Evaluation of a gene expression panel for prognostic stratification in high-risk prostate cancer

INTRODUCTION: Prostate cancer exhibits marked biological heterogeneity, making prognostic stratification essential for therapeutic decision-making. Although many patients with high-risk disease respond to curative treatment, a subset develops biochemical recurrence, highlighting the need for biomarkers capable of early identification of poorer prognosis. OBJECTIVE: To evaluate the expression of the genes KLK4, KLK14, KLK15, CDH1, SPOP, PTEN, MUC1, TSPAN1, and EZH2 as prognostic biomarkers in patients with high-risk prostate cancer, and to correlate their expression with clinical and pathological variables. METHODS: This retrospective study included 149 patients classified as high risk according to D'Amico criteria who underwent radical prostatectomy. Gene expression was assessed by quantitative real-time PCR (qRT-PCR) using RNA extracted from surgical specimens. Associations between gene expression, biochemical recurrence, and clinicopathological variables were analyzed. RESULTS: Isolated expression of the evaluated genes showed no statistically significant association with biochemical recurrence. Patients without recurrence exhibited higher mean expression levels of SPOP, KLK4, KLK14, KLK15, PTEN, TSPAN1, and EZH2, whereas CDH1 showed higher expression in recurrent cases, without statistical significance. KLK14 expression was significantly higher in patients with PSA ≥10 ng/mL (p = 0.047). TSPAN1 significantly differentiated tumors with Gleason score ≥8 (p = 0.031). No statistically significant association was observed between biochemical recurrence and prostate-specific antigen (PSA) level, Gleason score, or pathological stage within this cohort. CONCLUSION: Isolated gene expression was not a reliable predictor of biochemical recurrence in high-risk prostate cancer. However, expression patterns observed for KLK14 and TSPAN1 suggest potential prognostic relevance. Integrating molecular markers with established clinical parameters may improve prognostic stratification and personalized management.