Hub genes associated with immune cell infiltration in breast cancer, identified through bioinformatic analyses of multiple datasets

Objective:The aim of this study was to identify hub genes associated with immune cell infiltration in breast cancer through bioinformatic analyses of multiple datasets.Methods:Nonparametric(NOISeq)and robust rank aggregation-ranked parametric(EdgeR)methods were used to assess robust differentially expressed genes across multiple datasets.Protein-protein interaction network,GO,KEGG enrichment,and subnetwork analyses were performed to identify immune-associated hub genes in breast cancer.Immune cell infiltration was evaluated with the CIBERSORT,XCELL,and TIMER methods.The association between the hub gene-based risk signature and survival was determined through Kaplan–Meier survival analysis,multivariate Cox analysis,and a nomogram with external verification.Results:We identified 163 robust differentially expressed genes in breast cancer through applying both nonparametric and parametric methods to multiple GEO(n=2,212)and TCGA(n=1,045)datasets.Integrated bioinformatic analyses further identified 10 hub genes:CXCL10,CXCL9,CXCL11,SPP1,POSTN,MMP9,DPT,COL1A1,ADAMDEC1,and RGS1.The 10 hub-gene-based risk signature significantly correlated with the prognosis of patients with breast cancer.Moreover,these hub genes were strongly associated with the extent of infiltration of CD4+T cells,CD8+T cells,neutrophils,macrophages,and myeloid dendritic cells into breast tumors.Conclusions:Integrated analyses of multiple databases led to the discovery of 10 robust hub genes that together may serve as a risk factor characteristic of the immune microenvironment in breast cancer.