An immune-related risk gene signature predicts the prognosis of breast cancer

Background Accurate prediction of the outcome of breast cancer remains as a challenge due to its heterogeneous nature. We aimed to construct an immune-related risk signature to predict the overall outcome of breast cancer using bioinformatic approaches. Methods In this study, transcriptome and survival data obtained from The Cancer Genome Atlas database and the Gene Expression Omnibus database were used to identify differentially expressed genes between breast cancer and normal samples. A regulatory network was constructed based on the immune-related prognostic genes and transcription factors screened from the differently expressed genes. The immune-related risk gene signature was obtained using the least absolute shrinkage and selection operator (LASSO) method and Cox regression model. The immune-related prognostic scores of breast cancer (IPSBC) calculated from the risk signature were used to group breast cancer patients by risk levels. The accuracy of IPSBC was evaluated by survival analysis and receiver operating characteristic curve analysis. The independency and the relationship of IPSBC with clinicopathological characteristics and abundance of tumor-infiltrated immune cells were also investigated. Results A total of 4296 differentially expressed genes between breast cancer and normal samples were identified, and a total of 13 prognostic immune-related genes were eventually selected as the risk gene signature, which was an independent prognostic factor of the overall survival of breast cancer. The IPSBC stratified breast cancer patients into low- and high-risk groups. Breast cancer patients in the high-risk group were associated with worse overall outcomes, more advanced stage and less abundance of tumor-infiltrated immune cells, including B cells, CD4(+) T cells, CD8(+) T cells, neutrophils, macrophages, and dendritic cells compared to low-risk group. Conclusion In this study, an immune-related gene signature of breast cancer was identified, which could be used as potential prognostic and therapeutic targets of breast cancer.