背景：研究发现超过90%的骨关节炎患者被证实存在滑膜病变，滑膜炎的发生还可能促进软骨退变。寻找滑膜病变的产生机制有助于找到精确的治疗靶点，获得良好预后。

目的：通过生物信息学途径，探究骨关节炎发生发展过程中滑膜组织的关键基因，为骨关节炎的研究提供新的思路。

方法：从公共数据库GEO下载骨关节炎滑膜相关芯片数据集GSE、GSE、GSE、GSE，纳入骨关节炎滑膜组织40例，正常滑膜组织36例。利用R软件筛选差异表达基因、GO功能富集以及KEGG通路富集分析，STRING在线分析工具及Cytoscape软件进一步筛选关键基因。

结果与结论：①共筛选出骨关节炎与健康对照之间的差异表达基因447个，其中上调201个，下调246个；这些基因主要富集在炎症反应、细胞外基质、胶原蛋白、血管发育的调控等功能以及MAPK信号通路、肿瘤坏死因子信号通路、花生四烯酸代谢等方面；分析蛋白质相互作用后筛选出基质金属蛋白酶9、白细胞介素6、血管内皮生长因子A、JUN、前列腺素内过氧化物合酶2、CXCL8、MYC、表皮生长因子受体8个关键基因；②利用生物信息学分析所筛选出来的关键基因血管内皮生长因子A、基质金属蛋白酶9、JUN、前列腺素内过氧化物合酶2可能成为骨关节炎诊断的生物学标志物以及治疗的潜在靶点。

BACKGROUND:Over 90% of patients with osteoarthritis suffer from synovial lesions, and the occurrence of synovitis may also promote cartilage explore the potential mechanisms of synovial lesions is beneficial to find precise treatment targets and achieve good long-term prognosis.

OBJECTIVE:To identify candidate hub genes in the synovial tissues during the development of osteoarthritis by bioinformatics analysis, and to further provide new insights for osteoarthritis study.

METHODS:The osteoarthritis synovial-associated chip data sets GSE, GSE, GSE, and GSE were downloaded from the public database GEO, including 40 cases of osteoarthritic synovial tissue and 36 cases of normal synovial tissue. R software was used to screen differentially expressed genes using Gene ontology function enrichment and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and hub genes are selected by STRING online analysis tool and Cytoscape software.

RESULTS AND CONCLUSION:Total 447 differentially expressed genes were selected between osteoarthritis and healthy control, including 201 up-regulated and 246 down-regulated genes. These differentially expressed genes were mainly enriched in inflammatory response, regulations of extracellular matrix,collagen, blood vessel development, as well as MAPK signaling pathway, tumor necrosis factor signaling pathway, arachidonic acid metabolism. Eight hub genes were screened by analyzing the protein interactions, which included matrix metalloproteinase 9, interleukin 6, vascular endothelial growth factor A,JUN, prostaglandin peroxide enzyme 2, CXCL8, MYC, epidermal growth factor receptor. The hub genes selected by bioinformatics analysis such as vascular endothelial growth factor A, matrix metalloproteinase 9, JUN, and prostaglandin peroxidase 2 may be biomarkers for the diagnosis of osteoarthritis and potential targets for treatment.

0 引言 Introduction

骨关节炎是一种以软骨变性、滑膜炎以及骨赘形成等为特征的慢性炎症和退行性关节疾病，常导致老年人慢性残疾。目前骨关节炎的致病机制尚不明确。以往骨关节炎的研究热点大多集中于软骨组织，随着研究不断深入，滑膜病变在骨关节炎发生发展中的作用引起人们的重视。研究发现超过90%的骨关节炎患者被证实存在滑膜病变，且骨关节炎滑膜的病变程度与关节严重的疼痛和功能障碍有关[1]。此外，滑膜炎的发生还可能促进软骨退变[2]。寻找滑膜病变的产生机制有助于找到精确的治疗靶点，获得良好预后，从而从根本上解决骨关节炎问题，这有赖于生物学标志物的鉴定。因此寻找疾病差异基因、相关信号通路是骨关节炎发病机制及治疗靶点研究中亟待解决的问题。

随着基因芯片和RNA测序技术的迅速发展，生物信息学分析成为一个重要的研究方向，为识别可靠的功能差异表达基因 (differently expressed genes，DEGs)、微小RNA (miRNAs)、环状RNA (cir RNAs)和长链非编码RNA(IncRNAs)提供了新的线索和核心数据，在筛选各种疾病候选生物标志物方面也展示出了巨大的优势。然而，由于样本源于不同的测序平台，表达的mRNA结果与基因谱对应不一致，很大一部分骨关节炎的生物信息学分析仅限于单一芯片数据，结果较为局限，可靠性差，而多组基因芯片数据可有效地解决这一问题。此外，随着大量研究的进行，大量上传至公共数据库的基因信息未被有效利用。因此，此次研究利用多组基因芯片数据再次进行筛选与分析，拟鉴定出更具有生物学意义的标志物，为骨关节炎的研究提供新思路。

文章来源：《诊断病理学杂志》 网址: http://www.zdblxzz.cn/qikandaodu/2021/0226/394.html