Construction and Validation of an Immune-related Gene Pairs Prognostic Model for Breast Cancer

Abstract Background: Immunotherapy plays an increasingly important role in the treatment of advanced female breast cancer, which has the highest mortality rate among malignant tumors. The purpose of this study was to identify immune-related genes associated with breast cancer prognosis as possible targets of immunotherapy, and their related biological processes and signaling pathways.Methods: Clinical data and gene expression profiles of patients with breast cancer were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and divided into training (n = 1053) and verification (n = 508) groups. CIBERSORT was used to predict differences in immune cell infiltration in patient subsets stratified according to risk. Gene Ontology (GO) enrichment analysis was used to identify pathways associated with immune-related genes in patient subsets stratified according to risk.Results: The prognostic model composed of 27 immune-related gene pairs significantly distinguished between high- and low-risk patients. Univariate and multivariate analyses indicated that the model was an independent prognostic factor for breast cancer. Among the identified genes, APOBEC3G, PLXNB1, and C3AR1 had not been previously studied in breast cancer and warrant further exploration. CCR chemokine receptor binding, regulation of leukocyte-mediated cytotoxicity, T cell migration, T cell receptor complex, and other pathways were significantly enriched in low-risk patients. M2 and M0 macrophages were more highly expressed in high-risk than in low-risk patients. CD8+ T cells and naïve B cells were more abundant in low-risk than in high-risk patients.Conclusion: The immune-related gene pairs prognostic model developed in the current study can help assess breast cancer prognosis and provides a potential target and research direction for breast cancer immunotherapy in the future.