GC-MS-based untargeted metabolomics of plasma and urine to evaluate metabolic changes in prostate cancer

Abstract Prostate cancer (CaP) is a common cancer in men. Its late detection and inefficient diagnosis are a challenge for researchers who are currently searching for new cancer-related indicators that would facilitate better detectability of CaP and explain its pathogenesis. In the present preliminary study, endogenous volatile metabolites were detected in plasma and urine samples by using the metabolic fingerprinting approach. The analyses were performed using the GC-QqQ/MS technique in the scan mode. The detected and putatively identified metabolites were statistically analyzed using advanced univariate and multivariate statistical methods. Eleven urinary and three plasma metabolites were selected as statistically significant in patients with CaP as compared to those in healthy controls. Supervised methods such as logistic regression and quadratic support vector machine were applied to obtain the classification models. The accuracy, sensitivity, and specificity of the models were above 83%, 85%, and 81%, respectively. The putatively identified metabolites were associated with biochemical pathways such as tricarboxylic acid cycle, glycolysis, carbohydrate conversion, and steroidal lipid metabolism that are mainly involved in energy production for cell growth and proliferation.