two consecutive periods (Figure 3(c)) factor-independent phase from the commitment to enter S characterized by DNA replication, sequential differentiation, phase. The G1 checkpoint commitment process integrates and segregation of replicated chromosomes into two separate various and complex extracellular and intracellular signal daughter cells. Both positive-acting and negative-acting pro- transduction into the cell nucleus. Any malfunction of the teins control the cells’ entry and advancement through the G1 checkpoint may result in uncontrolled cell proliferation cell cycle, which is composed of four distinct phases: G1 (Gap or genetic instability, possibly the origin of cancer or other 1), S (synthesis), G2 (Gap 2), and M (mitosis) [31]. The G1 diseases development [35]. phase, where the cell grows in size, acts as a quality control The Wnt/𝛽-catenin signaling pathways (Figure 3(d))are check to determine whether the cell is ready to divide. The S composed of many functional networks, including a bundle phaseiswherethecellcopiesitsDNA.TheG2phaseinvolves of signaling pathways consisting of various proteins that cell checking as to whether all of its DNA has been correctly transducesignalsfromtheoutsideofacellthroughthe copied. The M phase is the cell division phase where the receptors on the cell surface and into the cell interior. cell divides in two. Find out more about how cells prepare They contribute significantly to the developmental process, to divide and then share out their DNA and split in two. particularly to direct cell attachment and proliferation. They There are many reported discussions in regards to the cell areoneofthemostpowerfulsignalingpathwaysandplaycrit- cycle regulators and checkpoint functions involved in bladder ical roles in human development by controlling the genetic BioMed Research International 13

Carcinogens Testosterone Dihydro- Other SHH FasL TGF𝛽 Pathways in cancer testosterone ligands Cholesterol

TGF𝛽RI PTCH1 SMO Fas TGF𝛽RII

+ Elongin B FADD p Fumarate HPH Elongin C CUL2 Apoptosis Smad2/3 Smad4 AR FH VHL Rbx1 CASP8 HSP Microtubule Cos2 𝛽 Fu Su (fu) Malate TGF- signaling CASP3 Mitochondrion pathway GSTP1 CASP9 Cyt C Bid CASP9 Mitochondrion DCC CASP3 AR GLI APPL HIF-𝛼 AR e Apoptosis Bcl-2

HIF-𝛽 EVI1 p300/CBP Tissue invasion AML-EVI1 e and metastasis BMP GLI1 Wnt e BRCA2 hMLH1 𝛼-Catenin Failed repair Rad51 of genes ECAD 𝛽-Catenin 𝛽-Catenin HIP1 PTCH1, 2 hMSH2 CtBP Glut1 VEGF TGF-𝛼 e HDAC Adherens junction DNA DNA e PDGF𝛽 + p PSA TGF𝛽 Wnt Frizzled Dv1 GSK-3𝛽 𝛽-Catenin Bax hMSH3 TGF𝛽RII + Axin APC Wnt signaling p TCF/LEF pathway hMSH6 Sustained Insensitivity to e Genomic damage Survivin angiogenesis anti-growth signals DNA damage c-Myc VEGF signaling pathway ITGA CyclinD1 Resistance to ECM FAK MITF e ITGB p53 chemotherapy PI3K-Akt 𝜅 𝛼 COX-2 p53 signaling signaling pathway IKK + I B pathway + p ECM-receptor p iNOS interaction Focal adhesion MDM2 p14/ARF NF𝜅B e e NKX3.1 cIAPs

PTEN mTOR signaling Bcl-XL TRAFs − pathway p + Bcl-2 CyclinD1 p16/INK4a p21 Cell cycle p PIP3 PKB/Akt + CRKL mTOR Evading apoptosis p + BCR-ABL CBL PI3K p CDK4/6 + CASP9 p Rb p15/INK4a e Miz1 CRK CyclinD1 c-Myc + + p Bad Bcl-XL p Max + E2F p FKHR CDK2 e + CyclinE p MDM2 p53 e p27/kip1 CKS1 + p27 p Proliferation + Skp2 p p21 Block of Jak-STAT differentiation signaling pathway STAT5 e Bcl-XL Cytokine-cytokine + receptor interaction p STAT3 Jak1 e VEGF STAT1 𝛼 NORE1A TGF EGFR GMCSFR GCSFR E2F MCSFR MST1 EGF ERBB2 RASSF1 GCSFR MCSFR IL-6 c-Myc TCR CyclinD1 𝛼 RAR PU.1 C/EBP𝛼 AML1 PDGF PDGFR ErbB signaling VEGF target genes pathway c-Jun CyclinD1 target genes target genes target genes IGF-1 IGFR + MMPs p c-Fos c-Myc KITLG Grb2 Sos Ras Raf ERK e IL-8 e e e ? c-KIT + MEK + 𝛿 CyclinA1 e e e e e p p c-Myc PPAR FLT3LG FLT3 RET/PTC Ets1 HGF MET DAPK CyclinD1 e e e PU.1 PU.1 C/EBP𝛼 C/EBP𝛼 AML1 CDK4 FGF FGFR RASSF1A MAPK signaling pathway RAR𝛽 RXR PPAR𝛾 RXR PLZF-RAP𝛼 FML-RAR𝛼 AML1-ETC RalBP1 Cdc42/Rac PPFP + Ral TRK p PLD1 PKC RalGDS DAG TCF PLC𝛾 PA PPAR signaling Rac/Rho JNK pathway plakogldoin 2+ Ligands Retinoic acid IP3 Ca

Colorectal Pancreatic Acute myeloid Chronic myeloid Basal cell Renal cell Endometrial Small cell Non-small-cell Glioma Thyroid cancer Melanoma Bladder cancer Prostate cancer cancer cancer leukemia leukemia carcinoma carcinoma cancer lung cancer lung cancer (a) The proteins in the early stage bladder cancer network marker are enriched in “hsa05200:Pathways in cancer” (Rank 2in Table 4)

Bladder cancer

Urothelial cell Normal urothelium MAPK signaling pathway H-Ras + + + p p p DNA DNA FGFR3 Ras Raf MEK ERK MSK1 c-Myc Normal Hyperplasia urothelium Promoter methylation DNA RASSF1A DAPK Low-grade noninvasive papillary tumour

Recurrence p14ARF MDM2 CIS/dysplasia Genetic alterations DNA INK4a p53 p21 p16 Oncogenes: HRAS, FGFR3 Tumor suppressors: CDKN2A, p53, and RB p53 signaling + CyclinD1 p Cell cycle pathway Rb CDK4 DNA E2F G1/S progression

