Prognostic Analysis of Patients with Breast Cancer Based on Tumor Mutational Burden, DNA Damage Repair Genes, and Immune Infiltration

Abstract

\n Background\n\nBreast cancer has a high tumor-specific death rate and poor prognosis. In this study, we aimed to provide a basis for the prognostic risk in patients with breast cancer using significant gene sets selected by analyzing tumor mutational burden (TMB) and DNA damage repair (DDR).\nMethods\n\nBreast cancer genomic and transcriptomic data were obtained from The Cancer Genome Atlas, and the breast tumor samples were grouped into high- and low-TMB groups based on the calculated TMB. The differentially expressed DDR genes between the two groups were used to construct the breast cancer prognosis model; they were also further analyzed, and 10 key prognostic genes were screened. Using least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, seven genes were selected to construct a linear risk assessment model related to survival, and the patients were divided into high- and low-risk groups.\nResults\n\nThe ability of the model to predict the 5-year and 10-year survival was assessed by time-dependent receiver operating characteristic (ROC) curves. The GSE26085 dataset was used to verify the prognostic risk model. Finally, immune cell abundance and infiltration between the high-and low-risk groups were compared.\nConclusions\n\nWe established a risk prognostic model based on the MDC1, PARP3, PSMB1, PSMB9, PSMD2, PSMD7, and PSMD14 genes, providing a basis for further exploration of a population-based prediction of prognosis and immunotherapy response in patients with breast cancer.