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.
摘要：
慢性活动性EBV感染（CAEBV）与不良预后和高死亡率相关。我们进行了生物信息学分析，以筛选出与CAEBV相关的关键基因。使用加权基因共表达网络分析（WGCNA）来鉴定与小儿CAEBV最相关的基因模块。此外，研究了小儿急性传染性单核细胞增多症（AIM）和小儿CAEBV之间的差异表达基因（DEGs）。然后进行最小绝对收缩和选择算子(LASSO)和随机森林以识别与小儿CAEBV相关的关键变量。我们还探讨了这些hub基因与EBV感染相关通路和免疫细胞丰度之间的相关性。与儿科AIM相比，1561个DEG在小儿CAEBV中上调，这些基因主要富集在炎症反应和炎症相关通路中。WGCNA分析表明，蓝色模块中的基因大多与儿童CAEBV有关。蓝色模块和DEGs中的基因相交以获得174个基因，并且这些基因也富集在炎症反应相关途径中。应用随机森林和LASSO算法,从这174个基因中筛选出与CAEBV相关的关键基因,产生TPST1、TNFSF8和RAB3GAP1。这三个基因在区分小儿CAEBV和小儿AIM方面显示出良好的诊断性能。此外，Cibersort和GSEA分析表明，这三个基因与骨髓细胞富集和持续EBV感染途径呈正相关。分别。我们的发现系统分析了AIM和CAEBV之间的差异，并确定TPST1，TNFSF8和RAB3GAP1是CAEBV发生发展的关键基因。