Overexpression VEGF signaling TSP-1 pathway ERBB2 DNA Invasive tumour EGF ErbB signaling Endothelial (high grade) EGFR pathway VEGF cell proliferation Angiogenesis Reduced expression MMPs Degradation of extracellular matrix Endothelial E-cad Defect in cell migration cell-cell adhesion Oxidative TP Adherens stress IL-8 Chemoattraction of junction endothelial cells Metastasis

(b) The proteins in the early stage bladder cancer network marker are enriched in “hsa05219:Bladder cancer” (Rank 7in Table 4)

Figure 3: Continued. 14 BioMed Research International

Cell cycle

Growth factor DNA damage check point Growth factor withdrawal TGF𝛽 Smc1 Smc3 ARF 107 Stag1, 2 p 300 p Rad21 Cohesin E2F4, 5 Smad2, 3 GSK3𝛽 DNA-PK ATMATR DP-1, 2 4 Smad Mdm2 Mps1 SCF + p + Esp1 Separin Skp2 p c-Myc + p Mad1 e 53 1 e Rb p Apoptosis PTTG Securin MAPK Miz + p + e + signaling e + + p u u u e e Mad2 pathway 16 15 18 19 27 57 21 p p p p p , p Chk1, 2 BubR1 APC/C Ink4a Ink4bInk4cInk4dKip1, 2 Cip1 GADD45 14-3-3𝜎 Bub1 Bub3 Cdc20

+ p 14-3-3 PCNA + + p p Ubiquitin Cdc25A Cdc25B, C mediated + proteolysis − p + p p − R-point CycD p CycE − CycA − CycH CycA CycB + p p − p 4 6 2 2 7 p CDK1 CDK , + CDK CDK + CDK + CDK1 Plk1 (start) u p p + + + + + + + p p p p p p u + + + + + p p p p p SCF APC/C Ab1 Cdc6 Cdc45 Rb Wee Myt1 + Skp2 p107, 130 Rb p Cdh1 HDAC − ORC MCM p + 2 4 5 2 1 2 3 p E F , E F , , + Cdc14 p DP-1, 2 DP-1, 2 Cdc7 Bub2 MEN Dbf4 S-phase proteins, CycE DNA biosynthesis Orc (origin Mcm (minichromosome DNA recognition complex) maintenance) complex DNA

Orc 1 Orc 2 Mcm 2 Mcm 3 Orc 3 Orc 4 Mcm 4 Mcm 5 Orc 5 Orc 6 Mcm 6 Mcm 7

G1 (c) The proteins in the early stage bladder cancer network marker are enriched in “hsa04110:Cell cycle” (Rank 1 in Table 4)

WNT signaling pathway Adherens junction

Alzheimer’s MAPK signaling disease pathway + p Canonical pathway PS-1 TAK1 NLK PKA Idax 1 FRP Duplin + c-myc Cer- CBP p Dally Scaffold c-jun CKI𝜀 + 52 Porc GBP p Pontin fra-1 Cell Frizzled Dvl GSK-3𝛽𝛽 𝛽-Catenin TCF/LEF Wnt cycD LRP5/6 Axin APC CKI𝛼 SMAD4 DNA cycle CK2 𝛿 SMAD3 PPAR Wif-1 Axam ICAT BAMB1 Uterine SOST Catenin, 𝛽 interacting protein 1 + Phosphorylation Dkk p P53 signaling independence pathway 𝛽 -TrCP TGF-𝛽 signaling P53 Siah-1 Skp1 PAR-1 Nkd Ubiquitin mediated pathway SIP Cull proteolysis APC Skp1 Rbx1 TBL1 + u Phosphorylation Proteolysis independence Planar cellpolarity (PCP) pathway Focal adhesion Stbm Prickle Daam1 RhoA ROCK2 Cytoskeletal change Wnt11 Frizzled Dvl Rac JNK Gene transcription Knypek MAPK sigaling pathway + Wnt/Ca2 pathway CaMKII 2+ − Ca p Wnt5 Frizzled PLC CaN NFAT

G protein? PKC DNA

(d) The proteins in the early stage bladder cancer network marker are enriched in “hsa04110:Wnt signaling pathway” (Rank 5in Table 4)

Figure 3: Continued. BioMed Research International 15

Ubiquitin mediated proteolysis Ub Degradation of Ub Ub target protein E1 Ub Ub Ub Ub Ub Proteasome Ub Target E2 Polyubiquitination ATP AMP Ub E3

Target Ub Ub

Target-recognizing E2-interacting Ub Ub domain domain Recycling

E1 E2 (ubiquitin-activating enzyme) (ubiquitin-conjugating enzyme)

UBE1 UBLE1A UBLE1B UBE1C UBE2A UBE2B UBE2CUBE2DE UBE2F UBE2G1 UBE2G2 UBE2H

UBE2I UBE2J1 UBE2J2 UBE2L3 UBE2L6 UBE2M UBE2N UBE2O UBE2Q UBE2R UBE2S UBE2U UBE2W UBE2Z HIP2 APCLLCN

E3 (ubiquitin ligase) HECT type E3 U-box type E3 Single RING-finger type E3

Ub Ub Ub Ub Ub Ub Ub Ub Ub Target Target Target Ub E2 E2 E2

HECT U-box RING-finger domain domain domain

E6AP UBE3B UBE3C Smurf Itch UBE4A UBE4B CHIP Mdm2 CBL Parkin SIAH-1 PML TRAF6 MEKK1

WWP1 WWP2 TRIP12 NEDD4 ARF-BP1 CYC4 PRP19 UIP5 COP1 PIRH2 cIAPs PIAS SYVN NHLRC1 AIRE EDD1 HERC1 HERC2 HERC3 HERC4 MGRN1 BRCA1 FANCLMID1 Trim32 Trim37

Multisubunit RING-finger type E3 Target Adaptor Target RING Cullin recognizing RING Cullin Adaptor recognizing Other Cullin-Rbx E3 finger protein subunit APC/C finger protein subunit subunits

SCF complex RBX1 Cul1 Skp1 F-box Apc11 Apc2 ? Cdc20 Apc1 Apc3 Cdh1 Apc4 Apc5 Apc7 RING- ECV complex RBX1 Cul2 EloB VHLbox Apc6 finger E2 Ub Apc11 E2 Ub Apc8 Apc9 EloC Apc10 Apc12 RBX1 Cul3 BTB Cul3 complex Apc13 Ub Ub Cullin Ub Apc2 Cul4 complex RBX1 Cul4 DDB1 DCAF Ub

