目的:通过生物信息学的方法分析健康者和RA患者滑膜组织的差异基因，从转录组学水平上探讨RA可能的发病机制及潜在治疗作用靶点。方法:从美国国家生物技术信息中心（NCBI）的子数据库基因芯片公共数据库（GEO）下载符合筛选标准的健康人群和RA患者的芯片信息，利用R语言及相关软件包进行分析获得差异表达基因、GO富集分析和KEGG信号通路富集分析结果。利用STRING、Cytoscape等工具探讨差异基因的蛋白交互作用网络和生物学通路，分析关键基因与通路，寻找潜在作用靶点。结果:①与健康者滑膜相比，RA患者的滑膜有231个差异基因，其中表达上调的基因126个，表达下调的基因105个。② GO富集分析表明上调基因主要参与了免疫应答的正向调节、细胞因子的产生、细胞表面组成、信号受体活动等生物学过程，下调基因主要参与了细胞增殖的负调控、细胞外基质组成、转录调控区域DNA结合等生物学过程。③ KEGG上调基因信号通路富集分析主要是细胞因子受体相互作用通路，下调基因富集通路主要是癌症转录失调等。④通过STRING建立蛋白交互作用网络，使用Cytoscape的MCODE插件筛选出3个显著模块，选取各模块中的基因进行通路富集分析，3个模块基因主要富集在趋化因子受体通路、磷脂酰肌醇-3-激酶/蛋白激B（PI3K/Akt）通路等。结论:RA患者滑膜差异基因表达主要集中在细胞因子与受体间的相互作用信号通路、趋化因子信号通路、PI3K-Akt信号通路等方面，其中PI3K-Akt信号通路在RA的发生发展中发挥了重要作用，有望成为诊断和治疗的重要靶点。“,”Objective:To explore the possible pathogenesis and potential therapeutic targets of rheumatoid arthritis (RA), the differential genes in the synovial tissue of healthy controls (HCs) and RA patients were analyzed by bioinformatics.Methods:The microarray data of HCs and RA who met the screening criteria were downloaded from Gene Expression Omnibus (GEO) database. R software and related packages were used for data analysis, including differential expression genes (DEGs), GO enrichment analysis and Kyoto encyclopedia of genes and genomes (KEGG) signal pathway enrichment analysis. Protein-protein interaction (PPI) network was established by STRING and visualized by Cytoscape software, these tools were used to analyze key genes and pathways and search for potential targets.Results:① Compared with HCs, patients with RA expressed 231 synovial DEGs which included 126 up-regulated genes and 105 down-regulated genes. ② The GO analysis results showed that up-regulated DEGs were preminarily involved in positive regulation of response, cell surface, signaling receptor activity. Down-regulated DEGs involved negative regulation of cell proliferation, extracellular matrix, transcription regulatory region DNA binding. ③ The significant KEGG enriched pathways of the up-regulated DEGs pointed at cytokine-cytokine receptor interaction, and the down-regulatory DEGs implied transcriptional dys-regulation. ④ PPI network was performed with the STRING, and the top 3 significant modules were identified with the MCODE, revealed that these genes were involved in significant pathways including chemokine receptor pathway and PI3K-AKT signaling pathway.Conclusion:The expression of different synovial genes in RA patients mainly focuses on the interaction between cytokines and receptors, chemokine signaling pathway and PI3K-Akt signaling pathway, among which PI3K-Akt signaling pathway plays an important role in the occurrence and development of RA. PI3K-Akt signaling pathway and its related genes might be potential diagnostic biomarkers for therapeutic strategies of RA.