Identification of Genes with Prognostic Value in the Breast Cancer Microenvironment Using Bioinformatics Analysis

Background: Stromal and immune cells play essential roles in the development of breast cancer (BC). This study was conducted to identify prognosis-related genes from the tumor microenvironment. Material/Methods: The gene expression profiles of 622 BC samples were downloaded from TCGA (The Cancer Genome Atlas) database. Stromal and immune scores were calculated by using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumours using Expression data) algorithm. Then, differentially expressed genes (DEGs) between the high score group and the low score group were screened. The intersecting DEGs were selected through Venn diagrams, and survival analysis was conducted. Functional and pathway enrichment analyses were performed using the DAVID (Database for Annotation, Visualization and Integrated Discovery), and a protein-protein interaction (PPI) network was constructed with the STRING database and Cytoscape. These genes were validated for prognostic value by use of the KM (Kaplan-Meier) plotter tool. Results: The low immune score group was associated with a poor prognosis. However, there was no difference in the prognosis between the high and low stromal score groups. A total of 248 intersecting DEGs were found in BC, and 61 genes were significantly associated with the prognosis of BC patients in the TCGA database. These genes were enriched in the immune response, components of the plasma membrane, and receptor activity. Furthermore, in the validation group, 31 of 61 genes were significantly associated with prognosis. Conclusions: Our bioinformatics analysis identified 31 tumor microenvironment-related genes as potential prognostic predictors for breast cancer patients. Some of these genes that have not been widely investigated previously, such as CXCL9, GPR18, S1PR4, SASH3, and PYH1N1, might be additional predictive factors for BC patients.