Adaptor Target Target protein ECS complex RBX2 Cul5 EloB SOCSbox ? EloC Target recognizing subunit Cdh1 Cdc20 Cul7 complex RBX1 Cul7 Skp1 Fbxw8 (e) The proteins in the early stage bladder cancer network marker are enriched in “hsa04120:Ubiquitin mediated proteolysis pathway” (Rank 13 in Table 4)

Figure 3: Overview of significant pathways in network marker of early stage bladder cancer. Among these KEGG pathways via DAVID tool (Table 4) showing a significant association with specific proteins of early stage bladder cancer, these molecular pathways are entitled with P value ≤ 0.05. It shows that these pathways are identified to play an important role in the carcinogenesis mechanism of early stage bladder cancer. The proteins in network markers of early stage bladder cancer highlighted by stars show potential targets in the pathways. Due tothe different naming system, the same proteins in both these tables and in our text show the different names. programs of embryonic development and adult homeostasis participates in each of the stages of malignant cancer devel- [36]. Under normal conditions, the Wnt signaling pathway opment and clearly contributes to human tumor progression. is critical for healthy and normal development, while in Much research has been reported on the relationship between adult cells, a dysregulated Wnt signaling pathway can lead Wnt signaling pathways and urological cancers (including to tumorigenesis. For this purpose, cancer cells must have bladder cancer) [37, 38]. theabilitytoswitchfromquiescentmodetoproliferation Other pathways identified in early stage bladder cancer, mode,aswellasswitchingbetweencellproliferationand such as the Notch signaling pathway, adherens junctions, cell invasion modes. Therefore, the Wnt signaling pathway the TGF-𝛽 signaling pathway, ubiquitin-mediated proteolysis 16 BioMed Research International

Ribosome Ribosomal RNAs Bacteria/archaea 23S 5S 16S eukaryotes 25S 5S 5.8S 18S

Ribosomal proteins

EF-Tu S10 L3 L4 L23 L2 S19 L22 S3 RP-L16 L29 L7/L12 S20e L3e L4e L23Ae L8e S15e L17e S3e L35e stalk L10e SecY S17 L14 L24 L5S14 S8 L6 L18 S5 L30 L15 S11eL23eL26e S4e L11eS29e S15Ae L9eL32e L19e L5e S2e L7e L27Ae

IF1 RpoA L36 S13S11 S4 L17 L13 S9 L34e L14eS18eS14e S9e L18e L13Ae S16e Large subunit (haloarcula marismortui) EF-Tu, G RpoC, B S7 S12 L7A L7/L12 L12 L10L1 L11 S5e S23e L30e L7Ae LP1, LP2 LP0 L10Ae L12e

EF-Ts IF2 IF3 RF1 S2 S15 L35 L20 L34 L31 L32 L9 S18 S6 SAe S13e

L28L33 L21 L27FtsY, F S16L19 S1 S20 S21 L25

L10e L13e L15e L21e L24e L31e L35Ae L37e L37Ae L39e L40e L41e L44e

S3Ae S6e S8eS17e S19e S24e S25e S26e S27e S27Ae S28e S30e LX

L6eL18Ae L22e L27e L28e L29e L36e L38e

S7e S10e S12e S21e Small subunit (thermus aquaticus) (a) The proteins in the late stage bladder cancer network marker are enriched in “hsa03010:Ribosome” (Rank 1 in Table 5)

Spliceosome

Spliceosome components 󳰀 󳰀 5 splice site Branch point 3 splice site U1 U2 U4/U6 U5 Pre-mRNA ExonGU A AG Exon U1snRNA U2snRNA U4snRNA U5snRNA

U6snRNA Sm Sm Sm 󳰀 U1-70K U2A Lsm Snu114 U1 Intron Prp43 󳰀󳰀 U1A U2B Sm Bn2 mRNA U1C SF3a Prp3 Prp6 Prp4 U1 related SF3b Prp8 U1 Common U2 CypH components U2 related Prp8BP A U2 FBP11 AF Prp31 Prp28 S164 U2AF U6 Snu13 DIB1 Prp5 Prp22 p68 PUF60 U2 4 6 5 UAP56 U5 U /U . U CA150 SPF30 tri-SnRNP associated SPF45 A SnRNP27 CHERP U1 Sad1 U2 U4/U6.U5 SR140 Snu66 A Post-spliceosomal complex tri-snRNP Prp43 Complex A U4 U6 Prp16 Prp22 Snu23 EJC/ U5 TREX Prp38 Prp17 Slu7 2nd step Prp18 PRP19 complex U6 Prp19 Prp19 Common complex related EJC/TREX components U5 U2 19 U4 U6 A Prp SKIP ACINUS CBP80/20 U1 U4 U5 CDC5 eIFA3 1st step Syf hnRNPs U2 Complex C A SPF27 Y14 Isy1 SR U6 PRL1 magoh Prp2 PPIL1 Complex B U5 U2 AD002 UAP56 A U1 CypE CTNNBL1 THOC

HSP73 CCDC12 Activated spliceosome∗ complex B RBM22 NPW38 Complex B specific G10 U4/U6 snRNP NPW38BF U6 snRNA AQR Lsm Prp3 5󳰀 3󳰀 3󳰀 5󳰀 Sm Prp4 Prp31 CypH

U4 snRNA Snu13

(b) The proteins in the late stage bladder cancer network marker are enriched in “hsa03040:Ribosome” (Rank 2in Table 5) Figure 4: Continued. BioMed Research International 17

Ubiquitin mediated proteolysis Ub Degradation of Ub Ub E1 target protein Ub Ub Ub Ub Ub Proteasome Ub Target E2 Polyubiquitination ATP AMP Ub E3

Target Ub Ub

Target-recognizing E2-interacting Ub Ub domain domain Recycling

E1 E2 (ubiquitin-activating enzyme) (ubiquitin-conjugating enzyme)

UBE1 UBLE1A UBLE1B UBE1C UBE2A UBE2B UBE2CUBE2DE UBE2F UBE2G1 UBE2G2 UBE2H

UBE2I UBE2J1 UBE2J2 UBE2L3 UBE2L6 UBE2M UBE2N UBE2O UBE2Q UBE2R UBE2S UBE2U UBE2W UBE2Z HIP2 APCLLCN

E3 (ubiquitin ligase) HECT type E3 U-box type E3 Single RING-finger type E3

Ub Ub Ub Ub Ub Ub Ub Ub Ub Target Target Target Ub E2 E2 E2

HECT U-box RING-finger domain domain domain

E6AP UBE3B UBE3C Smurf Itch UBE4A UBE4B CHIP Mdm2 CBL Parkin SIAH-1 PML TRAF6 MEKK1

