High-throughput metabolomics enables biomarker discovery in prostate cancer

Prostate cancer (PCa) is the most frequently diagnosed cancer and the second leading cause of cancer death among men in the world. Therefore, it is important to discover new biomarkers for the diagnosis of PCa, preferably noninvasive ones. Metabolomics can be used to analyse the entire metabolic profile of a biological system and discover potential biomarkers. Advances in liquid chromatography-tandem mass spectrometry technology have led to the application of metabolomics in PCa, toward identifying metabolic alterations in PCa. In this work, the art metabolomics platform by fast ultrahigh performance liquid chromatography-tandem mass spectrometry (FPLC/MS) coupled with multivariate statistical analyses were employed to identify sensitive and economical peripheral urine biomarker(s) associated with the entire measurable metabolome from PCa patients (n 1⁄4 236) and age-matched healthy controls (n 1⁄4 233), with the aim of discovering alterations in the metabolic phenotype and thus discovering potential biomarkers for the diagnosis of PCa. Metabolic differences among PCa and control subjects were identified based on orthogonal signal correction-partial least squares discriminant analysis (OPLSDA). Glycocholic acid, hippurate, 5-hydroxy-L-tryptophan, taurocholic acid, and chenodeoxycholic acid in the PCa subjects were significantly different from the control cases. Bioinformatics analysis found that these differentially-expressed metabolites had a strong correlation with the farnesoid X receptor and retinoic X receptor (FXR/RXR) activation, etc. To demonstrate the utility of urine biomarkers for the early diagnosis of PCa, three metabolites comprising glycocholic acid, hippurate and 5-hydroxy-L-tryptophan were selected as candidate biomarkers (AUC > 0.95) and validation in separate, independent patient cohorts, yielded satisfactory accuracy, sensitivity and specificity. Furthermore, these data suggest that panels of analytes may be valuable to translate metabolomic findings to clinically useful diagnostic tests. Potentially, the present study provides promising diagnosis tool for PCa.