Integrative multi-omics and machine learning framework identifies PRDX4 as a redox-EMT regulator and predictive...

Bone metastasis is a major cause of morbidity and mortality in breast cancer, yet effective prognostic models and targeted therapies remain limited. Here, a machine learning (ML)-driven multi-omics framework integrating epithelial-mesenchymal transition (EMT) and nucleotide metabolism (NM) signatures is presented to uncover prognostic biomarkers and guide rational drug discovery. Using gene expression omnibus (GEO) and the cancer genome atlas-breast invasive carcinoma (TCGA-BRCA) bone metastasis datasets, applied the least absolute shrinkage and selection operator (LASSO) ML to identify NM-associated hub genes, revealing peroxiredoxin 4 (PRDX4) as a key risk-associated gene. Multi-level analyses demonstrated that PRDX4 expression correlates with immune cell infiltration, microsatellite instability (MSI), tumor mutational burden (TMB), EMT activation, and poor overall survival. Consensus clustering stratified patients into distinct EMT-NM molecular subgroups with divergent clinical outcomes, immune checkpoint expression, and tumor stemness scores, providing a foundation for precision patient stratification. To accelerate translational impact, we performed drug repurposing and molecular docking, identifying Docetaxel as a high-affinity PRDX4-targeting compound with favorable binding energetics. Together, this work demonstrates how ML-driven multi-omics analysis can bridge biomarker discovery and drug design, guiding multitarget and multi-drug strategies to improve outcomes in bone metastatic breast cancer.