Urine metabolic analysis as a non-invasive method to predict biochemical recurrence in prostate cancer

Metabolomics has proven to be a useful science for obtaining biomarkers in prostate cancer. In this work, urine samples were analyzed by nuclear magnetic resonance (NMR) spectroscopy to identify potential urinary biomarkers associated with biochemical recurrence in prostate cancer.

Urine samples were obtained from patients undergoing transrectal prostate biopsy after prostate massage. Patients were classified as with or without biochemical recurrence after having received prostate cancer treatment. All spectra were acquired using a Bruker Avance III DRX 600 spectrometer. Univariate and multivariate analysis were performed with metabolites and clinical variables to predict tumor presence.

Data were collected from 70 patients treated for prostate cancer, 16 of whom developed biochemical recurrence within 5 years following treatment, with an average time to diagnosed recurrence of 25.68±15.39 months. After establishing a predictive model with the 25 most influential metabolites in Partial Least Squares Discriminant Analysis (PLS-DA analysis), a predictive model of biochemical recurrence was obtained with an area under the curve of 0.95, a sensitivity of 80%, specificity of 98%, positive predictive value (PPV) of 92% and a negative predictive value (NPV) of 96%. Metabolites derived from amino acid metabolism and glycolysis featured most predominantly in this model.

The metabolic profile in urine can be used to construct a model with good discrimination for predicting the development of biochemical recurrence. The molecules highlighted herein frequently belong to amino acid metabolism and glycolysis.