An integrative machine learning approach identifies the centrality of ferroptosis, cuproptosis, and immune pathway crosstalk for breast cancer stratification and therapy guidance.

Breast cancer (BRCA) is a leading cause of cancer-related mortality in women, characterized by marked heterogeneity in molecular subtypes, immune microenvironment, and therapeutic response. Current gene expression classifiers often lack mechanistic grounding, limiting their clinical utility. Using an integrated machine learning approach, we identified a four-gene panel, FOXO4, EGFR, FGF2, and CDKN2A, capturing convergent dysregulation across ferroptosis, cuproptosis, and immune pathways. The panel reflects not only redox and proliferative dysregulation but also distinct immune microenvironmental patterns, with FGF2 linked to stromal remodeling and CDKN2A correlated with adaptive immune responses, underscoring its biological integration into BRCA pathology. This panel is rooted in recurrent dysregulation of oxidative stress control (FOXO4), proliferative and angiogenic signalling (EGFR, FGF2), and cell-cycle-immune interfaces (CDKN2A), linking classification to central regulatory mechanisms. The model achieved 97-98% test accuracy (AUC 0.99) for tumour-healthy discrimination. Our findings reveal transcriptional convergence between redox-metabolic and immune-escape programs in BRCA. We propose a compact and interpretable panel with translational potential, offering a minimal, mechanism-informed diagnostic framework for clinical deployment in breast cancer for precision oncology.