WWP1 WWP2 TRIP12 NEDD4 ARF-BP1 CYC4 PRP19 UIP5 COP1 PIRH2 cIAPs PIAS SYVN NHLRC1 AIRE EDD1 HERC1 HERC2 HERC3 HERC4 MGRN1 BRCA1 FANCLMID1 Trim32 Trim37

Multisubunit RING-finger type E3 Target Adaptor Target RING Cullin recognizing RING Cullin Adaptor recognizing Other Cullin-Rbx E3 finger protein subunit APC/C finger protein subunit subunits

SCF complex RBX1 Cul1 Skp1 F-box Apc11 Apc2 ? Cdc20 Apc1 Apc3 Cdh1 Apc4 Apc5 Apc7 RING- ECV complex RBX1 Cul2 EloB VHLbox Apc6 finger E2 Ub Apc11 E2 Ub Apc8 Apc9 EloC Apc10 Apc12 RBX1 Cul3 BTB Cul3 complex Apc13 Ub Ub Cullin Ub Apc2 Cul4 complex RBX1 Cul4 DDB1 DCAF Ub

Adaptor Target Target protein ECS complex RBX2 Cul5 EloB SOCSbox ? EloC Target-recognizing subunit Cdh1 Cdc20 Cul7 complex RBX1 Cul7 Skp1 Fbxw8 (c) The proteins in the early stage bladder cancer network marker are enriched in “hsa04120:Ubiquitin mediated proteolysis pathway” (Rank 3 in Table 5)

Figure 4: Overview of significant pathways in network marker of late stage bladder cancer. Among these KEGG pathways via DAVID tool (Table 5) showing a significant association with specific proteins of late stage bladder cancer, these molecular pathways are entitled with P value ≤ 0.05. It shows that these pathways are identified to play an important role in the carcinogenesis mechanism of late stage bladder cancer. The proteins in network markers of late stage bladder cancer highlighted by stars show potential targets in the pathways. Due to the different naming system, the same proteins in both these tables and in our text show the different names.

(Figures 3(e) and 4(c)), and the p53 signaling pathway are also thebiologicalprocessesarerelatedtothemetabolicprocesses. associated with cancer [39–43]. Second, about the cellular components, there are three of TheNOAanalysisresultsofthepathwayandgene them related to the ribosome. Finally, about the molecular enrichment analysis of the early stage bladder cancer is functions, there are RNA binding, heparin binding and cyclin shown in Table 4(b): (1) Biological processes (2) Cellular binding, which are very different from the late stage bladder components (3) Molecular functions. We saw that most of cancer. 18 BioMed Research International

Table 4: (a) The pathways analysis for 152 early stage significant proteins in carcinogenesis. (b) The pathway analysis and gene set enrichment analysis of the top 20 proteins of early stage bladder cancer on (1) biological processes, (2) cellular components, and (3)molecularfunctions by NOA.

(a)

Rank Term Count Symbol P value YWHAZ, CREBBP, TP53, PRKDC, RB1, CDK2, HDAC2, 1 hsa04110:Cell cycle 13 1.50E − 14 EP300, HDAC1, PCNA, MDM2, MYC, andCUL1 TRAF2, EP300, HDAC2, 2 hsa05200:Pathways in cancer 11 HDAC1, CREBBP, TP53, MDM2, 3.51E − 07 RB1, MYC, CDK2, and CTNNB1 EP300, CREBBP, TP53, MDM2, 3 hsa05215:Prostate cancer 7 1.45E − 06 RB1, CDK2, and CTNNB1 4 hsa05220:Chronic myeloid 6 HDAC2, HDAC1, TP53, MDM2, 1.32E − 05 leukemia RB1, and MYC EP300, CREBBP, TP53, MYC, 5 hsa04310:Wnt signaling pathway 6 3.78E − 04 CUL1, and CTNNB1 TRAF2, TP53, RB1, MYC, and 6 hsa05222:Small cell lung cancer 5 4.04E − 04 CDK2 7 hsa05219:Bladder cancer 4 TP53, MDM2, RB1, and MYC 7. 24 E − 04 EP300, HDAC2, HDAC1, and 8 hsa04330:Notch signaling pathway 4 0.001008 CREBBP EP300,CREBBP,SRC,and 9 hsa04520:Adherens junction 4 0.00418 CTNNB1 10 hsa04350:TGF-beta signaling 4 EP300,CREBBP,MYC,and 0.005889 pathway CUL1 EP300, HDAC2, HDAC1, 11 hsa05016:Huntington’s disease 5 0.006736 CREBBP, and TP53 12 hsa05216:Thyroid cancer 3 TP53,MYC,andCTNNB1 0.00676 13 hsa04120:Ubiquitin mediated 4 CUL3, MDM2, BRCA1, and 0.020254 proteolysis CUL1 14 hsa05213:Endometrial cancer 3 TP53,MYC,andCTNNB1 0.020793 15 hsa05214:Glioma 3 TP53, MDM2, and RB1 0.029762 16 hsa04115:p53 signaling pathway 3 TP53, MDM2, and CDK2 0.034267 17 hsa05218:Melanoma 3 TP53, MDM2, and RB1 0.037091 18 hsa05210:Colorectal cancer 3 TP53,MYC,andCTNNB1 0.050311 19 hsa04210:Apoptosis 3 TRAF2, IRAK1, and TP53 0.053574 hsa03450:Non-homologous 20 2 XRCC6and PRKDC 0.05487 end-joining 21 hsa04916:Melanogenesis 3 EP300, CREBBP, and CTNNB1 0.067353 22 hsa04114:Oocyte meiosis 3 YWHAZ, CDK2, and CUL1 0.080913 hsa04722:Neurotrophin signaling 23 3 IRAK1, YWHAZ, and TP53 0.099295 pathway The significant pathways via DAVID Bioinformatics database are selected for the 152 significant proteins in carcinogenesis. Black backgroundes indicat 𝑃 value > 0.05. (b)

GO:term 𝑃 value Corrected 𝑃 value 𝑅𝑇𝐺𝑂Term name (1) Biological processes 0044260 5.3E − 11 1.7E − 81479119342818Cellular macromolecule metabolic process BioMed Research International 19

(b) Continued.

