Table_2_Identification of immune-related genes in diagnosing atherosclerosis with rheumatoid arthritis through bioinformatics analysis and machine learning.xls

BackgroundIncreasing evidence has proven that rheumatoid arthritis (RA) can aggravate atherosclerosis (AS), and we aimed to explore potential diagnostic genes for patients with AS and RA.MethodsWe obtained the data from public databases, including Gene Expression Omnibus (GEO) and STRING, and obtained the differentially expressed genes (DEGs) and module genes with Limma and weighted gene co-expression network analysis (WGCNA). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis, the protein–protein interaction (PPI) network, and machine learning algorithms [least absolute shrinkage and selection operator (LASSO) regression and random forest] were performed to explore the immune-related hub genes. We used a nomogram and receiver operating characteristic (ROC) curve to assess the diagnostic efficacy, which has been validated with GSE55235 and GSE73754. Finally, immune infiltration was developed in AS.ResultsThe AS dataset included 5,322 DEGs, while there were 1,439 DEGs and 206 module genes in RA. The intersection of DEGs for AS and crucial genes for RA was 53, which were involved in immunity. After the PPI network and machine learning construction, six hub genes were used for the construction of a nomogram and for diagnostic efficacy assessment, which showed great diagnostic value (area under the curve from 0.723 to 1). Immune infiltration also revealed the disorder of immunocytes.ConclusionSix immune-related hub genes (NFIL3, EED, GRK2, MAP3K11, RMI1, and TPST1) were recognized, and the nomogram was developed for AS with RA diagnosis.

背景：日益增多的证据证实类风湿性关节炎（RA）可加剧动脉粥样硬化（AS），本研究旨在探索AS和RA患者的潜在诊断基因。方法：我们从公共数据库中获取数据，包括基因表达综合数据库（GEO）和STRING，使用Limma和加权基因共表达网络分析（WGCNA）获得差异表达基因（DEGs）和模块基因。通过京都基因与基因组百科全书（KEGG）和基因本体（GO）富集分析、蛋白质-蛋白质相互作用（PPI）网络以及机器学习算法[最小绝对收缩和选择算子（LASSO）回归和随机森林]来探索与免疫相关的枢纽基因。我们使用列线图和接受者操作特征（ROC）曲线评估诊断效能，并通过GSE55235和GSE73754进行验证。最终，在AS中构建了免疫浸润模型。结果：AS数据集包括5,322个DEGs，而RA中存在1,439个DEGs和206个模块基因。AS和RA关键基因的DEGs交集为53，这些基因参与免疫反应。在构建PPI网络和机器学习模型后，我们利用六个枢纽基因（NFIL3、EED、GRK2、MAP3K11、RMI1和TPST1）构建了列线图，并进行了诊断效能评估，显示出极高的诊断价值（曲线下面积从0.723到1）。免疫浸润也揭示了免疫细胞的紊乱。结论：识别出六个与免疫相关的枢纽基因（NFIL3、EED、GRK2、MAP3K11、RMI1和TPST1），并开发了用于RA诊断的AS列线图。