Several studies have demonstrated the usefulness of monitoring an RNA transcript in urine, such as PCA3, for prostate cancer (PCa) diagnosis. PCa screening would benefit from additional biomarkers of higher specificity and could be used in conjunction with prostate-specific antigen (PSA) testing, in order to better determine biopsy candidates. We used urine sediments after prostate massage (PM) from 215 consecutive patients, who presented for prostate biopsy. We tested whether prostate-specific G-protein coupled receptor (PSGR), a biomarker previously described to be over-expressed in PCa tissue, could also be detected by quantitative real-time PCR in post-PM urine sediment. We combined these findings with prostate cancer gene 3 (PCA3), the current gold standard for PCa diagnosis in urine, to test if a combination of both biomarkers could improve the sensitivity of PCA3 alone. By univariate analysis we found that PSGR and PCA3 were significant predictors of PCa. Receiver operator characteristic curve analysis and its multivariate extension, multivariate ROC (MultiROC), were used to assess the outcome predictive values of the individual and the paired biomarkers. We obtained the following area under the curve values: PSA (0.602), PSGR (0.681), PCA3 (0.656), and PSGRvPCA3 (0.729). Then, we tested whether a combination of PSGR and PCA3 could improve specificity by fixing the sensitivity at 95%. We obtained specificities of 15% (PSGR), 17% (PCA3), and 34% (PSGRvPCA3). A multiplexed model including PSGR and PCA3 improves the specificity for the detection of PCa, especially in the area of high sensitivity. This could be clinically useful for determining which patients should undergo biopsy. © 2010 Wiley-Liss, Inc.