Computational Identification and Validation of Metabolic Cell Death-Related Prognostic Biomarkers for Personalized Treatment Strategies in Prostate Cancer.

Abstract

Prostate cancer (PCa) is a prevalent malignancy characterized by metabolic dysregulation and varied prognosis. Identifying prognostic biomarkers related to metabolic cell death could enhance risk stratification and treatment strategies. The purpose of this study was to identify prognostic genes associated with metabolic cell death in PCa and formulate a risk model for improved patient stratification. We identified genes that exhibit differential expression in The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) cohort (n = 394), with validation using GSE70769 (n = 92) and RT-qPCR on tissue samples from 5 patients. Candidate genes were intersected with metabolic cell death-related genes to identify prognostic markers. Independent prognostic factors were determined utilizing univariate and multivariate Cox regression analyses (p < 0.05, HR ≠ 1). A nomogram was designed, and the validation of gene expression was carried out using RT-qPCR on tissue samples from five PCa patients. A total of 78 candidate genes were identified, with ASNS and ZNF419 emerging as independent prognostic factors. The gene-based risk model successfully stratified patients into high- and low-risk groups, demonstrating correlations with overall survival and clinicopathological features, while also revealing significant differences in immune cell infiltration patterns through immune microenvironment analysis. Additionally, somatic mutation analysis indicated TP53, TTN, and SPOP as frequently mutated genes. This study identifies ASNS and ZNF419 as novel prognostic biomarkers in PCa, contributing to improved risk stratification and personalized treatment strategies. Further investigation into their functional roles may provide insights into therapeutic targets for PCa management.