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 sub-network 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.

Huanyu Zhao;Ruoyu Dang;Yipan Zhu;Baijian Qu;Yasra Sayyed;Ying Wen;Xicheng Liu;Jianping Lin;Luyuan Li

State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy,Tianjin Key Laboratory of Molecular Drug Research,Nankai University,Tianjin 300350,China;Department of Physiology and Pathophysiology,School of Basic Medical Sciences,Capital Medical University,Beijing 100069,China

癌症生物学与医学（英文版）

[1]Huanyu Zhao;Ruoyu Dang;Yipan Zhu;Baijian Qu;Yasra Sayyed;Ying Wen;Xicheng Liu;Jianping Lin;Luyuan Li-.Hub genes associated with immune cell infiltration in breast cancer, identified through bioinformatic analyses of multiple datasets)[J].癌症生物学与医学（英文版）,2022(09):1352-1374

NOISeq,XCELL,RGS1

Hub,genes,associated,immune,infiltration,breast,cancer,identified,through,bioinformatic,analyses,multiple,datasets,Objective,aim,this,study,was,identify,hub,Methods,Nonparametric,robust,aggregation,ranked,EdgeR,methods,were,used,assess,differentially,expressed,across,Protein,protein,interaction,network,enrichment,sub,performed,Immune,evaluated,CIBERSORT,TIMER,association,between,risk,signature,survival,determined,Kaplan,Meier,analysis,multivariate,Cox,nomogram,external,verification,Results,We,applying,both,nonparametric,GEO,TCGA,Integrated,further,CXCL10,CXCL9,CXCL11,SPP1,POSTN,MMP9,DPT,COL1A1,ADAMDEC1,significantly,correlated,prognosis,patients,Moreover,these,strongly,extent,CD4+,cells,CD8+,neutrophils,macrophages,myeloid,dendritic,into,tumors,Conclusions,databases,led,discovery,that,together,may,serve,characteristic,microenvironment

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