Fig. 1

ROC for the predictive accuracy for detecting cancer using uniplex and multiplex regression models for discriminating TA and NTA biopsy negative cores (two biopsies). When pan-biomarkers used alone (Max_CAV1 CG10, Max_EVX1 CG1, Max_PLA2G16 CG5, Max_SPAG4 CG2, Min_FGF1 CG3, and Min_NCR2 CG2), the predictive accuracy was 0.747, p = 0.004 (solid curve). Clinical features (age and LogPSA) had predictive accuracy AUC 0.631, p = 0.005 (dashed and dotted curve). Multiplex model incorporating pan-biomarkers and clinical features (dashed curve) had highest predictive accuracy (AUC 0.815, p < 0.0001) for discriminating TA vs NTA biopsy negative cores