Abstract
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 (CSF3R, TMEM132A, STAB1, VIM, DUOXA1, PILRB, and SLC2A4) were used to construct risk equations. The high-risk index was significantly associated with a poor BFS, which was also validated in the validation cohort. The area under the ROC curve (AUC) for BFS at 1-, 3-, and 5- year in the entire cohort was 0.819, 0.785, and 0.772, respectively, demonstrating the excellent predictive power of the MSRPI. Additionally, the MSRPI was found to be an independent prognostic factor for BFS in PCa. More importantly, MSRPI helped differentiate between cold and hot tumors. Hot tumors were associated with the high-risk group. Multiple drugs demonstrated significantly lower IC50 values in the high-risk group, offering the prospect of precision therapy for patients with PCa. 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.