Exploration of Prognostic Biomarkers of Muscle-Invasive Bladder Cancer (MIBC) by Bioinformatics.

Abstract

We aimed to discover prognostic factors of muscle-invasive bladder cancer (MIBC) and investigate their relationship with immune therapies. Online data of MIBC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO) database. Weighted gene co-expression network analysis (WGCNA) and univariate Cox analysis were applied to classify genes into different groups. Venn diagram was used to find the intersection of genes, and prognostic efficacy was proved by Kaplan-Meier analysis. Heatmap was utilized for differential analysis. Riskscore (RS) was calculated according to multivariate Cox analysis and evaluated by receiver operating characteristic curve (ROC). MIBC samples from TCGA and GEO were analyzed by WGCNA and univariate Cox analysis and intersected at 4 genes, CLK4, DEDD2, ENO1, and SYTL1. Higher SYTL1 and DEDD2 expressions were significantly correlated with high tumor grades. Riskscore based on genes showed great prognostic efficiency in predicting overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in TCGA dataset (P < .001). The area under the ROC curve (AUC) of RS reached 0.671 in predicting 1-year survival and 0.653 in 3-year survival. KEGG pathways enrichment filtered 5 enriched pathways. xCell analysis showed increased T cell CD4+ Th2 cell, macrophage, macrophage M1, and macrophage M2 infiltration in high RS samples (P < .001). In immune checkpoints analysis, PD-L1 expression was significantly higher in patients with high RS. We have, therefore, constructed RS as a convincing prognostic index for MIBC patients and found potential targeted pathways.