Background The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify differentially expressed genes (DEGs), pathways and immune infiltration involved in RA utilizing integrated bioinformatics analysis and investigating potential molecular mechanisms. Materials and methods The expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 normal controls. The microarray datasets were consolidated and DEGs were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1) software, respectively. The protein-protein interaction (PPI) network of DEGs were developed utilizing the STRING database. Finally, the CIBERSORT was used to evaluate the infiltration of immune cells in RA. Results A total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on cytokine receptor activity and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. The principal component analysis showed that there was a significant difference between the two tissues in infiltration immune. Conclusion This study shows that screening for DEGs, pathways and immune infiltration utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. Besides, our study provides valuable data related to DEGs, pathways and immune infiltration of RA and may provide new insight into the understanding of molecular mechanisms.

中文翻译：

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综合生物信息学分析鉴定类风湿关节炎差异表达基因、信号通路和免疫浸润

背景 与类风湿性关节炎 (RA) 相关的致残率在炎症性关节疾病中名列前茅。然而，原因和潜在的分子事件尚不清楚。在这里，我们旨在利用综合生物信息学分析和研究潜在的分子机制来鉴定 RA 中涉及的差异表达基因 (DEG)、途径和免疫浸润。材料与方法 GSE55235、GSE55457、GSE55584和GSE77298的表达谱下载自Gene Expression Omnibus数据库，该数据库包含76个滑膜样本，包括49个RA样本和27个正常对照。整合微阵列数据集，获取 DEG，并通过生物信息学技术进一步分析。分别使用 R（3.6.1 版）软件对 DEG 进行基因本体论 (GO) 和京都基因和基因组百科全书 (KEGG) 通路富集分析。DEG 的蛋白质-蛋白质相互作用 (PPI) 网络是利用 STRING 数据库开发的。最后，CIBERSORT 用于评估 RA 中免疫细胞的浸润。结果共识别出828个DEGs，其中上调758个，下调70个。GO 和 KEGG 通路分析表明，这些 DEG 主要关注细胞因子受体活性和相关信号通路。从 PPI 网络中确定了 DEG 中最密切相关的 30 个基因。主成分分析表明两种组织在浸润免疫方面存在显着差异。结论 本研究表明，筛查 DEG，利用综合生物信息学分析的途径和免疫浸润有助于理解 RA 发展中涉及的分子机制。此外，我们的研究提供了与 RA 的 DEG、途径和免疫浸润相关的有价值的数据，并可能为理解分子机制提供新的见解。