Metabolomics in Prostate Cancer: A Minimally Invasive Method Using Urine after Prostatic Massage to Predict Gleason Grade.

BACKGROUND

In this study, biomarkers that can predict prostate cancer with a Gleason grade of 8 or higher were explored through nuclear magnetic resonance (NMR).

METHODS

Patients scheduled for transrectal prostate biopsy were enrolled, and urine samples were collected after prostate massage. Patients with cancer were categorised as having Gleason grades of 6-7 or ≥8. All spectra were acquired using a Bruker Avance III DRX 600 spectrometer. For statistical analysis, univariate and multivariate analyses were conducted using metabolites and clinical variables, and the presence of tumours with Gleason grades of ≥8 was predicted.

RESULTS

Data were obtained from 107 patients with prostate cancer: 73 (68.2%) with Gleason grades of 6-7 and 34 (31.8%) with Gleason grades of ≥8. A predictive model incorporating the 29 most significant metabolites identified through partial least squares-discriminant analysis was established. Suspicious digital rectal examination (DRE) results were considered. The model predicted a Gleason grade of ≥8, demonstrating an area under the curve of 0.92, sensitivity of 82%, specificity of 92%, positive predictive value of 84% and negative predictive value of 90%. Metabolites associated with amino acid metabolism and glycolysis were prominent in this model.

CONCLUSIONS

Our study demonstrates that a model combining urinary metabolites with clinical data, specifically DRE findings, can effectively stratify risk in patients with biopsy-confirmed prostate cancer according to Gleason grade. Metabolites linked to glycolysis and amino acid metabolism were particularly relevant. This minimally invasive approach may assist clinical decision-making, although validation in larger multi-centre cohorts is required to confirm its robustness and generalisability.