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Updated: Mar 6, 2026
Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
ProPSA and the Prostate Health Index as predictive markers for aggressiveness in low-risk prostate cancer-results
I Heidegger1, H Klocker1, R Pichler1
1Department of Urology, Medical University Innsbruck, Innsbruck, Austria.
Background:
One of the major challenges in prostate cancer (PCa) treatment is distinguishing insignificant PCa from those forms that need active treatment. We evaluated the impact of PSA isoforms on risk stratification in patients with low-risk PCa as well as in active surveillance (AS) candidates who underwent radical prostatectomy.
Methods:
A total of 112 patients with biopsy confirmed Gleason score (GS) 6 PCa of four different international institutions were prospectively enrolled in the study. Blood withdrawal was performed the day before radical prostatectomy. In addition, patients were classified according to the EAU and NCCN criteria for AS candidates. PSA, free PSA (fPSA) and proPSA were measured using dual monoclonal antibody sandwich immunoassays. In addition, the Prostate Health Index (PHI=proPSA/fPSA × √PSA) was calculated. Final histology of the radical prostatectomy specimens was correlated to PSA, its isoforms and PHI.
Results:
Serum proPSA levels were significantly elevated in those patients with an upgrade in final histology (GS⩾7). In addition, higher proPSA levels were predictive for extraprostatic extension (⩾pT3a) as well as for positive surgical margins. Interestingly, PHI had an even higher predictive power when compared with proPSA alone concerning GS upgrading, extraprostatic extension and surgical margins in both the total and the AS patient group.
Conclusion:
We showed in a multicenter study that proPSA is a valuable biomarker to detect patients with aggressive PCa in a cohort of GS 6 patients, who would benefit from active tumor therapy. Combining proPSA with the standard markers PSA and fPSA using PHI further increases the predictive accuracy significantly. Moreover, our data support the use of PHI for monitoring PCa patients under AS.
