INTRODUCTION AND OBJECTIVE: A previously reported Prostatype algorithm (P-score) incorporating a three-gene signature (IGFBP3, F3, VGLL3) with clinical parameters (Gleason score, PSA and T-stage) was developed and validated in 596 historical patients selected from a population-based cohort. This study aims to retrospectively validate Prostatype algorithms capability to predict the risk of metastasis and prostate cancer (PCa) specific mortality in new diagnosed PCa patients.METHODS: All 716 patients included were diagnosed with PCa using core needle biopsy from January 2008 to December 2010 at Skåne University hospital Malm€ o and Lund Sweden with a follow-up time 8-10 years after diagnosis. Gene expression was assessed by RT-qPCR on all core needle biopsies from diagnosis. A predefined genes score was computed from the expression of the three-gene signature and then combined with clinical parameters (Gleason score, PSA and T-stage) to calculate the P-score (units: 0-15). Cox proportional hazard regression models were used to evaluate the association of gene signature with clinical outcomes. The risk stratification of the score system was evaluated by Kaplan-Meier curve. The receiver operating characteristic analysis and decision-curve analysis were used to assess the prediction accuracy and the net-benefit, respectively. Analysis results were compared to known risk scores such as DAmico.RESULTS: Total 365 had valid data, 316 patients were without metastasis at diagnosis, 47 had secondary metastasis during follow up and 33 died due to PCa. All patients that died had a high P-score. The gene signature added significant prognostic information for metastasis (p<0.01) and PCa-specific death (p<0.01). One unit change in P-score has a hazard ratio (HR) of 1.6(95% CI: 1.41-1.82, p<0.0001) for predicting PCa death and HR of 1.48 (95% CI: 1.35-1.63, p<0.0001) for predicting PCa-specific death. P-score was significantly better than DAmico for predicting end-point PCa death (0.89 v.s 0.77, p<0,0001) and metastasis (0.86 v.s 0.77, p<0,0001), unrelated to which treatment was given. Gene signature alone showed a similar prediction power as D'Amico for both end-points. P-score has a much higher net-benefit than D'Amico as revealed by decision curve analysis.CONCLUSIONS: P-score gave improved prognostic evaluation for metastasis and death in PCa compared to other known risk indicators. P-score can be a beneficial tool when treatment decision is made for patients with localised PCa.
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