GO:term 𝑃 value Corrected 𝑃 value 𝑅𝑇𝐺𝑂Term name 0043170 7.3E − 10 2.4E − 71479119397518Macromolecule metabolic process 0044237 3.6E − 81.2E − 51479119496318Cellular metabolic process 0006414 1.4E − 7 4.7E − 514791191015Translational elongation 0019538 1.0E − 63.2E − 41479119252813Protein metabolic process 0008152 1.1E − 63.6E − 41479119603318Metabolic process 0044238 1.7E − 65.6E − 41479119525817Primary metabolic process 0016071 4.4E − 6 0.0014 14791 19 364 6 mRNA metabolic process 0044267 3.3E − 5 0.0110 14791 19 1883 10 Cellular protein metabolic process 0009987 1.2E − 4 0.0406 14791 19 9216 19 Cellular process (2) Cellular components 0030529 7.9E − 10 7.8E − 816768185109Ribonucleoprotein complex 0032991 1.8E − 71.8E − 51676818331214Macromolecular complex 0043228 1.2E − 61.2E − 41676818205111Non-membrane-bounded organelle 0043232 1.2E − 61.2E − 41676818205111Intracellular non-membrane-bounded organelle 0005840 1.5E − 61.5E − 416768181965Ribosome 0005829 2.0E − 62.0E − 4167681812699Cytosol 0043229 8.3E − 6 8.2E − 4 16768 18 8759 18 Intracellular organelle 0043226 8.5E − 6 8.4E − 4 16768 18 8773 18 Organelle 0044445 1.7E − 5 0.0016 16768 18 150 4 Cytosolic part 0033279 2.8E − 4 0.0287 16768 18 123 3 Ribosomal subunit (3)Molecularfunctions 0003735 1.0E − 61.1E − 415767191615Structural constituent of ribosome 0003723 1.8E − 4 0.0193 15767 19 755 6 RNA binding 0005198 8.0E − 4 0.0825 15767 19 643 5 Structural molecule activity 0031625 0.0013 0.1360 15767 19 45 2 Ubiquitin protein ligase binding 0003678 0.0013 0.1360 15767 19 45 2 DNA helicase activity 0004535 0.0024 0.2480 15767 19 2 1 Poly(A)-specific ribonuclease activity 0033130 0.0048 0.4956 15767 19 4 1 Acetylcholine receptor binding 0008201 0.0079 0.8140 15767 19 112 2 Heparin binding 0030332 0.0096 0.9890 15767 19 8 1 Cyclin binding 0004386 0.0129 1 15767 19 145 2 Helicase activity 𝑅: number of genes in reference set. 𝑇: number of genes in test set. 𝐺: number of genes annotated by given term in reference set. 𝑂: number of genes annotated by given term in test set.

3.4. Pathway Analysis of Late Stage Bladder Cancer. The the nucleolus is easily detected by microscopy in living most important results in this study as compared to our cells. From electron microscopy images, three major previous work are that we reveal related pathways of late stage components were constantly exhibited by mammalian bladder cancer in comparison to early stage cancer to reveal cells. They include fibrillar centers (FCs), which appear the evolution of network biomarkers in the carcinogenesis as surrounding structures of various sizes, with a very low process. From Table 5,weobservedthatonlythreepathways, electron opacity; the dense fibrillar component (DFC), which ribosome, spliceosome, and ubiquitin-mediated proteolysis always constitutes a rim intimately accompanied with the pathways, were hit by the 50 candidate proteins identified in fibrillar centers, composed of densely packed fibrils; and the late stage bladder cancer. This is indicative of the evolution of granular component (GC), which is composed of granules cancer mechanisms from early stage bladder cancer. that surround the fibrillar components. There is evidence Thenucleolusisthesiteofribosomebiogenesis that changes in nucleolar morphology and function may (Figure 4(a)). Due to the higher concentration of both RNA depend on both the rate and status of ribosome biogenesis andproteinsinthenucleolusthaninthenucleoplasm, and on the proliferative activity of cycling cells [44]. In 20 BioMed Research International

Table 5: (a) The pathways analysis for 50 significant proteins in late stage bladder cancer carcinogenesis. (b) The pathway analysis andgeneset enrichment analysis of the top 20 proteins of late stage bladder cancer on (1) biological processes, (2) cellular components and (3)molecular functions by NOA.

(a)

Rank Term Count Symbol P value RPS28, RPS16, RPL22, RPL27, RPL12, 1 hsa03010:Ribosome 8 2.26E − 07 RPS6, RPS8, and RPS23 HSPA1L,CDC5L,SF3A1,SNRPE,and 2 hsa03040:Spliceosome 5 0.004054716 HNRNPU hsa04120:Ubiquitin 3 4 CUL3, CUL5, BRCA1, and CUL1 0.034906958 mediated proteolysis The significant pathways via DAVID Bioinformatics database are selected for the 50 significant proteins in carcinogenesis. (b)

GO:term 𝑃 value Corrected 𝑃 value 𝑅𝑇𝐺𝑂 Term name (1) Biological processes GO:0045786 1.8E − 6 0.0010 14791 18 178 5 Negative regulation of cell cycle GO:0022402 2.4E − 6 0.0014 14791 18 562 7 Cell cycle process GO:0007050 8.4E − 6 0.0049 14791 18 111 4 Cell cycle arrest GO:0051726 8.5E − 6 0.0049 14791 18 435 6 Regulation of cell cycle GO:0060710 1.3E − 5 0.0080 14791 18 5 2 Chorioallantoic fusion GO:0044260 1.4E − 5 0.0081 14791 18 3428 13 Cellular macromolecule metabolic process GO:0051052 1.5E − 5 0.0091 14791 18 130 4 Regulation of DNA Metabolic process GO:0008629 2.6E − 5 0.0155 14791 18 49 3 Inductionofapoptosisbyintracellular signals GO:0006917 2.8E − 5 0.0162 14791 18 313 5 Inductionofapoptosis GO:0012502 2.8E − 5 0.0165 14791 18 314 5 Inductionofprogrammedcelldeath (2) Cellular components GO:0032991 5.2E − 10 5.5E − 8 16768 18 3312 16 Macromolecular complex GO:0005829 1.4E − 71.5E − 5 16768 18 1269 10 Cytosol GO:0043234 2.5E − 62.6E − 41676818274812 Protein complex GO:0005654 6.1E − 6 6.4E − 416768184656 Nucleoplasm GO:0044428 6.4E − 5 0.0067 16768 18 1932 9 Nuclear part GO:0000307 9.8E − 5 0.0102 16768 18 14 2 Cyclin-dependent protein kinase holoenzyme complex GO:0030529 1.5E − 4 0.0163 16768 18 510 5 Ribonucleoprotein complex GO:0031461 4.9E − 4 0.0516 16768 18 31 2 Cullin-RING ubiquitin ligase complex GO:0022627 7.4E − 4 0.0777 16768 18 38 2 Cytosolic small ribosomal subunit GO:0043626 0.0010 0.1116 16768 18 1 1 PCNA complex (3)Molecularfunctions GO:0019899 3.1E − 5 0.0037 15767 18 584 6 Enzyme binding GO:0005515 1.1E − 4 0.0129 15767 18 8097 17 Protein binding GO:0030337 0.0011 0.1335 15767 18 1 1 DNA polymerase processivity factor activity GO:0000701 0.0011 0.1335 15767 18 1 1 Purine-specific mismatch base pair DNA N-glycosylase activity GO:0031625 0.0011 0.1384 15767 18 45 2 Ubiquitin protein ligase binding GO:0000166 0.0021 0.2510 15767 18 2283 8 Nucleotide binding GO:0035033 0.0022 0.2669 15767 18 2 1 Histone deacetylase regulator activity BioMed Research International 21

