RESUMO
INTRODUCTION: High-grade prostatic intraepithelial neoplasia (HGPIN) is a recognized precursor stage of PCa. Men who present HGPIN in a first prostate biopsy face years of active surveillance including repeat biopsies. This study aimed to identify non-invasive prognostic biomarkers that differentiate early on between indolent HGPIN cases and those that will transform into actual PCa. METHODS: We measured the expression of 21 candidate mRNA biomarkers using quantitative PCR in urine sediment samples from a cohort of 90 patients with initial diagnosis of HGPIN and a posterior follow up of at least two years. Uni- and multivariate statistical analyses were applied to analyze the candidate biomarkers and multiplex models using combinations of these biomarkers. RESULTS: PSMA, PCA3, PSGR, GOLM, KLK3, CDH1, and SPINK1 behaved as predictors for PCa presence in repeat biopsies. Multiplex models outperformed (AUC = 0.81-0.86) the predictive power of single genes, including the FDA-approved PCA3 (AUC = 0.70). With a fixed sensitivity of 95%, the specificity of our multiplex models was of 41-58%, compared to the 30% of PCA3. The PPV of our models (30-38%) was also higher than the PPV of PCA3 (27%), suggesting that benign cases could be more accurately identified. Applying statistical models, we estimated that 33% to 47% of repeat biopsies could be prevented with a multiplex PCR model, representing an easy applicable and significant advantage over the current gold standard in urine sediment. DISCUSSION: Using multiplex RTqPCR-based models in urine sediment it is possible to improve the current diagnostic method of choice (PCA3) to differentiate between benign HGPIN and PCa cases.