Prognostic Value and Immune Signatures of Anoikis-related Genes in Breast Cancer

Abstract

From databases of the Cancer Genome Atlas (TCGA) and GSE42568, transcriptome data of breast cancer patients was obtained. Then, anoikis-related genes (ANRGs) were identified and constructed a risk score system. As a threshold value, the median risk score was used to stratify patients into low-risk and high-risk groups. Kaplan-Meier analysis was then conducted to evaluate the prognostic ability of the risk score system, which was validated using GSE7390. Furthermore, we identified potential enrichment of function and tumor immune infiltration in the model. Finally, the biological functions of a risk gene (EPB41L4B) in breast cancer were investigated through in vitro experiments. We constructed a risk score system via 9 prognosis ANRGs (CXCL2, EPB41L4B, SLC7A5, SFRP1, SDC1, BHLHE41, SPINT1, KRT15, and CD24). The Kaplan-Meier analysis showed that both TCGA-BRCA (training set) and GSE7390 (testing set) patients with high-risk status had significantly worse survival outcomes. In addition, the calibration plots were in good agreement with the prognosis prediction. Breast cancer patients with immunosuppressive microenvironment could be screened using risk groups since risk scores were correlated negatively with ESTIMATE score, tumor-infiltration lymphocytes, immune checkpoints, and chemotactic factors. Furthermore, cellular viability and cell migration of cancerous breast cells were inhibited and apoptosis was promoted by down-regulation of EPB41L4B gene expression. Based on ANRGs, a 9-gene prognostic model could be developed to predict breast cancer prognosis; moreover, patients of the high-risk group were in an immunosuppressed tumor microenvironment.