Abstract
Enhancing early-stage breast cancer detection requires integrating additional screening methods with current diagnostic imaging. Omics screening, using easily collectible serum samples, could serve as an initial step. Alongside biomarker identification capabilities, omics analysis allows for a comprehensive analysis of prevalent histological types-DCIS and IDC. Employing metabolomics, metallomics, and machine learning, could yield accurate screening models with valuable insights into organism responses. Serum samples of confirmed breast cancer patients were utilized to analyze metabolite and metal ion profiles, using two distinct analysis methods, proton NMR for metabolomics and ICP-OES for metallomics. The resulting responses were then subjected to discriminant analysis, progression biomarker exploration, examination of correlations between patients' metabolites and metal ions, and the impact of age and menopause status. Measured NMR spectra and metabolite relative integrals were used to achieve statistically significant discrimination through MVA between breast cancer and control groups. The analysis identified 24 metabolites and 4 metal ions crucial for discrimination. Furthermore, four metabolites were associated with disease progression. Additionally, there were important correlations and relationships between metabolite relative integrals, metal ion concentrations, and age/menopausal status subgroups. Quantified relative integrals allowed for discrimination between studied subgroups, validated with a holdout set. Feature importance and statistical analysis for metabolomics and metallomics extracted a set of common entities which in combination provides valuable insights into ongoing molecular disturbances and disease progression.