基于GEO数据库对骨关节炎半月板差异基因的筛选及生物信息学分析

doi:10.20047/j.issn1673-7210.2023.32.02

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摘要目的基于GEO数据库及生物信息学来筛选骨关节炎半月板相关差异表达基因（DEGs），并分析生物学功能，以期为骨关节炎治疗提供新思路与方法。方法从GEO数据库下载数据集（GSE98918），分为正常组与骨关节炎组，各12例，利用R语言对数据进行差异分析获得DEGs，利用DAVID 6.8数据库对DEGs进行GO及KEGG信号通路分析，后运用STRING数据库及Cytoscape软件进行蛋白互作网络分析（PPI）及获得关键靶基因，进一步运用荧光定量PCR试验验证关节靶基因表达。结果最终获得220个DEGs，包括114个上调和106个下调DEGs。GO主要涉及细胞外区、细胞外间隙、胶原三聚体、胶原纤维组织、蛋白质细胞外基质等过程。KEGG主要与补体与凝血级联、金黄色葡萄球菌感染、细胞外基质受体相互作用、蛋白质消化吸收、朊病毒病等信号通路有关。PPI分析获取5个关键靶基因，包含VEGFA、MMP9、COL1A1、SPI1、ITGB1。预测前5位靶基因miRNA分别为hsa-miR-4728-5p、hsa-miR-6750-3p、hsa-miR-6727-3p、hsa-miR-6734-3p、hsa-miR-6780b-3p。结论通过运用生物信息学分析来阐述骨关节炎与健康关节之间差异基因的潜在特征，有效分析发病机制，为临床上治疗骨关节炎提供了新方向。

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	徐文飞1　　明春玉2　　梅其杰1　　岳星1　　袁长深1　　郭锦荣1　　段戡1　　梁晓辉3▲

关键词 ：骨关节炎, 半月板, 生物信息学分析, GEO数据库

Abstract：Objective To screen osteoarthritis meniscus-related differentially expressed genes (DEGs) based on the GEO database and bioinformatics, and to analyse their biological functions, with a view to providing new ideas and methods for the treatment of osteoarthritis. Methods The dataset (GSE98918) was downloaded from GEO database and divided into 12 cases in normal group and 12 cases in osteoarthritis group. The difference analysis of the data was conducted using R language to obtain DEGs. The GO and KEGG signaling pathways of DEGs were analyzed using DAVID 6.8 database. After that, STRING database and Cytoscape software were used for proteinprotein interaction (PPI) and key target genes were obtained. The expression of joint target genes was further verified by fluorescence quantitative PCR. Results The final score was 220 DEGs, including 114 up-regulated and 106 down-regulated DEGs. GO mainly involves extracellular region, extracellular space, collagen trimer, collagen fibril tissue, protein extracellular matrix, and other processes. KEGG was mainly related to signaling pathways such as complement and coagulation cascade, staphylococcus aureus infection, extracellular matrix receptor interaction, protein digestion and absorption, prion disease, etc. Five key target genes were identified by PPI analysis, including VEGFA, MMP9, COL1A1, SPI1, and ITGB1. The top five predicted miRNAs were hsa-miR-4728-5p, hsa-miR-6750-3p, hsa-miR-6727-3p, hsa-miR-6734-3p, and hsa-miR-6780b-3p. Conclusion Bioinformatics analysis is used to elucidate the potential characteristics of the differential genes between osteoarthritis and healthy joints, effectively analyze the pathogenesis, and provide a new direction for clinical treatment of osteoarthritis.

Key words： Osteoarthritis Meniscus Bioinformatics analysis GEO database

基金资助:国家自然科学基金资助项目（82160912、82060875）；
广西壮族自治区一流学科建设项目重点课题（2018xk074）。

通讯作者: ▲通讯作者

引用本文:

徐文飞1　　明春玉2　　梅其杰1　　岳星1　　袁长深1　　郭锦荣1　　段戡1　　梁晓辉3▲. 基于GEO数据库对骨关节炎半月板差异基因的筛选及生物信息学分析[J]. 中国医药导报, 2023, 20(32): 11-16.
XU Wenfei1 MING Chunyu2 MEI Qijie1 YUE Xing1 YUAN Changshen1 GUO Jinrong1 DUAN Kan1 LIANG Xiaohui3▲. Screening and bioinformatics analysis of meniscus differential genes in osteoarthritis based on GEO database. 中国医药导报, 2023, 20(32): 11-16.

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