Rheumatoid arthritis (RA) is a multi-systemic inflammatory autoimmune disease involving peripheral joints, and the pathogenesis is not clear. Studies showed that DNA methylation and expression might also be involved in the pathogenesis of RA. This study integrated three expression profile datasets (GSE55235, GSE12021, and GSE55457) and one methylation profile dataset GSE111942 to elucidate the potential essential candidate genes and pathways in RA. Differentially expressed genes (DEGs) and differentially methylation genes (DMGs) were identified by R programming software, using Limma package and ChAMP package, respectively. DAVID performed gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Functional annotation and construction of a protein–protein interaction (PPI) network and the Molecular Complex Detection Algorithm (MCODE) were analysed by STRING and Cystoscope, respectively. Then the connection analysis of DEGs and DMGs was carried out, and further to analyse the relationship between methylation and gene expression, aiming to screen out the potential genes. In this study, 288 DEGs and 228 DMGs were identified, and the majority of DEGs were up-regulated. Enrichment analysis represented that DEGs mainly involved immune response and participated in the Cytokine–cytokine receptor interaction signal pathway. 282 nodes were identified from DEGs PPI network and MCODE, filtering the most significant 2 modules, 23 core node genes were identified and most of them are involved in the T cell receptor signalling pathway and chemokine-mediated signalling pathway. Cross-analysis revealed 4 genes [KNTC1 (cg 01277763), LRRC8D (cg 07600884), DHRS9 (cg 05961700), and UCP2 (cg 05205664)] that exhibited differential expression and methylation in RA simultaneously. Therefore, the four genes could be used as the target for RA.

类风湿关节炎（RA）是一种多系统性炎症性自身免疫性疾病，涉及周围关节，其发病机理尚不清楚。研究表明，DNA甲基化和表达也可能与RA的发病机制有关。这项研究整合了三个表达谱数据集（GSE55235，GSE12021和GSE55457）和一个甲基化谱数据集GSE111942，以阐明RA中潜在的潜在候选基因和途径。通过R编程软件分别使用Limma软件包和ChAMP软件包，鉴定了差异表达的基因（DEG）和甲基化的甲基化基因（DMG）。DAVID进行了DEG的基因本体论（GO）和《京都基因与基因组百科全书》（KEGG）途径富集分析。通过STRING和Cystoscope分别分析了蛋白质-蛋白质相互作用（PPI）网络和分子复合物检测算法（MCODE）的功能注释和构建。然后进行了DEGs与DMGs的连接分析，进一步分析了甲基化与基因表达之间的关系，旨在筛选出潜在的基因。在这项研究中，确定了288个DEG和228个DMG，并且大多数DEG被上调。富集分析表明，DEGs主要参与免疫反应，并参与细胞因子-细胞因子受体相互作用的信号通路。从DEG的PPI网络和MCODE中识别出282个节点，过滤了最重要的2个模块，鉴定出23个核心节点基因，其中大多数与T细胞受体信号传导途径和趋化因子介导的信号传导途径有关。交叉分析揭示了4个基因[KNTC1（cg 01277763），LRRC8D（cg 07600884），DHRS9（cg 05961700）和UCP2（cg 05205​​664）]在RA中同时显示差异表达和甲基化。因此，这四个基因可用作RA的靶标。