基于生物信息学探究骨关节炎基因及治疗药物

Exploring osteoarthritis genes and therapeutic drugs based on bioinformatics

摘要:

目的： 基于生物信息学的方法探索骨关节炎（osteoarthritis，OA）的发病基因和潜在治疗药物。方法： 利用网络基因表达数据库（gene expression omnibus，GEO）下载关于骨关节炎的芯片GSE55235、GSE12021和GSE55457。通过R语言软件分析获得差异表达基因（differentially expressed genes，DEGs）。通过DAVID在线数据库对DEGs进行GO富集分析和KEGG通路分析。利用String数据库对DEGs进行蛋白互作分析并通过Cytoscape软件进行可视化。通过连接图（connectivity map，cMap）数据库分析具有潜在治疗性OA的小分子药物。结果： 共获得67个DEGs，其中18个基因表达上调，49个基因表达下调。GO分析表明，DEGs的生物学过程集中在细胞增殖、细胞黏附、机械刺激反应、成骨细胞分化和细胞对钙离子反应的调节上，参与细胞外空间、内质网膜和细胞周期蛋白依赖性蛋白激酶全酶复合物等细胞组成。分子功能包括蛋白质同二聚化活性、生长因子活性、转录因子活性、RNA聚合酶Ⅱ核心启动子近端区域序列特异性结合、蛋白质异二聚化活性、过氧化物酶活性、MAP激酶酪氨酸/丝氨酸/苏氨酸磷酸酶活性。KEGG通路分析表明，这些DEGs主要参与破骨细胞分化、TNF信号通路、PI3K-Akt信号通路、MAPK信号通路、细胞因子-细胞因子受体相互作用和Jak-STAT信号通路。从PPI网络中鉴定出前10个中枢基因是IL-6、JUN、VEGFA、ATF3、DUSP1、MYC、PTGS2、JUNB、CDKN1A和CD44。还筛选了一些用于治疗OA的潜在小分子药物，如雌二醇和来氟米特。结论： 所选择的关键基因可能是OA的诊断标志物和潜在治疗靶点，所选择的小分子药物可能是治疗OA的潜在治疗药物。

Abstract:

Objective: To explore the key pathogenic genes involved in osteoarthritis (OA) and potential therapeutic drugs for OA by bioinformatics. Methods: The expression profiles of GSE55235, GSE12021 and GSE55457 were downloaded from the gene expression omnibus (GEO) database. The microarray data were integrated to obtain differentially expressed genes (DEGs) by R software. The gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichments of DEGs were performed by DAVID online analyses. The protein-protein interaction (PPI) was analyzed by using STRING online database and visual editing by Cytoscape software. Finally, small molecule drugs with potential therapeutic OA were analyzed by connectivity map (cMap) database. Results: A total of 67 DEGs were obtained, of which 18 genes were up-regulated and 49 genes were down-regulated. GO analysis showed that the biological process of DEGs focused on cell proliferation, cell adhesion, response to mechanical stimulus, osteoblast differentiation, and the regulation of cellular response to calcium ions. The main cell composition included extracellular space, endoplasmic reticulum membrane and cyclin-dependent protein kinase holoenzyme complex. The molecular functions included protein homodimerization activity, growth factor activity, transcription factor activity, RNA polymeraseⅡcore promoter proximal region sequence-specific binding, protein heterodimerization activity, peroxidase activity, and MAP kinase tyrosine/serine/threonine phosphatase activity. KEGG pathway analysis showed that these DEGs were mainly involved in the osteoclast differentiation, TNF signaling pathway, PI3K-Akt signaling pathway, MAPK signaling pathway, cytokine-cytokine receptor interaction and Jak-STAT signaling pathway. The top 10 hub genes IL-6, JUN, VEGFA, ATF3, DUSP1, MYC, PTGS2, JUNB, CDKN1A and CD44 were identified from the PPI network. Some potential small molecular drugs for the treatment of OA, such as estradiol and leflunomide, were also been screened. Conclusion: The selected key genes may be the diagnostic markers and potential therapeutic targets of OA, and the selected small molecular drugs may be potential therapeutic drugs for OA treatment.