Metabolic syndrome-related prognostic index: Predicting biochemical recurrence and differentiating between cold and hot tumors in prostate cancer.

Background: The prostate, as an endocrine and reproductive organ, undergoes complex hormonal and metabolic changes. Recent studies have shown a potential relationship between metabolic syndrome and the progression and recurrence of prostate cancer (PCa). This study aimed to construct a metabolic syndrome-related prognostic index (MSRPI) to predict biochemical recurrence-free survival (BFS) in patients with PCa and to identify cold and hot tumors to improve individualized treatment for patients with PCa. Methods: The Cancer Genome Atlas database provided training and test data, and the Gene Expression Omnibus database provided validation data. We extracted prognostic differentially expressed metabolic syndrome-related genes (DEMSRGs) related to BFS using univariate Cox analysis and identified potential tumor subtypes by consensus clustering. The least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression were used to construct the MSRPI. We further validated the predictive power of the MSRPI using KaplanMeier survival analysis and receiver operating characteristic (ROC) curves, both internally and externally. Drug sensitivity was predicted using the half-maximal inhibitory concentration (IC50). Finally, we explored the landscape of somatic mutations in the risk groups. Results: Forty-six prognostic DEMSRGs and two metabolic syndrome-associated molecular clusters were identified. Cluster 2 was more immunogenic. Seven metabolic syndrome-related genes ( Conclusion: The MSRPI developed in this study was able to predict biochemical recurrence in patients with PCa and identify cold and hot tumors. MSRPI has the potential to improve personalized precision treatment.