Rheumatoid arthritis (RA) is a chronic inflammatory arthritis. This study aimed to identify potential biomarkers and possible pathogenesis of RA using various bioinformatics analysis tools. The GMrepo database provided a visual representation of the analysis of intestinal flora. We selected the GSE55235 and GSE55457 datasets from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) separately. With the intersection of these DEGs with the target genes associated with RA found in the GeneCards database, we obtained the DEGs targeted by RA (DERATGs). Subsequently, Disease Ontology, Gene Ontology, and the Kyoto Encyclopedia of Genes and Genomes were used to analyze DERATGs functionally. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed on the data from the gene expression matrix. Additionally, the protein-protein interaction network, transcription factor (TF)-targets, target-drug, microRNA (miRNA)-mRNA networks, and RNA-binding proteins (RBPs)-DERATGs correlation analyses were built. The CIBERSORT was used to evaluate the inflammatory immune state. The single-sample GSEA (ssGSEA) algorithm and differential analysis of DERATGs were used among the infiltration degree subtypes. There were some correlations between the abundance of gut flora and the prevalence of RA. A total of 54 DERATGs were identified, mainly related to immune and inflammatory responses and immunodeficiency diseases. Through GSEA and GSVA analysis, we found pathway alterations related to metabolic regulations, autoimmune diseases, and immunodeficiency-related disorders. We obtained 20 hub genes and 2 subnetworks. Additionally, we found that 39 TFs, 174 drugs, 2310 miRNAs, and several RBPs were related to DERATGs. Mast, plasma, and naive B cells differed during immune infiltration. We discovered DERATGs’ differences among subtypes using the ssGSEA algorithm and subtype grouping. The findings of this study could help with RA diagnosis, prognosis, and targeted molecular treatment.

中文翻译：

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基于肠道菌群、miRNA、转录因子和RNA结合蛋白数据库、GSEA和GSVA通路观察以及免疫浸润分型综合分析揭示类风湿关节炎的免疫炎症靶点

类风湿性关节炎（RA）是一种慢性炎症性关节炎。本研究旨在利用各种生物信息学分析工具来识别 RA 的潜在生物标志物和可能的发病机制。GMrepo 数据库提供了肠道菌群分析的可视化表示。我们从基因表达综合数据库中选择了 GSE55235 和 GSE55457 数据集来分别识别差异表达基因（DEG）。通过将这些DEG与GeneCards数据库中发现的与RA相关的靶基因进行交叉，我们获得了RA靶向的DEG（DERATGs）。随后，疾病本体论、基因本体论以及京都基因和基因组百科全书被用来对DERATGs进行功能分析。对基因表达矩阵的数据进行基因集富集分析 (GSEA) 和基因集变异分析 (GSVA)。此外，还建立了蛋白质-蛋白质相互作用网络、转录因子（TF）-靶标、靶标-药物、微小RNA（miRNA）-mRNA网络和RNA结合蛋白（RBP）-DERATGs相关分析。CIBERSORT用于评估炎症免疫状态。单样本GSEA（ssGSEA）算法和DERATGs的差异分析用于浸润程度亚型。肠道菌群丰度与 RA 患病率之间存在一定相关性。共鉴定出54个DERATG，主要与免疫和炎症反应以及免疫缺陷疾病相关。通过 GSEA 和 GSVA 分析，我们发现与代谢调节、自身免疫性疾病和免疫缺陷相关疾病相关的通路改变。我们获得了 20 个中心基因和 2 个子网络。此外，我们发现 39 个 TF、174 种药物、2310 个 miRNA 和几个 RBP 与 DERATG 相关。肥大、血浆和初始 B 细胞在免疫浸润过程中存在差异。我们使用 ssGSEA 算法和亚型分组发现了 DERATGs 在亚型之间的差异。这项研究的结果有助于 RA 的诊断、预后和靶向分子治疗。