Mitophagy-Driven Immune Cell Infiltration Patterns Define Breast Cancer Subtypes with Differential Treatment Responses

Background The development of breast cancer (BC) entails intricate immunological and molecular interactions. Although mitophagy-related genes (MRGs) are essential for maintaining cellular homeostasis, little is known about their expression patterns, immunological effects, and therapeutic applications in BC. The purpose of this work was to describe immunological microenvironment changes, MRG dysregulation, and their predictive modeling potential in BC. Methods Using data from single-cell RNA sequencing (scRNA-seq) (GSE248288) and The Cancer Genome Atlas (TCGA), we examined immune cell infiltration MRG expression profiles, and functional pathways. Machine learning methods identified significant MRGs for predictive modeling, which were confirmed using qPCR in BC cell lines. Subtypes were identified using hierarchical clustering, and weighted gene co-expression network analysis (WGCNA) showed subtype-specific modules. Drug sensitivity and immunotherapy responses were evaluated using the oncoPredict and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms. Results Tumor tissues showed elevated MRGs (e.g., SRC, PGAM5, FUNDC1) and altered immunological infiltration, with more activated T cells and macrophages but fewer naïve B cells. scRNA-seq demonstrated increased MRG activity in NK and B cells, which is connected to PI3K-AKT-mTOR signaling and allograft rejection. A nine-MRG predictive model (PGAM5, PRKN, TOMM40, FUNDC1, MAP1LC3B, PINK1, MTERF3, CSNK2A2, and SRC) demonstrated great diagnostic accuracy (AUC: 0.99). BC subgroups based on MRGs expression displayed unique molecular profiles: Cluster 1 (cell cycle dysregulation), Cluster 2 (cilia-related pathways), and Cluster 3 (small GTPase signaling). Cluster 2 showed potential immunotherapy responsiveness (low TIDE scores), whereas Cluster 1 was sensitive to chemotherapy (paclitaxel, gemcitabine). Conclusions MRGs are important regulators of breast cancer progression, regulating immunological dynamics, metabolic pathways, and treatment responses. The established nine-MRG model accurately predicts BC occurrence, whereas subtype-specific biochemical and immunological aspects provide insights for tailored therapy. These findings emphasize MRGs as possible biomarkers for diagnosis and personalized therapy options in BC.