Breast cancer, with an overall poor clinical prognosis, is one of the most heterogeneous cancers. DNA damage repair (DDR) and epithelial–mesenchymal transition (EMT) have been identified to be associated with cancer’s progression. Our study aimed to explore whether genes with both functions play a more crucial role in the prognosis, immune, and therapy response of breast cancer patients. Based on the Cancer Genome Atlas (TCGA) cancer database, we used LASSO regression analysis to identify the six prognostic-related genes with both DDR and EMT functions, including TP63, YWHAZ, BRCA1, CCND2, YWHAG, and HIPK2. Based on the six genes, we defined the risk scores of the patients and reasonably analyzed the overall survival rate between the patients with the different risk scores. We found that overall survival in higher-risk-score patients was lower than in lower-risk-score patients. Subsequently, further GO and KEGG analyses for patients revealed that the levels of immune infiltration varied for patients with high and low risk scores, and the high-risk-score patients had lower immune infiltration’s levels and were insensitive to treatment with chemotherapeutic agents. Furthermore, the Gene Expression Omnibus (GEO) database validated our findings. Our data suggest that TP63, YWHAZ, BRCA1, CCND2, YWHAG, and HIPK2 can be potential genetic markers of prognostic assessment, immune infiltration and chemotherapeutic drug sensitivity in breast cancer patients.

Breast cancer (BRCA) is one of the leading causes of female death worldwide. There are substantial evidences that DNA damage repair (DDR) and epithelial-mesenchymal transition (EMT) are critically related to cancer’s progression and treatment. Nevertheless, it has not been illuminated whether genes with the two functions play a more crucial role in the prognosis, immune and therapy response of BRCA patients. In this study, We identified the prognostic-related genes with both DDR and EMT functions and explored the immune infiltration and chemosensitivity between the different risk groups. The transcriptome expression data and clinical information of BRCA patients were extracted from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The univariate Cox regression analysis was used to screen the prognosis-related DEDGs. The least absolute shrinkage and selection operator (LASSO) Cox regression was performed to construct a prognosis model. Additionally, the multivariate COX regression was conducted to construct a prognostic nomogram. ESTIMATE algorithm, ssGSEA, and the IC50 of chemotherapeutic drugs were used to assess immune activity and responsiveness to chemotherapy. And the prognostic model of six DEDGs were validated in two independent GEO cohorts. The study found that the high-risk group’s patients had significantly lower survival rates than the low-risk group. The immune infiltration levels were lower in the high-risk group. Moreover, patients in the high-risk group were more insensitive to chemotherapeutic agents. This study provides a theoretical framework for BRCA’s treatment and contributing into individualized therapy strategies in BRCA.