Department of Orthopedics of the Second Hospital of Jilin University, Ziqiang Street 218, Changchun, Jilin 130041, China.
Research Centre of the Second Hospital of Jilin University, Ziqiang Street 218, Changchun, Jilin 130041, China.
Biomed Res Int. 2018 Aug 14;2018:9482726. doi: 10.1155/2018/9482726. eCollection 2018.
Osteoarthritis (OA) is one of the most common diseases worldwide, but the pathogenic genes and pathways are largely unclear. The aim of this study was to screen and verify hub genes involved in OA and explore potential molecular mechanisms. The expression profiles of GSE12021 and GSE55235 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 39 samples, including 20 osteoarthritis synovial membranes and 19 matched normal synovial membranes. The raw data were integrated to obtain differentially expressed genes (DEGs) and were deeply analyzed by bioinformatics methods. The Gene Ontology (GO) and pathway enrichment of DEGs were performed by DAVID and Kyoto Encyclopedia of Genes and Genomes (KEGG) online analyses, respectively. The protein-protein interaction (PPI) networks of the DEGs were constructed based on data from the STRING database. The top 10 hub genes VEGFA, IL6, JUN, IL1, MYC, IL4, PTGS2, ATF3, EGR1, and DUSP1 were identified from the PPI network. Module analysis revealed that OA was associated with significant pathways including TNF signaling pathway, cytokine-cytokine receptor interaction, and osteoclast differentiation. The qRT-PCR result showed that the expression level of IL6, VEGFA, JUN, IL-1, and ATF3 was significantly increased in OA samples (p < 0.05), and these candidate genes could be used as potential diagnostic biomarkers and therapeutic targets of OA.
骨关节炎（OA）是全球最常见的疾病之一，但致病基因和途径在很大程度上尚不清楚。本研究旨在筛选和验证 OA 相关的枢纽基因，并探讨潜在的分子机制。从基因表达综合数据库（GEO）下载了 GSE12021 和 GSE55235 的表达谱数据，其中包含 39 个样本，包括 20 例骨关节炎滑膜和 19 例匹配的正常滑膜。通过生物信息学方法对原始数据进行整合，获得差异表达基因（DEGs），并进行深入分析。使用 DAVID 和京都基因与基因组百科全书（KEGG）在线分析分别对 DEGs 的基因本体论（GO）和通路富集进行分析。基于 STRING 数据库中的数据构建 DEGs 的蛋白质-蛋白质相互作用（PPI）网络。从 PPI 网络中确定了排名前 10 的枢纽基因 VEGFA、IL6、JUN、IL1、MYC、IL4、PTGS2、ATF3、EGR1 和 DUSP1。模块分析表明，OA 与 TNF 信号通路、细胞因子-细胞因子受体相互作用和破骨细胞分化等显著途径有关。qRT-PCR 结果显示，OA 样本中 IL6、VEGFA、JUN、IL-1 和 ATF3 的表达水平显著升高（p<0.05），这些候选基因可以作为 OA 的潜在诊断生物标志物和治疗靶点。
