Abstract Early and accurate differentiation of prostate cancer (PC) from benign prostatic hyperplasia (BPH) remains challenging; metabolomics enables biomarker discovery by capturing disease-specific metabolic changes. The study includes 64 expressed prostatic secretion (EPS) from 31 PC cases and 33 BPH cases. Nuclear magnetic resonance (NMR) spectroscopy was used for metabolomics. Multivariate analyses, including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and artificial neural network (ANN) modelling, were performed to identify discriminative metabolites. Diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis. Clinical correlations with prostate-specific antigen (PSA) levels, multiparametric MRI (mpMRI), and Gleason score (GS) were executed. Citrate, glutamate, myo-inositol, and cis-aconitate were identified as key metabolites distinguishing PC from BPH, showing significant correlations with PSA, mpMRI-derived Prostate Imaging Reporting and Data System (PI-RADS) scores and apparent diffusion coefficient (ADC) values as well as histopathology-based GS. The identified metabolic signature demonstrates strong potential as a noninvasive tool to support early PC detection and clinical decision-making, showing correlation with PSA, mpMRI indices and GS to enhance diagnostic accuracy before structural changes become evident.
Kumar et al. (Wed,) studied this question.