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Clinicians currently use simple cut-points, such as serum prostate-specific antigen (PSA) >or=4 ng/ml, to decide whether to recommend further work-up for prostate cancer (PCa). As an alternative strategy, we evaluated multivariable models giving probabilities of a PCa diagnosis based on PSA and several circulating novel biomarkers. We measured total PSA, free PSA (fPSA), fPSA subfractions (single-chain fPSA-I and multichain fPSA-N), total human glandular kallikrein 2 (hK2) and full-length and cleaved forms of soluble urokinase plasminogen activator receptor (suPAR) in pretreatment serum from 355 men referred for prostate biopsy. Age and total PSA were combined in a "base" regression model to predict biopsy outcome. We then compared this base model to models supplemented by various combinations of circulating markers, using concordance index (AUC) to measure diagnostic discrimination. PCa prediction was significantly enhanced by models supplemented by measurements of suPAR fragments and fPSA isoforms. Addition of these markers improved bootstrap-corrected AUC from 0.611 for a cut-point and 0.706 for the base model to 0.754 for the full model (p=0.005). This improved diagnostic accuracy was also seen in subanalysis of patients with PSA 2-9.99 ng/ml and normal findings on DRE (0.652 vs. 0.715, p=0.039). In this setting, hK2 did not add diagnostic information. Measurements of individual forms of suPAR and PSA isoforms contributed significantly to discrimination of men with PCa from those with no evidence of malignancy.

Original publication




Journal article


Int J Cancer

Publication Date





1499 - 1504


Aged, Area Under Curve, Biomarkers, Tumor, Biopsy, Humans, Male, Middle Aged, Neoplasm Staging, Patient Selection, Prostate-Specific Antigen, Prostatic Neoplasms, Receptors, Cell Surface, Receptors, Urokinase Plasminogen Activator, Tissue Kallikreins