Construction of a survival model for predicting biochemical recurrence of prostate cancer based on propionate metabolism-related genes

Background: About 20-40% of prostate cancer (PCa) develop biochemical recurrence (BCR) after surgery, and propionate metabolism may contribute to tumor progression. BCR remains a major clinical challenge in PCa, as current tools based on histopathology and prostate-specific antigen (PSA) fail to capture the molecular heterogeneity driving the disease. While metabolic reprogramming is known to facilitate post-treatment adaptation, the specific role of propionate metabolism in this context remains largely unexplored. Therefore, this study aimed to systematically investigate propionate metabolism-related genes (PMRGs) to develop a novel prognostic model for the improved early prediction of recurrence. Methods: In this study, The Cancer Genome Atlas-Prostate Adenocarcinoma (TCGA-PRAD), GSE70770 and 412 PMRGs were employed. Differentially expressed genes (DEGs) in PCa and control and DEGs2 in BCR and no BCR samples obtained by differential analysis were intersected with PMRGs to get candidate genes. After Cox and least absolute shrinkage and selection operator (LASSO) regression analyses, biomarkers were identified to construct risk models. Results: were identified to construct risk model after a series of analyses. Meanwhile, the nomogram for risk score can effectively predict BCR-free recurrence in PCa patients. Besides, 16 signaling pathways were significantly associated with riskScore, such as cell cycle and DNA replication. In immune-related analysis, six immune cells were significantly associated with the biomarkers. Furthermore, Wee1 Inhibitor_1046, Paclitaxel_1080, etc. were therapeutic drugs for PCa patients. Finally, the expression trend of four biomarkers was confirmed in clinical samples. Conclusions: In this study, PMRGs were regarded as biomarkers in PCa for risk model construction, which suggest that propionate metabolism represents a biologically relevant axis in PCa recurrence and may offer a novel framework for biomarker-driven risk assessment.