Screening and identification of potential prognostic biomarkers in bladder urothelial carcinoma: Evidence from bioinformatics analysis

Abstract

Bladder urothelial carcinoma (BLCA) is a very much invasive urological malignant tumor that results in reduced patient survival. At current, the mechanism of BLCA metastasis is not clear. This study was aimed to explore the prognostic and diagnostic related biomarkers in BLCA and guide the therapy. First, we acquired data of gene expression in non-muscle invasive bladder cancer samples (N = 15) and muscle invasive bladder cancer samples (N = 15) from the Gene Expression Omnibus (GEO) database. Integrated analysis was used to explore differentially expressed genes (DEGs) (up and down regulated genes) between non invasive BLCA samples and invasive BLCA samples. Pathway enrichment analyses and Gene ontology (GO) of DEGs were performed. DEGs were used to construct a protein–protein interaction (PPI) network using Protein Interaction Network Analysis platform (PINA) database, and the modules from PPI network were verified by Cytoscape software and hub genes were extracted. Furthermore, gene regulatory networks were depicted based on miRNAs and TFs with interest target genes. The survival analysis, expression analysis, stage analysis, mutation analysis, immunohistochemical (IHC) analysis, ROC analysis and RT-PCR for the integrated expression score was applied to determine the diagnostic ability of the candidate biomarkers. In total, 900 DEGs that consisted of 455 up regulated and 445 down regulated genes were screened out. The differentially expressed genes were enriched in stearate biosynthesis, metabolic pathways, acetate conversion to acetyl-CoA, focal adhesion, oxoacid metabolic process, mitochondrial matrix, circulatory system development and extracellular matrix, of which pathways and GO terms were associated in the BLCA development. PPI network analysis, demonstrated the interactions between those DEGs, and 10 hub genes (P4HB, APEX1, SIRT5, SRP68, HIP1R, MYC, A2M, MAP1B, HIPK2 and TCF) were extracted from the network and verified by survival analysis, expression analysis, stage analysis, mutation analysis, immunohistochemical (IHC) analysis, ROC analysis, RT-PCR and immune infiltration analysis.. In this study, we highlighted mechanisms underlying the control of BLCA molecular signatures by TFs and miRNAs which their alteration reduce the chance of survival rate in BLCA. These results revealed that the DEGs may serve as candidate essential genes during BLCA metastasis. The 10 hub genes, including P4HB, APEX1, SIRT5, SRP68, HIP1R, MYC, A2M, MAP1B, HIPK2 and TCF, may serve as promising prognostic biomarkers in BLCA metastasis. In conclusion, the present study identifies DEGs and hub genes that may be involved in poor prognosis and early recurrence of BLCA. The expression levels of P4HB, APEX1, SIRT5, SRP68, HIP1R, MYC, A2M, MAP1B, HIPK2 and TCF are of high prognostic value, and may help us understand better the underlying carcinogenesis or progression of BLCA. Further studies are required to elucidate molecular pathogenesis and alteration in signaling pathways for these genes in BLCA.