Urinary PSA-ZINC biomarker outperforms standard of care in early detection of prostate cancer.

Background: Urine is a promising biological fluid for prostate cancer (PCa) diagnostics due to its non-invasive collection and wide range of biomarkers. The aim of this study was to assess the role of urinary PSA (uPSA) and urinary Zinc (uZinc) as biomarkers for the diagnosis of PCa in combination with routine parameters of standard of care (SOC - blood PSA, abnormal DRE, age) and MRI in patients candidates for prostate biopsy.

Methods: Urine samples after prostatic massages were collected from men with suspected PCa scheduled for prostate biopsy. Quantification of uPSA was performed by ECLIA platform and confirmed by ELISA assay, while uZinc measurement was evaluated by ICP-MS and confirmed by colorimetric in vitro assay. Six multivariate logistic regression analysis were performed to assess diagnostic performance of uPSA and uZinc (urine), SOC and MRI alone, and combination of MRI+SOC, MRI+urine and SOC+MRI+urine. The discriminative power of the logistic models was assessed by calculating the area under the receiver operating characteristic (ROC) curves (AUC).

Results: Two hundred thirty-eight patients were included in the analysis; 145 of them were diagnosed with PCa. Urine test showed a better discrimination of HS from CP, in respect of uPSA and uZinc alone, both for PCa of any grade and Gleason Score ≥7 (4+3) (AUC 0.804 and 0.823 respectively). ROC curve combining SOC+MRI+urine showed an AUC=0.882, that is statistically different from SOC or MRI alone, or MRI+SOC (P=0.0001, P=0.0001, and P=0.008 respectively). PCa risk algorithm designed considering SOC+MRI+urine results in potential reduction of 57% of unnecessary biopsies compared to the current standard parameters.

Conclusions: The loss of uPSA and Zinc production and secretion during neoplastic transformation of the prostate could potentially represent a hallmark of PCa. Its combination with age, PSA and DRE, as well as with mpMRI could represent an interesting approach to improve the diagnostic accuracy of PCa.