Untargeted metabolomics based on nuclear magnetic resonance spectroscopy and multivariate classification techniques for identifying metabolites associated with breast cancer patients

In this study, multivariate classification techniques combined with proton nuclear magnetic resonance ((HNMR)-H-1) spectroscopy is proposed to identify breast cancer biomarkers that can precisely distinguish between healthy control and breast cancer (BC) patients. In this regard, first optimizing the metabolite extraction procedure was performed using Box-Behnken design (BBD). Then, data-driven soft independent modeling of class analogy (DD-SIMCA) model and partial least squares-discriminant analysis (PLS-DA) were successfully utilized for separating healthy from BC patient's classes. On this matter, both DD-SIMCA and PLS-DA models could successfully distinguish the healthy class from the BC class with the model's sensitivity and specificity of 100%. Variable importance in projection (VIP) method was implemented to detect significant metabolites. Based on significant variables, 13 significant metabolites (e.g., lactic acid, cysteine, and glucose) were detected as the influential factors for this discrimination. Also, a heat map revealing the trend in metabolites levels was depicted and altered metabolic pathways were detected.