<?xml version='1.0' encoding='UTF-8'?><xml><records><record><source-app name="HighWire" version="7.x">Drupal-HighWire</source-app><ref-type name="Journal Article">17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhao, Huanyu</style></author><author><style face="normal" font="default" size="100%">Dang, Ruoyu</style></author><author><style face="normal" font="default" size="100%">Zhu, Yipan</style></author><author><style face="normal" font="default" size="100%">Qu, Baijian</style></author><author><style face="normal" font="default" size="100%">Sayyed, Yasra</style></author><author><style face="normal" font="default" size="100%">Wen, Ying</style></author><author><style face="normal" font="default" size="100%">Liu, Xicheng</style></author><author><style face="normal" font="default" size="100%">Lin, Jianping</style></author><author><style face="normal" font="default" size="100%">Li, Luyuan</style></author></authors><secondary-authors></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Hub genes associated with immune cell infiltration in breast cancer, identified through bioinformatic analyses of multiple datasets</style></title><secondary-title><style face="normal" font="default" size="100%">Cancer Biology &amp;amp; Medicine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022-09-15 00:00:00</style></date></pub-dates></dates><pages><style  face="normal" font="default" size="100%">1352-1374</style></pages><doi><style  face="normal" font="default" size="100%">10.20892/j.issn.2095-3941.2021.0586</style></doi><volume><style face="normal" font="default" size="100%">19</style></volume><issue><style face="normal" font="default" size="100%">9</style></issue><abstract><style  face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>