(b) Continued.

GO:term 𝑃 value Corrected 𝑃 value 𝑅𝑇𝐺𝑂 Term name GO:0004696 0.0022 0.2669 15767 18 2 1 Glycogen synthase kinase 3 activity GO:0000700 0.0022 0.2669 15767 18 2 1 Mismatch base pair DNA N-glycosylase activity GO:0005200 0.0031 0.3705 15767 18 74 2 Structural constituent of cytoskeleton 𝑅: number of genes in reference set. 𝑇: number of genes in test set. 𝐺: number of genes annotated by given term in reference set. 𝑂: number of genes annotated by given term in test set.

Table 6: The pathways analysis for 18 significant proteins in early and late stage bladder cancer carcinogenesis.

Rank Term Count Symbol P value 1 hsa03010:Ribosome 4 CUL3, CUL5, BRCA1, and CUL1 1.4E − 3 cancer cells the upregulated ribosome biogenesis leads to an and Sm2) that are responsible for the assembly and ordering increased demand of ribosomal proteins for rRNA binding. of the snRNAs. They form the Sm core of the spliceosomal In this way, after ribosome biogenesis alterations, cycling snRNPs [48] and process the pre-mRNA [49]. Spliceosomes cells can activate the p53 pathway to ensure cell cycle arrest or not only catalyze splicing by a series of reactions, but they alternatively to start the apoptotic program [45]. According are also the main cellular machinery that guides splicing. to our analysis, there were eight significant proteins in the Recently, scientists have found two natural compounds that latestagecancertohittheribosomepathway. can interfere with spliceosome function that also display Alternative splicing is a modification of the premessenger anticancer activity in vitro and in vivo [50, 51]. Therefore, it is RNA (pre-mRNA) transcript in which internal noncoding believable that inhibiting the spliceosome could act as a new regions of pre-mRNA (introns) are removed and then the targetforanticancerdrugdevelopment[52], and it should be remaining segments (exons) are joined (Figure 4(b)). The for- validatedinvivoorinvitrointhefuture. mation of mature messenger RNA (mRNA) is subsequently The NOA analysis results of the pathway and gene enrich- 󸀠 󸀠 capped at its 5 end and polyadenylated at its 3 end, and ment analysis of the late stage bladder cancer is shown in transported out of the nucleus to be translated into protein Table 5(b): (1) Biological processes (2) Cellular components in the cytoplasm. Most genes use alternative splicing to (3) Molecular functions. We saw most of the biological generate multiple spliced transcripts. These transcripts con- processes are related to cell cycle, which are different from tain various combinations of exons resulting from different the metabolic processes of early stage. Second, about the mRNAvariantsandthenaresynthesizedasproteinisoforms. cellular components, there are complex evolution behav- The exons are always around 50–250 base pairs, whereas iors of the network compared with the early stage bladder introns could be as long as several thousands of base pairs. cancer; there is only one intersection of these two stages For nuclear encoded genes, splicing takes place within the that is ribonucleoprotein complex. It gives us many clues nucleus after or simultaneously with transcription. Splicing to develop evolutionary strategies for cancer target therapy. is necessary for the eukaryotic messenger RNA (mRNA) Finally, about the molecular functions, there are enzyme before it can be translated into a correct protein. The spliceo- binding, protein binding and nucleotide binding, which are some is a dynamic intracellular macromolecular complex very different from the early stage bladder cancer. All the of multiple proteins and ribonucleoproteins (snRNPs). For evolutionary behaviors from early to late stage bladder cancer many eukaryotic introns, the spliceosome carries out the two let us reveal more hidden carcinogenesis mechanism. main functions of alternative splicing. First, it recognizes the intron-exon boundaries and second it catalyzes the cut-and- 3.5.PathwayAnalysisofBothEarlyandLateStageBladder paste reactions that remove introns and concatenate exons. Cancer. The only pathway to intersect between early and late The various spliceosomal machinery complex is formed from stage bladder cancer is the ubiquitin-mediated proteolysis 5 ribonucleo-protein (RNP) subunits, termed uridine-rich pathway (Table 6). This means it is the only housekeeping (U-rich) small nuclear RNP (snRNP), transiently associated pathway for bladder cancer and that the mechanisms of with more than 760 non-snRNPs splicing factors (RNA early and late stage bladder cancer are completely different. helicases, SR splicing factors, etc.) [46, 47]. Each spliceosomal We hypothesize that this may be a novel concept for target snRNP (U1, U2, U4, U5, and U6) consists of a uridine-rich therapy. Various other researches have never built a model in small nuclear RNA (snRNA) complexed with a set of seven accordance with the network markers at the different stages proteins known as canonical Sm core or SNRP proteins. The of cancer. Our results show that the network markers of early 󸀠 seven Sm proteins (B/B ,D1,D2,D3,E,F,andG)forma stage hit common mechanisms and fundamental pathways, core ring structure that surrounds the RNA. All Sm proteins such as cell cycle, cell proliferation, and Wnt signaling, among contain a conserved sequence motif in two segments (Sm1 others, which are implicated in various cancers. These provide 22 BioMed Research International clues in that early stage bladder cancer is active in many [3]D.S.Kaufman,W.U.Shipley,andA.S.Feldman,“Bladder related pathways and we can assume that it is an active cancer,” The