Gene network screening of bladder cancer via modular analysis

BACKGROUND: Bladder cancer (BC) is one of the most common cancers of the urinary system. Negative regulation of apoptotic pathways is of the most significant biological process in cancer. More accurate tumor characterization and stratification of BC patients for selection of more appropriate treatments are required. METHODS: The data for this study are from the National Center for Biotechnology Information (NCBI)’s Online Mendelian Inheritance in Man (OMIM) database. Disease-associated genes were performed via multiple text-based Searching in Agilent Literature Search software version 3.2.2. MCODE version 1.32 was used for computational analysis of network for the gene complex detection. Genes with common biological processes or pathways were divided into the same module. DAVID was used for Gene ontology (GO) and pathway analysis. The OS time of hub gene expression was analyzed by GEPIA. The study used Pearson Correlation Coefficient for correlated calculation of the hub genes in the same module (Bladder Urothelial Carcinoma samples compared with normal samples). We enriched the modules and predict the regulated miRNAs by Cluepedia. Interactions within each pathway can be investigated and new potential associations are revealed through gene/miRNA enrichments. RESULTS: A total of 187 BC-associated genes were got from OMIM and used for network construction. A total of seventy-five modules were found in the network. EGFR, AR, MET, RELA, TP53, TSG101 are hub genes (edges above 10) of the largest 3 modules. The results demonstrate that BC patients with low-expressed TSG101 have longer OS, and are associated with TP53. Low-expressed RELA and over-expressed AR patients have a higher survival time. Low-expressed TSG101 patients have a longer survival time. CONCLUSIONS: In our study, we found that miRNA17, miRNA20a, miRNA15a, has-let-7b and miRNA16 were miRNAs regulating the top 3 modules.