Department of Joint Surgery, Xi'an Jiaotong University Affiliated Hong Hui Hospital, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710000, P.R. China.
Department of Breast Surgery, Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, Xinjiang 830011, P.R. China.
Mol Med Rep. 2020 Oct;22(4):3513-3524. doi: 10.3892/mmr.2020.11406. Epub 2020 Aug 3.
Rheumatoid arthritis (RA) and osteoarthritis (OA) are the two most common debilitating joint disorders and although both share similar clinical manifestations, the pathogenesis of each is different and remains relatively unclear. The present study aimed to use bioinformatic analysis to identify pivotal genes and pathways involved in the pathogenesis of RA. Microarray datasets from patients with RA and OA were obtained from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were identified using GEO2R software; Gene Ontology analysis and pathway enrichment were analyzed using the Database for Annotation, Visualization and Integrated Discovery and the Kyoto Encylopedia for Genes and Genomes, respectively; and protein‑protein interaction networks of DEGs were constructed using the Search Tool for the Retrieval of Interacting Genes database, and module analysis and pathway crosstalk of the PPI network was visualized using plugins of Cytoscape. In addition, the prediction of target mRNAs for differentially expressed microRNAs (DEMs) was performing using the starBase database and the identified pivotal genes were verified using reverse‑transcription quantitative PCR in synovial tissue from patients with RA. A total of 566 DEGs were identified in GSE55457, GSE55235 while 23 DEMs were identified in the GSE72564 dataset. Upregulated DEGs were found to be mostly enriched in the 'Cytokine‑cytokine receptor interaction' pathway, whereas downregulated DEGs were discovered to be enriched in the 'PPAR signaling pathway'. The top 25 DEGs were mostly enriched in the 'Chemokine signaling pathway'. In addition, six of the miRNA target genes were selected as potential biomarkers and a total of 24 genes were selected as potential hub genes. Experimental validation demonstrated that the expression levels of Cytotoxic T‑Lymphocyte Associated Protein 4 (CTLA4), Zeta‑chain‑associated protein kinase 70 (ZAP70) and LCK proto‑oncogene (LCK) were significantly increased, whereas HGF expression levels were decreased in RA synovial tissue. In conclusion, these findings suggest that the identified DEGs and pivotal genes in the present study may further enhance our knowledge of the underlying pathways in the pathogenesis of RA. These genes may also serve as diagnostic biomarkers and therapeutic targets for RA; however, further experimental validation is necessary following the bioinformatic analysis to determine our conclusions.
类风湿关节炎（RA）和骨关节炎（OA）是两种最常见的使人虚弱的关节疾病，尽管它们具有相似的临床表现，但每种疾病的发病机制不同，且仍相对不清楚。本研究旨在使用生物信息学分析来鉴定 RA 发病机制中涉及的关键基因和途径。从基因表达综合数据库（GEO）数据库中获取 RA 和 OA 患者的微阵列数据集，并使用 GEO2R 软件鉴定差异表达基因（DEGs）；使用数据库注释、可视化和综合发现（DAVID）和京都基因与基因组百科全书（KEGG）分别进行基因本体论分析和途径富集分析；使用互作基因检索工具（STRING）数据库构建 DEGs 的蛋白质-蛋白质相互作用网络，并使用 Cytoscape 的插件进行 PPI 网络的模块分析和途径串扰可视化。此外，使用 starBase 数据库预测差异表达 microRNA（DEM）的靶 mRNAs，并使用 RA 患者滑膜组织的逆转录定量 PCR 验证鉴定的关键基因。在 GSE55457 和 GSE55235 中鉴定出 566 个 DEG，在 GSE72564 中鉴定出 23 个 DEM。上调的 DEGs 主要富集在“细胞因子-细胞因子受体相互作用”途径中，而下调的 DEGs 富集在“PPAR 信号通路”中。前 25 个 DEGs 主要富集在“趋化因子信号通路”中。此外，选择了 6 个 miRNA 靶基因作为潜在的生物标志物，并选择了 24 个基因作为潜在的枢纽基因。实验验证表明，在 RA 滑膜组织中 CTLA4、ZAP70 和 LCK 的表达水平显著增加，而 HGF 的表达水平降低。总之，本研究鉴定的 DEGs 和关键基因可能进一步增强我们对 RA 发病机制中潜在途径的认识。这些基因也可能作为 RA 的诊断生物标志物和治疗靶点；然而，在进行生物信息学分析后，需要进行进一步的实验验证，以确定我们的结论。
Am J Transl Res. 2022-9-15
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