Lancet,vol.374,no.9685,pp.239–249,2009. process to change the cell. In contrast, in the late stage of [4]A.M.Donaldson,J.G.Gonzalez,M.K.B.Parmar,andN. bladder cancer, the cells were inactive and close to silence. Donaldson, “The MRC superficial bladder cancer trial of intrav- This may mean that the cells are close to death. Should we esical mytomicin-c after complete surgical resection. Sequential attempt to save these cells, we should aim to focus on the statistical methods applied to survival data from a randomised ribosome and spliceosome pathways. Of course ubiquitin- clinical trial,” International Journal of Surgery,vol.7,no.5,pp. mediated proteolysis pathways are both active in early and 441–445, 2009. late stage cancer. [5] V. H. Cowling and M. D. Cole, “Mechanism of transcriptional activation by the Myc oncoproteins,” Seminars in Cancer Biol- ogy,vol.16,no.4,pp.242–252,2006. 4. Conclusions [6] M. Pacal and R. Bremner, “Insights from animal models on the origins and progression of retinoblastoma,” Current Molecular Bladder cancer is among the 10 most common forms of Medicine,vol.6,no.7,pp.759–781,2006. carcinoma in the USA and worldwide. It is a lethal disease [7] J. N. Contessa, J. Hampton, G. Lammering et al., “Ionizing like other cancers and understanding the carcinogenesis radiation activates Erb-B receptor dependent Akt and p70 S6 mechanism can help to develop new therapeutic strategy. kinase signaling in carcinoma cells,” Oncogene,vol.21,no.25, Identifying the PPI interface to develop small molecule pp.4032–4041,2002. inhibitors has become a new direction for targeted cancer [8] A. M. Codegoni, M. I. Nicoletti, G. Buraggi et al., “Molecu- therapy.Thisstudy,whichfollowsfromourpriorwork,ana- larcharacterisationofapanelofhumanovariancarcinoma lyzes the carcinogenesis mechanism from early to late stage xenografts,” European Journal of Cancer,vol.34,no.9,pp.1432– bladder cancer using a network-based biomarker evolution 1438, 1998. approach. Other research studies do not distinguish network [9] T. R. Golub, D. K. Slonim, P. Tamayo et al., “Molecular markers between these two stages of bladder cancer. Thus, our classification of cancer: class discovery and class prediction by approach is advantageous in that it can provide added insight gene expression monitoring,” Science,vol.286,no.5439,pp.531– into the significant network marker evolution of the carcino- 527, 1999. genesis process of bladder cancer. The network markers and [10] H. Han, D. J. Bearss, L. W. Browne, and et al, “Identification of their related pathways identified in early stage bladder cancer differentially expressed genes in pancreatic cancer cells using are mostly related to ordinary cancer mechanisms, which cDNA microarray,” Cancer Research,vol.62,pp.2890–2896, just show a highly active state of the early stage and cannot 2002. reveal additional novel results. All of these results should be [11] K.-Q. Liu, Z.-P. Liu, J.-K. Hao, L. Chen, and X.-M. Zhao, validatedinvivoorinvitrointhefuture.However,fromthe “Identifying dysregulated pathways in cancers from pathway two specific and significant pathways identified in late stage interaction networks,” BMC Bioinformatics,vol.13,no.1,article 126, 2012. bladder cancer, ribosome pathway and spliceosome pathway, we identified a novel result, which has potential to become a [12] H. Uramoto, K. Sugio, T. Oyama et al., “Expression of the p53 family in lung cancer,” Anticancer Research,vol.26,no.3,pp. targetforcancertherapy.Theonlycorepathwayinthesetwo 1785–1790, 2006. stages is the ubiquitin-mediated proteolysis pathway, which is a significant cue of carcinogenesis from early to late stage [13] E. A. Horvat, J. D. Zhang, U. Stefan, O. Sahin, and K. A. .Zweig, “Anetwork-based method to assess the statistical significance of bladder cancer. Applying our method to study more cancers mild co-regulation effects,” PLoS ONE,vol.8,ArticleIDe73413, and more classification groups (such as stage, age, ethics, and 2013. sex) will give us further insight into the various pathogenesis [14] Y.-C. Wang and B.-S. Chen, “A network-based biomarker mechanisms. approach for molecular investigation and diagnosis of lung cancer,” BMC Medical Genomics,vol.4,article2,2011. Conflict of Interests [15]A.A.Ivanov,F.R.Khuri,andH.Fu,“Targetingprotein-protein interactions as an anticancer strategy,” Trends in Pharmacologi- The authors declare that there is no conflict of interests cal Sciences,vol.34,no.7,pp.393–400,2013. regarding the publication of this paper. [16] L.Chen,R.Liu,Z.-P.Liu,M.Li,andK.Aihara,“Detectingearly- warning signals for sudden deterioration of complex diseases by dynamical network biomarkers,” Scientific Reports,vol.2,article Acknowledgment 342, 2012. The authors are grateful for the support provided by the [17] R. Liu, X. Wang, K. Aihara, and L. Chen, “Early diagnosis of Ministry of Science and Technology (NSC-102-2745-E-007- complex diseases by molecular biomarkers, network biomark- ers, and dynamical network biomarkers,” Medicinal Research 001-ASP). Reviews,vol.34,pp.455–478,2014. [18] R.Liu,X.Yu,X.Liu,D.Xu,K.Aihara,andL.Chen,“Identifying References critical transitions of complex diseases based on a single sample,” Bioinformatics,vol.30,no.11,pp.1579–1586,2014. [1] T. N. Seyfried and L. M. Shelton, “Cancer as a metabolic [19] C. Zhang, J. Wang, K. Hanspers, D. Xu, L. Chen, and A. R. disease,” Nutrition & Metabolism,vol.7,article7,2010. Pico, “NOA: a cytoscape plugin for network ontology analysis,” [2] http://www.cancer.gov/cancertopics/types/bladder. Bioinformatics, vol. 29, no. 16, pp. 2066–2067, 2013. BioMed Research International 23

