Robust Prediction of Prognosis and Immunotherapy Response for Bladder Cancer through Machine Learning Algorithm

Abstract

The important roles of machine learning and ferroptosis in bladder cancer (BCa) are still poorly understood. In this study, a comprehensive analysis of 19 ferroptosis-related genes (FRGs) was performed in 1322 patients with BCa from four independent patient cohorts and a pan-cancer cohort of 9824 patients. Twelve FRGs were selected through machine learning algorithm to construct the prognosis model. Significantly differential survival outcomes (hazard ratio (HR) = 2.09, 95% confidence interval (CI): 1.55–2.82, p < 0.0001) were observed between patients with high and low ferroptosis scores in the TCGA cohort, which was also verified in the E-MTAB-4321 cohort (HR = 4.71, 95% CI: 1.58–14.03, p < 0.0001), the GSE31684 cohort (HR = 1.76, 95% CI: 1.08–2.87, p = 0.02), and the pan-cancer cohort (HR = 1.15, 95% CI: 1.07–1.24, p < 0.0001). Tumor immunity-related pathways, including the IL-17 signaling pathway and JAK-STAT signaling pathway, were found to be associated with the ferroptosis score in BCa through a functional enrichment analysis. Further verification in the IMvigor210 cohort revealed the BCa patients with high ferroptosis scores tended to have worse survival outcome after receiving tumor immunotherapy. Significantly different ferroptosis scores could also be found between BCa patients with different reactions to treatment with immune checkpoint inhibitors.