Identification of Differentially Expressed Genes in Urinary Bladder Cancer by Meta-Analysis by Using a Bioinformatics Tool

Background: Bladder cancer is the ninth most prevalent malignant disease globally, which ranges from mild with low mortality rate to extremely high grade tumors associated with high mortality rate. The present study was aimed to identify the key genes associated with bladder cancer progression and later it may also be used as marker in the diagnosis and prognosis.

Materials and methods: The GSE3167, GSE7476, GSE68928 and GSE31189 data expression profiles were downloaded from the Gene Expression Omnibus (GEO) which includes 124 bladder cancer samples and 66 normal bladder tissues. The MGEx-TDB tool was used to analyse and find out the differentially expressed genes (DEGs), and the Gene Ontology (GO) functional annotation and KEGG pathway analysis were performed. Protein-protein Interaction (PPI) network ad based on centrality analysis hub genes was identified and were analysed towards in the diagnosis and prognosis of bladder cancer.

Results: In total, 475 differentially expressed genes including 196 up regulated genes and 279 down regulated genes were obtained from the four data sets analysis. GO analysis of the DEGs revealed that the up regulated genes were associated with mitotic nuclear division, cell division and apoptotic process. The down regulated genes were coupled with cell adhesion, MAPK cascade, cell differentiation. KEGG pathway analysis has shown that the up regulated genes were enriched in the pathways such as cell cycle, p53 signaling pathway and FoxO signaling pathway. The down regulated genes were enriched in pathways such as Focal adhesion, MAPK signaling pathway, platelet activation, proteoglycans in cancer, and pathways in cancer. From the constructed PPI network, based on higher degree, the hub genes were identified. CDK1, CCNB2, MAPK14, CDC20, STAT1 and NUSAP1 from the up regulated genes and FYN, UBC and ADAM22 from the down regulated genes.

Conclusion: This study enabled us to identify the key genes and the associated pathways. This will help us to understand the mechanism behind the tumor progression and its diagnosis.


Mylsamy S, Devaki K

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