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Copyright (c) 2023 Yi Zhang, Wenli Liu, Yue Li, Xiaoyu An, Dandan Zhao
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The undersigned hereby assign all rights, included but not limited to copyright, for this manuscript to CMB Association upon its submission for consideration to publication on Cellular and Molecular Biology. The rights assigned include, but are not limited to, the sole and exclusive rights to license, sell, subsequently assign, derive, distribute, display and reproduce this manuscript, in whole or in part, in any format, electronic or otherwise, including those in existence at the time this agreement was signed. The authors hereby warrant that they have not granted or assigned, and shall not grant or assign, the aforementioned rights to any other person, firm, organization, or other entity. All rights are automatically restored to authors if this manuscript is not accepted for publication.Bioinformatics analysis to screen the key genes in pediatric Chronic Active Epstein-Barr Virus Infection
Corresponding Author(s) : Yi Zhang
Cellular and Molecular Biology,
Vol. 69 No. 7: Issue 7
Abstract
Chronic active EBV infection (CAEBV) is associated with poor prognosis and high mortality. We performed bioinformatics analysis to screen out key genes associated with CAEBV. Weighted gene co-expression network analysis (WGCNA) was used to identify the gene module which was most correlated with pediatric CAEBV. Furthermore, the differentially expressed genes (DEGs) between pediatric acute infectious mononucleosis (AIM) and pediatric CAEBV were investigated. Least absolute shrinkage and selection operator (LASSO) and random forest then were performed to identify the key variables associated with pediatric CAEBV. We also explored the correlation between these hub genes with EBV infection related pathway and immune cell abundance. Compared with pediatric AIM, 1561 DEGs were up-regulated in pediatric CAEBV, and these genes were mainly enriched in inflammatory response and inflammation-related pathways. WGCNA analysis showed that genes in blue module were mostly related to pediatric CAEBV. Genes in the blue module and DEGs are intersected to get 174 genes and these genes are also enriched in inflammatory response-related pathways. The key CAEBV-related genes were selected from these 174 genes by applying the random Forest and LASSO algorithm, resulting in TPST1, TNFSF8 and RAB3GAP1. These three genes showed good diagnostic performance in distinguishing pediatric CAEBV from pediatric AIM. Furthermore, Cibersort and GSEA analysis indicated that these three genes were positively correlated with myeloid cell enrichment and persistent EBV infection pathway, respectively. Our finding systematically analyzed the difference between AIM and CAEBV and identified TPST1, TNFSF8 and RAB3GAP1 were the key genes in the development of CAEBV.
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