A novel expressed prostatic secretion (EPS)-urine metabolomic signature for the diagnosis of clinically significant prostate cancer.
Cancer Biol Med
; 2021 May 26.
Article
in En
| MEDLINE
| ID: mdl-34037347
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Diagnostic_studies
/
Prognostic_studies
Language:
En
Journal:
Cancer Biol Med
Year:
2021
Document type:
Article
Affiliation country:
Italy
Country of publication:
China