Bioinformatics analysis to screen key prognostic genes in the breast cancer tumor microenvironment

Increasing evidence has shown that the tumor microenvironment (TME) plays an important role in tumor occurrence and development and can also affect patient prognosis. In this study, we screened key prognostic genes in the breast cancer (BC) TME by analyzing the immune and stromal scores of tumor samples to detect differentially expressed genes (DEGs) and also constructed a TME-related prognostic model. First, we obtained mRNA-Seq and related clinical information for patients with BC from The Cancer Genome Atlas (TCGA) and calculated the stromal and immune scores of tumor tissues using the ESTIMATE algorithm. Next, we performed functional enrichment analysis and generated protein–protein interaction networks from the DEGs that were highly related to the TME. Finally, Cox proportional hazards regression analysis was performed on BC datasets from TCGA, and analyses were conducted on infiltrating immune cells and the human protein atlas. Together, these analyses indicated that the KLRB1 and SIT1 genes could be used as independent prognostic factors for BC, while risk score, age, and clinical stage could be used as prognostic factors. In summary, we found that the prognosis of BC is closely related to immune regulation in the TME.