Research Associate Case Western Reserve University
Introduction: Bladder cancer is a heterogeneous disease with high recurrence rates. The current prognosticaion depends on tumor stage and grade and there is a need for predictive biomarkers that can distinguish between progressive versus non-progressive disease. The prognosis of bladder cancer remains challenging due of lack of appropriate biomarker associated with the progression of bladder cancer. The aim of the present study was to identify, generate and clinically validate a novel gene signature to predict prognosis of bladder cancer.
Methods: We explored and analyzed the transcriptomic gene expression of bladder cancer patient datasets consisting of non-muscle invasive (NMIBC) and muscle invasive subtype (MIBC) from NCBI GEO (GSE154261, GSE57813, and GSE37317). The datasets were analyzed using GEO2R and Limma R packages. Pathway enrichment analysis of differentially expressed genes (DEGs) and between the NMIBC and MIBC group were analyzed by using the ingenuity pathway analysis (IPA), metascape web based portal and cytoscape. The above finding were functionally validated in human bladder cancer cell lines: RT4 (transitional cell papilloma), J82 (transitional cell carcinoma), HT1197 (bladder carcinoma), and 253JB-V (metastatic phenotype) and compared with the relative expression of UROsta (benign) urothelial cells.
Results: A total of 1516 DEGs were identified between non-muscle invasive and muscle invasive bladder cancer specimens. To identify genes of prognostic value, we performed Gene Ontology (GO) and Kyoto Gene and Genomic encyclopedia (KEGG) analysis. A total of seven genes including CDKN2A, CDC20, CTSV, FOXM1, MAGEA6, KRT23, and S100A9 were confirmed with strong prognostic values in bladder cancer and validated by qRT-PCR conducted in various human bladder cancer cells representing stage-specific disease progression. ULCAN, human protein atlas and The Cancer Genome Atlas datasets were used to confirm the predictive value of these genes in bladder cancer progression. Moreover, Kaplan-Meier analysis and Cox hazard ratio analysis were performed to determine the prognostic role of these genes. Univariate analysis performed on a validation set identified a 3-panel gene set viz. CDKN2A, CTSV and FOXM1 with 95.5% sensitivity and 100% specificity in predicting bladder cancer progression.
Conclusions: Our study screened and confirmed a 3-panel biomarker that could accurately predict the progression and prognosis of bladder cancer.
Source of Funding: Supported by Carter Kissell Endowment funds to SG.
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