PT  - JOURNAL ARTICLE
AU  - Denise Drago
AU  - Annapaola Andolfo
AU  - Ettore Mosca
AU  - Alessandro Orro
AU  - Luigi Nocera
AU  - Vito Cucchiara
AU  - Matteo Bellone
AU  - Francesco Montorsi
AU  - Alberto Briganti
TI  - A novel expressed prostatic secretion (EPS)-urine metabolomic signature for the diagnosis of clinically significant prostate cancer
AID  - 10.20892/j.issn.2095-3941.2020.0617
DP  - 2021 May 01
TA  - Cancer Biology and Medicine
PG  - 604--615
VI  - 18
IP  - 2
4099  - http://www.cancerbiomed.org/content/18/2/604.short
4100  - http://www.cancerbiomed.org/content/18/2/604.full
SO  - Cancer Biol Med2021 May 01; 18
AB  - Objective: Significant efforts are currently being made to identify novel biomarkers for the diagnosis and risk stratification of prostate cancer (PCa). Metabolomics can be a very useful approach in biomarker discovery because metabolites are an important read-out of the disease when characterized in biological samples. We aimed to determine a metabolomic signature which can accurately distinguish men with clinically significant PCa from those affected by benign prostatic hyperplasia (BPH).Methods: We first performed untargeted metabolomics using ultrahigh-performance liquid chromatography tandem mass spectrometry on expressed prostatic secretion urine (EPS-urine) from 25 patients affected by BPH and 25 men with clinically significant PCa (defined as Gleason score ≥ 3 + 4). Diagnosis was histologically confirmed after surgical treatment. The EPS-urine metabolomic approach was then applied to a larger, prospective cohort of 92 consecutive patients undergoing multiparametric magnetic resonance imaging for clinical suspicion of PCa prior to biopsy.Results: We established a novel metabolomic signature capable of accurately distinguishing PCa from benign tissue. A metabolomic signature was associated with clinically significant PCa in all subgroups of the Prostate Imaging Reporting and Data System (PI-RADS) classification (100% and 89.13% of accuracy when the PI-RADS was in range of 1–2 and 4–5, respectively, and 87.50% in the more critical cases when the PI-RADS was 3).Conclusions: A combination of metabolites and clinical variables can effectively help in identifying PCa patients that might be overlooked by current imaging technologies. Metabolites from EPS-urine should help in defining the diagnostic pathway of PCa, thus improving PCa detection and decreasing the number of unnecessary prostate biopsies.