Exploration of the immune microenvironment of breast cancer in large population cohorts - Deng, Youyuan; Wang, Jianguo; Hu, Zhiya; Cai, Yurong; Xu, Yiping; Xu, Ke (Frontiers in Endocrinology, 2022)
Tumor immune microenvironment is associated with tumor progression. However, previous studies have not fully explored the breast cancer (BC) immune microenvironment. All the data analyzed in this study were obtained from the open-accessed database, including The Cancer Genome Atlas, Gene Expression Omnibus (TCGA) and cBioportal databases. R software v4.0 and SPSS 13.0 were used to perform all the statistical analysis. Firstly, the clinical and expression profile information of TCGA, GSE20685, GSE20711, GSE48390, GSE58812 and METABRIC cohorts were collected. Then, 53 immune terms were quantified using the ssGSEA algorithm. A prognosis model based on HER2_Immune_PCA, IL12_score, IL13_score, IL4_score, IR7_score was established, which showed great prognosis prediction efficiency in both training group and validation groups. A nomogram was then established for a better clinical application. Clinical correlation showed that elderly BC patients might have a higher riskscore. Pathway enrichment analysis showed that the pathway of oxidative phosphorylation, E2F targets, hedgehog signaling, adipogenesis, DNA repair, glycolysis, heme metabolism, mTORC1 signaling was activated in high-risk group. Moreover, Tumor Immune Dysfunction and Exclusion and Genomics of Drug Sensitivity in Cancer analysis showed that low-risk patients might be more sensitive to PD-1 therapy, cisplatin, gemcitabine, paclitaxel and sunitinib. Finally, four genes XCL1, XCL2, TNFRSF17 and IRF4 were identified for risk group classification. In summary, our signature is a useful tool for prognosis and prediction of drug sensitivity of BC.