通过生物信息学分析和机器学习识别类风湿关节炎相关动脉粥样硬化的免疫相关基因。

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

机构信息

Department of Orthopaedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.

School of Clinical Medical, Weifang Medical University, Weifang, China.

出版信息

Front Immunol. 2023 Mar 9;14:1126647. doi: 10.3389/fimmu.2023.1126647. eCollection 2023.

BACKGROUND

Increasing 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.

METHODS

We 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.

RESULTS

The 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.

CONCLUSION

Six 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

越来越多的证据表明类风湿关节炎（RA）可加重动脉粥样硬化（AS），我们旨在探索 AS 和 RA 患者的潜在诊断基因。

方法

我们从公共数据库，包括基因表达综合（GEO）和 STRING 中获取数据，并使用 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。京都基因与基因组百科全书（KEGG）和基因本体论（GO）富集分析、蛋白质-蛋白质相互作用（PPI）网络和机器学习算法[最小绝对收缩和选择算子（LASSO）回归和随机森林]用于探索免疫相关的枢纽基因。我们使用列线图和接收者操作特征（ROC）曲线评估诊断效能，并使用 GSE55235 和 GSE73754 进行了验证。最后，我们研究了 AS 中的免疫浸润。

结果

AS 数据集包括 5322 个 DEGs，而 RA 中则有 1439 个 DEGs 和 206 个模块基因。AS 和 RA 的关键基因的交集有 53 个，这些基因与免疫有关。在 PPI 网络和机器学习构建之后，使用六个枢纽基因构建了列线图和用于诊断效能评估，这显示了很好的诊断价值（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。