Development and validation of tryptophan metabolism-related risk model and molecular subtypes for predicting postoperative biochemical recurrence in prostate cancer

Background: Biochemical recurrence (BCR) following radical prostatectomy (RP) remains a major challenge in prostate cancer (PCa) management. Tryptophan metabolism plays a pivotal role in tumor progression and immune modulation. This study aimed to develop and validate a tryptophan metabolismrelated risk model and molecular subtypes to predict BCR in PCa patients after RP. Methods: The Cancer Genome Atlas-Prostate Adenocarcinoma (TCGA-PRAD) dataset, including 421 PCa patients, was analyzed to identify key tryptophan metabolism-related genes (TMRGs) using differential expression, univariate Cox, and the least absolute shrinkage and selection operator (LASSO) regression analyses. The tryptophan metabolism-related risk model was constructed through multivariate Cox regression, and tryptophan metabolism-related molecular subtypes were established using consensus clustering. External validation was conducted using an independent dataset, while immunohistochemistry (IHC) and single-cell sequencing further confirmed TMRG expression patterns and their roles in the tumor Results: The tryptophan metabolism-related risk model and molecular subtypes effectively stratified PCa patients into low- and high-risk groups or two molecular subtypes. High-risk PCa patients (n=211) and those in Cluster 1 (n=261) exhibited significantly poorer biochemical recurrence-free survival (BRFS) and distinct clinicopathological features, immune infiltration profiles, and TME characteristics. External validation confirmed the robustness of the tryptophan metabolism-related risk model and molecular subtypes. IHC and single-cell sequencing highlighted the expression patterns of TMRGs and their regulatory roles in the TME. Conclusions: This study established and validated tryptophan metabolism-related risk scores and molecular subtypes as reliable predictors of BCR in PCa patients after RP. These findings provide a foundation for personalized follow-up and treatment strategies, contributing to improved clinical outcomes in PCa management.