Breast cancer (BC) is clinically defined as a cold tumor due to its low immunogenicity, which is usually insensitive to immunosuppressive agents. Herein, we investigated the predictive potential of novel immune-related genes (IRGs) in BC, with the objective of more effectively guiding the immunotherapy for patients with BC.The least absolute shrinkage and selection operator (LASSO) regression analysis was used to conduct the BC prognostic model based on IRGs, and univariate/multivariate Cox proportional hazards models were employed to establish the BC predictive nomograms. Then, we investigated the expression patterns of these IRGs utilizing The Cancer Genome Atlas (TCGA) database. Moreover, we also performed correlation analyses between IRGs and multiple immune features, including infiltration of immune cells, immune checkpoint members, and immune therapy response.In our study, six IRGs were finally identified to construct the BC prognostic model, including HMGB3, TNFSF4, CORO2A, SOCS3, TACR1, and FREM1. This model exhibited excellent predictive performance, with area under the curve (AUC) values of 0.676, 0.646, and 0.621 for the 1-, 3-, and 5-year timeframes, respectively. Analysis of expression profiles indicated that HMGB3, CORO2A, and TNFSF4 exhibited significant upregulation in BC tissues, displaying strong correlations with diminished overall survival. The three overexpressed genes showed statistically significant correlations with multiple important immune cells, including Tregs, macrophages, and CD4+/CD8+ T cells. Notably, distinct patterns of integrating gene expression and immune infiltration significantly affected the clinical outcomes of BC patients. These upregulated genes demonstrated significant co-expression patterns with key immune checkpoint regulators, suggesting close immunomodulatory interactions in BC. The Tumor Immune Dysfunctional and Exclusion (TIDE) scores were lower in the high-expression groups of HMGB3 and CORO2A, whereas the TIDE score was higher in the high-expression group of TNFSF4.This prognostic model reliably assessed risk for BC patients, providing critical guidance for precision oncology protocols and dynamic surveillance of disease progression.Copyright © 2025 AME Publishing Company. All rights reserved.