New findings on urinary prostate cancer metabolome through combined GC-MS and1H NMR analytical platforms

Introduction The inherent sensitivity of metabolomics allows the detection of subtle alterations in biological pathways, making it a powerful tool to study biomarkers and the mechanisms that underlie cancer. Objectives The purpose of this work was to characterize the urinary metabolic profile of prostate cancer (PCa) patients and cancer-free controls to obtain a holistic coverage of PCa metabolome. Methods Two groups of samples, a training set (n = 41 PCa and n = 42 controls) and an external validation set (n = 18 PCa and n = 18 controls) were analyzed using a dual analytical platform, namely gas chromatography-mass spectrometry (GC-MS) and proton nuclear magnetic resonance spectroscopy (H-1 NMR). Results The multivariate analysis models revealed a good discrimination between cases and controls with an AUC higher than 0.8, a sensitivity ranging from 67 to 89%, a specificity ranging from 74 to 89% and an accuracy from 73 to 86%, considering the training and external validation sets. A total of 28 metabolites (15 from GC-MS and 13 from(1)H NMR) accounted for the separation. These discriminant metabolites are involved in 14 biochemical pathways, indicating that PCa is highly linked to dysregulation of metabolic pathways associated with amino acids and energetic metabolism. Conclusion These findings confirmed the complementary information provided by GC-MS and(1)H NMR, enabling a more comprehensive picture of the altered metabolites, underlying pathways and deepening the understanding of PCa development and progression.