Ferritinophagy-Related Genes in Breast Cancer: Insights from Multi-Omics Analysis

Introduction: Breast cancer (BRCA) remains a major health burden. Ferritinophagy, a selective form of autophagy regulating iron homeostasis, has been linked to tumor progression. This study aims to explore ferritinophagy-associated biomarkers and develop a prognostic model in BRCA through integrative multi-omics analysis. Methods: 30 differentially expressed ferritinophagy-related differentially expressed genes (FeRDEGs) were detected in BRCA, followed by functional enrichment analyses to investigate their biological significance. The prognostic risk model was formulated by integrating Cox and LASSO regression analyses. Patients were stratified based on calculated risk scores, and model performance was validated in an independent cohort. Additionally, we assessed mutation profiles, drug-gene interactions, and immune infiltration characteristics. Results: Functional annotation and clustering revealed links to cellular stress responses, autophagy regulation, and metabolic remodeling. Through univariate Cox and LASSO regression analyses, ATG5, JUN, and TFRC were identified as significantly associated with patient survival. A multigene prognostic model was constructed based on their expression, which facilitated risk stratification of patients with statistically significant differences in survival outcomes. The two risk groups also exhibited different mutational landscapes, druggable gene profiles, and drug sensitivity, suggesting implications for personalized therapy. Immune infiltration analysis offered additional contextual support for the findings. Discussion: The multi-gene prognostic model based on ATG5, JUN, and TFRC demonstrated stable risk stratification and potential clinical utility in breast cancer. Future validation with larger, multi-center cohorts and additional multi-omics features could further enhance its predictive performance. Conclusion: We explored the biological relevance of FeRDEGs and established a prognostic model based on ATG5, JUN, and TFRC. The model was validated across multiple dimensions, supporting its potential value in risk stratification and clinical application. These findings might contribute to offering a foundation for exploring therapeutic strategies targeting iron metabolism and autophagy-related pathways.