A prognostic model for breast cancer based on mitochondria-associated endoplasmic reticulum membrane (MAM) signature genes

Mitochondria-associated endoplasmic reticulum membranes (MAMs) have emerged as key regulators of breast cancer biology. Growing evidence indicates that MAM dysfunction affects tumor progression, immune regulation, and treatment response. However, their prognostic value and therapeutic relevance in breast cancer remain insufficiently clarified.

We curated 83 MAM-related genes from the literature and used TCGA breast cancer cohorts to construct a prognostic model through univariate Cox, LASSO, and multivariate Cox regression analyses. Protein-level validation was performed using the Human Protein Atlas (HPA). Functional enrichment, immune infiltration, immunotherapy response prediction, and drug sensitivity profiling were conducted between risk groups. Single-cell RNA-seq data (GSE255068) were analyzed to map gene expression within the tumor microenvironment. Finally, qRT-PCR and western blotting validated the expression of key genes in breast cancer cell lines.

A four-gene signature (VDAC1, RYR2, PINK1, and TESPA1) robustly stratified breast cancer patients into high- and low-risk groups and independently predicted overall survival across multiple cohorts. A prognostic nomogram integrating the risk score with clinicopathological variables significantly improved survival prediction accuracy. Low-risk tumors were characterized by a more inflamed tumor microenvironment, with increased immune cell infiltration and enhanced immune-related pathway activity, and were predicted to respond more favorably to immune checkpoint blockade. In contrast, high-risk patients exhibited greater predicted sensitivity to multiple cytotoxic and targeted agents, a pattern that remained largely consistent across major molecular subtypes, highlighting the robustness of the signature beyond intrinsic subtype classification. Single-cell transcriptomic analyses further revealed distinct cell-type–specific expression patterns of the four genes within the tumor microenvironment. Finally, molecular experiments confirmed elevated expression of VDAC1, RYR2, and PINK1 in breast cancer cell lines, supporting their biological relevance in tumor progression.

This MAM-based prognostic model provides new insights into breast cancer biology and has potential utility in guiding personalized treatment, particularly regarding immunotherapy and chemotherapy sensitivity. Experimental validation highlights VDAC1 as a promising biomarker.