Construction of a DNA damage repair gene signature for predicting prognosis and immune response in breast cancer.

DNA damage repair (DDR) genes are involved in developing breast cancer. Recently, a targeted therapeutic strategy through DNA repair machinery, including PARPi, has initially shown broad development and application prospects in breast cancer therapy. However, few studies that focused on the correlation between the expression level of DNA repair genes, prognosis, and immune response in breast cancer patients have been recently conducted. Herein, we focused on identifying differentially expressed DNA repair genes (DEGs) in breast cancer specimens and normal samples using the Wilcoxon rank-sum test. Biofunction enrichment analysis was performed with DEGs using the R software "cluster Profiler" package. DNA repair genes were involved in multivariate and univariate Cox regression analyses. After the optimization by AIC value, 11 DNA repair genes were sorted as prognostic DNA repair genes for breast cancer patients to calculate risk scores. Simultaneously, a nomogram was used to represent the prognostic model, which was validated using a calibration curve and C-index. Single-sample gene set enrichment analysis (ssGSEA), CIBERSORT algorithms, and ESTIMATE scores were applied to evaluate the immune filtration of tumor samples. Subsequently, anticarcinogen sensitivity analysis was performed using the R software "pRRophetic" package. Unsupervised clustering was used to excavate the correlation between the expression level of prognostic-significant DNA repair genes and clinical features. In summary, 56 DEGs were sorted, and their potential enriched biofunction pathways were revealed. In total, 11 DNA repair genes (