Comprehensive analysis of the cancer driver genes in breast cancer demonstrates their roles in cancer prognosis and tumor microenvironment

Background Breast cancer is the most common malignancy in women. Cancer driver gene-mediated alterations in the tumor microenvironment are critical factors affecting the biological behavior of breast cancer. The purpose of this study was to identify the expression characteristics and prognostic value of cancer driver genes in breast cancer. Methods The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets are used as the training and test sets. Classified according to cancer and paracancerous tissues, we identified differentially expressed cancer driver genes. We further screened prognosis-associated genes, and candidate genes were submitted for the construction of a risk signature. Functional enrichment analysis and transcriptional regulatory networks were performed to search for possible mechanisms by which cancer driver genes affect breast cancer prognosis. Results We identified more than 200 differentially expressed driver genes and 27 prognosis-related genes. High-risk group patients had a lower survival rate compared to the low-risk group (P<0.05), and risk signature showed high specificity and sensitivity in predicting the patient prognosis (AUC 0.790). Multivariate regression analysis suggested that risk scores can independently predict patient prognosis. Further, we found differences in PD-1 expression, immune score, and stromal score among different risk groups. Conclusion Our study confirms the critical prognosis role of cancer driver genes in breast cancer. The cancer driver gene risk signature may provide a novel biomarker for clinical treatment strategy and survival prediction of breast cancer.

Automated summary

This study identified over 200 differentially expressed driver genes and 27 prognosis-related genes in breast cancer. Patients in the high-risk group had lower survival rates compared to the low-risk group (P<0.05), and the risk signature showed high specificity and sensitivity in predicting patient prognosis (AUC 0.790). Multivariate regression analysis indicated that risk scores can independently predict patient prognosis. Additionally, differences in PD-1 expression, immune score, and stromal score were found among different risk groups.