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Bladder cancer (BC) is a highly malignant and malevolent tumor whose incidence and mortality worldwide remains high. This paper aimed to identify the differentially expressed genes (DEGs) between normal and primary bladder cancer samples, which might be of prognostic value, and evaluate their association to clinical outcomes (survival) in patients with primary bladder cancer. A sample of 256 gene expression profiles of which 10 were normal bladder mucosae whereas 165 were primary bladder cancer tissues were downloaded from the gene expression omnibus (GEO). The limma package in R was used to perform screening for DEGs after variance reduction through quantile normalization and log2 transformation. Gene Ontology (GO), KEGG enrichment analysis, and Network analysis were performed to determine the functions of these DEGs. Cox proportional hazard regression model was then used to determine the association between the top selected DEGs and overall primary bladder cancer survival. Selected genes were then encoded as either low, middle or high expression and Kaplan-Meier plots then used to visualize the effect of these expression levels for the selected five genes on bladder cancer survival probability. This study identified a total of 5, 979 DEGs where 2, 878 were upregulated, 3, 101 were downregulated and 37, 169 were not significantly expressed differently. Cox proportional hazard stepwise regression models revealed a number of DEGs that have a statistically significant association to bladder cancer. The selected five DEGs associated with survival were EDNRB (HR=1.9858, 95%CI 1.4965–2.6350), COL14A1 (HR=1.5335, 95%C1 1.1670–2.0151), MOXD1 (HR=1.4878, 95%CI 1.1070–1.9996), FAM107A (HR=1.4970, 95%CI 1.1162–2.0077), and REM1 (HR=1.5999, 95%CI 1.2422–2.0605). After encoding these genes as either low, middle, or high expression, high expression of both EDNRB, COL14A1, FAM107A, and REM1 were found to result into statistically significant reductions in survival probabilities of patients (p0.05). The genes found in this study could provide useful insight in the understanding of the association between the identified DEGs and primary bladder cancer survival, which can further inform future studies and clinical therapies.

Differential Expression Analysis for the Identification of Survival Associated Genes in Primary Bladder Cancer using Microarray Data