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Background: Breast cancer is the most prevalent malignancy in women globally, and apoptosis plays an important role in its pathological process. However, studies on the relationship between breast cancer prognosis and apoptosis-related genes are scarce. This study aimed to construct an apoptosis-related specific risk model for breast cancer and preliminarily explore the immunological differences between the high- and low-risk groups of this model to improve the prognosis and treatment of patients with breast cancer.Methods: The Cancer Gene Atlas (TCGA) and Gene Expression Omnibus (GEO) were used to analyze the correlation between apoptosis-related genes and differentially expressed genes. Apoptotic genes associated with prognosis were selected, and a risk model was constructed and validated using univariate and multivariate Cox proportional regression analyses. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO)analyses were used to predict potential mechanical pathways. Tumor lymphocyte infiltration was analyzed between the high- and low-risk groups. The CellMiner database, RNA: RNA-seq expression and Compound activity: DTP NCI-60 were used to assess the drug sensitivity of the model genes. Immunohistochemistry was used to validate the levels of risk model signatures in clinical samples.Results: Seventy-four differentially expressed apoptosis-related genes were screened between breast cancer tissues and adjacent normal tissues. Immune infiltration analysis suggested that CD8-positive T-cells and natural killer cells was considerably higher in the low-risk group than in the high-risk group and that the immune effect was higher in the low-risk group than in the high-risk group. Drug sensitivity analysis showed a positive correlation between the model genes and the sensitivity of multiple drugs, such as vemurafenib, dabrafenib, PD-98059, and palbociclib. Immunohistochemistry results were consistent with the above-mentioned results.Conclusion: Our findings suggest that the developed apoptosis-related specific risk model could be a novel predictive tool for use in patients with breast cancer and can serve as the basis for future breast cancer therapy.

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