[20] W.-J. Kim, E.-J. Kim, S.-K. Kim et al., “Predictive value of [40] E. J. Allenspach, I. Maillard, J. C. Aster, and W. S. Pear, “Notch progression-related gene classifier in primary non-muscle inva- signaling in cancer,” Cancer Biology and Therapy,vol.1,pp.466– sive bladder cancer,” Molecular Cancer, vol. 9, article 3, 2010. 476, 2002. [21] A. Chatr-Aryamontri, B.-J. Breitkreutz, S. Heinicke et al., “The [41] V.Bolos, J. Grego-Bessa, and J. L. de la Pompa, “Notch signaling BioGRID interaction database: 2013 update,” Nucleic Acids in development and cancer,” Endocrine Reviews,vol.28,no.3, Research,vol.41,no.1,pp.D816–D823,2013. pp. 339–363, 2007. [22] J. M. Bland and D. G. Altman, “Multiple significance tests: the [42] J. Schneikert and J. Behrens, “The canonical Wnt signalling Bonferroni method,” British Medical Journal,vol.310,article pathway and its APC partner in colon cancer development,” 170, no. 6973, 1995. Gut,vol.56,no.3,pp.417–425,2007. [23] R. Johansson, System Modeling and Identification,PrenticeHall, [43] R. Derynck, R. J. Akhurst, and A. Balmain, “ TGF-𝛽 signaling 1993. in tumor suppression and cancer progression (Nature Genetics [24] M. Pagano and K. Gauvreau, Principles of Biostatistics, 2000. (2001) 29 (117–129)),” Nature Genetics,vol.29,no.3,p.351,2001. [25] M. Kanehisa, “Molecular network analysis of diseases and drugs [44] L. Montanaro, D. Trere,´ and M. Derenzini, “Nucleolus, ribo- in KEGG,” Methods in Molecular Biology, vol. 939, pp. 263–275, somes, and cancer,” The American Journal of Pathology,vol.173, 2013. no. 2, pp. 301–310, 2008. [26] J.-I. Satoh, “Molecular network of microRNA targets in [45] L. Montanaro, D. Trere,´ and M. Derenzini, “Changes in ribo- Alzheimer’s disease brains,” Experimental Neurology,vol.235, some biogenesis may induce cancer by down-regulating the cell no. 2, pp. 436–446, 2012. tumor suppressor potential,” Biochimica et Biophysica Acta— Reviews on Cancer,vol.1825,no.1,pp.101–110,2012. [27] D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Systematic and integrative analysis of large gene lists using DAVID bioin- [46] M. S. Jurica and M. J. Moore, “Pre-mRNA splicing: awash in a formatics resources,” Nature Protocols,vol.4,no.1,pp.44–57, sea of proteins,” Molecular Cell,vol.12,no.1,pp.5–14,2003. 2009. [47] Z.Zhou,L.J.Licklider,S.P.Gygi,andR.Reed,“Comprehensive [28] D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Bioin- proteomic analysis of the human spliceosome,” Nature,vol.419, formatics enrichment tools: paths toward the comprehensive no.6903,pp.182–185,2002. functional analysis of large gene lists,” Nucleic Acids Research, [48] R. S. Pillai, M. Grimmler, G. Meister et al., “Unique Sm vol. 37, no. 1, pp. 1–13, 2009. core structure of U7 snRNPs: assembly by a specialized SMN [29]J.Wang,Q.Huang,Z.-P.Liuetal.,“NOA:anovelNetwork complex and the role of a new component, Lsm11, in histone Ontology Analysis method,” Nucleic Acids Research,vol.39,no. RNA processing,” Genes and Development,vol.17,no.18,pp. 13, p. e87, 2011. 2321–2333, 2003. [30] M. Fassan, E. J. Trabulsi, L. G. Gomella, and R. Baffa, “Targeted [49] L. Agranat-Tamir, N. Shomron, J. Sperling, and R. Sperling, therapies in the management of metastatic bladder cancer,” “Interplay between pre-mRNA splicing and microRNA biogen- Biologics,vol.1,no.4,pp.393–406,2007. esis within the supraspliceosome,” Nucleic Acids Research,vol. 42,no.7,pp.4640–4651,2014. [31] J. Camacho, Molecular Ontology: Principles and Recent Advan- [50] D. Kaida, H. Motoyoshi, E. Tashiro et al., “Spliceostatin A targets ces,2012. SF3b and inhibits both splicing and nuclear retention of pre- [32] P. Korkolopoulou, P. Christodoulou, A.-E. Konstantinidou, mRNA,” Nature Chemical Biology,vol.3,no.9,pp.576–583, E. Thomas-Tsagli, P. Kapralos, and P. Davaris, “Cell cycle 2007. regulators in bladder cancer: a multivariate survival study with [51] Y. Kotake, K. Sagane, T. Owa et al., “Splicing factor SF3b as a emphasis on p27Kip1,” Human Pathology,vol.31,no.6,pp.751– target of the antitumor natural product pladienolide,” Nature 760, 2000. Chemical Biology,vol.3,no.9,pp.570–575,2007. [33] S. C. Doherty, S. R. McKeown, C. Stephen Downes et al., “Cell [52] R. J. van Alphen, E. A. C. Wiemer, H. Burger, and F. A. L. M. cycle checkpoint function in bladder cancer,” Journal of the Eskens, “The spliceosome as target for anticancer treatment,” National Cancer Institute,vol.95,no.24,pp.1859–1868,2003. British Journal of Cancer,vol.100,no.2,pp.228–232,2009. [34] G. H. Williams and K. Stoeber, “The cell cycle and cancer,” Journal of Pathology, vol. 226, no. 2, pp. 352–364, 2012. [35] J. Lukas, J. Bartkova, and J. Bartek, “Convergence of mitogenic signalling cascades from diverse classes of receptors at the cyclin D-cyclin-dependent kinase-pRb-controlled G1 check- point,” Molecular & Cellular Biology,vol.16,no.12,pp.6917– 6925, 1996. [36] T.Grigoryan,P.Wend,A.Klaus,andW.Birchmeier,“Decipher- ing the function of canonical Wnt signals in development and disease: conditional loss- and gain-of-function mutations of 𝛽- catenin in mice,” Genes and Development,vol.22,no.17,pp. 2308–2341, 2008. [37] S. Majid, S. Saini, and R. Dahiya, “Wnt signaling pathways in urological cancers: past decades and still growing,” Molecular Cancer, vol. 11, article 7, 2012. [38] I. Ahmad, The role of Wnt signalling in urothelial cell carcinoma [Ph.D. thesis], University of Glasgow, 2011. [39] H. Lodish et al., Molecular Cell Biology,2013. International Journal of Peptides

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