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Urine biomarkers can predict prostate cancer and PI-RADS score prior to biopsy

Authors :
Blaz Pavlovic
Konstantin Bräutigam
Florence Dartiguenave
Paul Martel
Arnas Rakauskas
Valérie Cesson
Markus Veit
Pascal Oechslin
Alexander Gu
Thomas Hermanns
Karim Saba
Cédric Poyet
Andreas M. Hötker
Niels J. Rupp
Massimo Valerio
Laurent Derré
Daniel Eberli
Irina Banzola
Source :
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Publication Year :
2024
Publisher :
Nature Portfolio, 2024.

Abstract

Abstract Prostate-Specific Antigen (PSA) based screening of prostate cancer (PCa) needs refinement. The aim of this study was the identification of urinary biomarkers to predict the Prostate Imaging—Reporting and Data System (PI-RADS) score and the presence of PCa prior to prostate biopsy. Urine samples from patients with elevated PSA were collected prior to prostate biopsy (cohort = 99). The re-analysis of mass spectrometry data from 45 samples was performed to identify urinary biomarkers to predict the PI-RADS score and the presence of PCa. The most promising candidates, i.e. SPARC-like protein 1 (SPARCL1), Lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1), Alpha-1-microglobulin/bikunin precursor (AMBP), keratin 13 (KRT13), cluster of differentiation 99 (CD99) and hornerin (HRNR), were quantified by ELISA and validated in an independent cohort of 54 samples. Various biomarker combinations showed the ability to predict the PI-RADS score (AUC = 0.79). In combination with the PI-RADS score, the biomarkers improve the detection of prostate carcinoma-free men (AUC = 0.89) and of those with clinically significant PCa (AUC = 0.93). We have uncovered the potential of urinary biomarkers for a test that allows a more stringent prioritization of mpMRI use and improves the decision criteria for prostate biopsy, minimizing patient burden by decreasing the number of unnecessary prostate biopsies.

Details

Language :
English
ISSN :
20452322
Volume :
14
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
edsdoj.b6949f6ccd9044508a54959dcc3ab281
Document Type :
article
Full Text :
https://doi.org/10.1038/s41598-024-68026-1