骨关节炎关键基因与治疗药物的生物信息学筛选

丁晓; 史晨辉; 孟德峰; 王海英; 韩飞; 蒲沛东; 王梦雨; 王维山

doi:10.13422/j.cnki.syfjx.20190304

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骨关节炎关键基因与治疗药物的生物信息学筛选

数据挖掘 | 更新时间：2021-02-09

- 骨关节炎关键基因与治疗药物的生物信息学筛选
- Bioinformatics Screening of Key Genes and Candidate Therapeutic Drugs of Osteoarthritis
- 中国实验方剂学杂志 2019年25卷第9期页码：189-196
- 作者机构：
  
  石河子大学　医学院　第一附属医院　骨科中心，新疆　石河子　 832000
- 作者简介：
  
  丁晓，在读硕士，从事骨关节疾病研究，E-mail：943611477@qq.com
  王维山，教授，主任医师，从事骨关节疾病研究，Tel：0993-2859427，E-mail：wwsmc2002@sina.com
- 基金信息：
  
  国家自然科学基金项目(81660374;81760404)
- DOI：10.13422/j.cnki.syfjx.20190304
  中图分类号： R22; R274; R28; C37; R344+ 13
- 收稿日期：2018-09-13，
  
  网络出版日期：2018-11-16，
  
  纸质出版日期：2019-05-05
- 稿件说明：
移动端阅览
丁晓, 史晨辉, 孟德峰, 等. 骨关节炎关键基因与治疗药物的生物信息学筛选[J]. 中国实验方剂学杂志, 2019,25(9):189-196.

Xiao DING, Chen-hui SHI, De-feng MENG, et al. Bioinformatics Screening of Key Genes and Candidate Therapeutic Drugs of Osteoarthritis[J]. Chinese journal of experimental traditional medical formulae, 2019, 25(9): 189-196.
丁晓, 史晨辉, 孟德峰, 等. 骨关节炎关键基因与治疗药物的生物信息学筛选[J]. 中国实验方剂学杂志, 2019,25(9):189-196. DOI： 10.13422/j.cnki.syfjx.20190304.

Xiao DING, Chen-hui SHI, De-feng MENG, et al. Bioinformatics Screening of Key Genes and Candidate Therapeutic Drugs of Osteoarthritis[J]. Chinese journal of experimental traditional medical formulae, 2019, 25(9): 189-196. DOI： 10.13422/j.cnki.syfjx.20190304.

摘要

目的：

利用生物信息学方法探寻骨关节炎(OA)关键基因及潜在治疗药物。

方法：

利用基因表达大棚车(GEO)下载GSE55235芯片数据，R语言3.5.0筛选差异表达基因，David在线数据库对差异表达基因进行基因本体(GO)富集分析及京都基因与基因组百科全书(KEGG)信号通路分析，String 10.5在线数据库构建分析蛋白质之间的相互作用，Cytoscape v3.6.1软件进行可视化编辑，MCODE插件进行子网络模块分析，筛选出OA发生过程中的核心基因。最后根据关联性图谱(CMap)分析具有潜在治疗OA的小分子药物。

结果：

筛选出556个差异基因，其中上调252个，下调304个，这些差异基因主要参与细胞外基质组织、炎症反应、细胞黏附、免疫应答以及胶原结合等方面；KEGG通路分析发现差异基因主要参与细胞外基质受体相互作用，磷脂酰肌醇3-激酶／蛋白激酶B(PI3K/Akt)信号通路及破骨细胞分化；蛋白质相互作用发现白细胞介素-6(IL-6)，JUN，血管内皮生长因子

(VEGFA)，FOS

MYC和早期生长反应蛋白-1(EGR-1)，转录激活因子-3(ATF-3)等关键基因；筛选出了一些潜在治疗OA的小分子药物，如石蒜碱和茴香霉素等。

结论：

所筛选的关键基因可能成为诊断OA的标志物或潜在治疗OA的靶点；筛选出来的小分子药物可作为治疗OA的关键药物进行研发。

Abstract

Objective:

To explore the key genes and potential therapeutic drugs for osteoarthritis(OA) by bioinformatics.

Method:

The microarray data GSE55235 was downloaded from the data platform of gene expression omnibus(GEO) and the differentially expressed genes were screened by R language software (3.5.0). Then

the differentially expressed genes were subjected to gene ontology(GO) enrichment analysis and Kyoto encyclopedia of genes and genomes(KEGG) signaling pathway analysis with David online database.The protein-protein interaction was analyzed by String 10.5 online database and visual editing was analyzed by Cytoscape v3.6.1 software. Subnetwork module analysis was utilized by MCODE plugin to screen the core genes in the process of OA.Finally

small molecule drugs with potential treatment for OA were analyzed by connectivity map(CMap) database.

Result:

A total of 556 differentially expressed genes were screened

among which 252 were up-regulated and 304 were down-regulated.These genes were mainly involved in extracellular matrix(ECM) organization

inflammatory response

cell adhesion

immune response

collagen binding

etc.The analysis of KEGG pathway showed that differential genes were mainly involved in ECM-receptor interaction

phosphatidylinositol 3 kinase-protein kinase B(PI3K/Akt) signaling pathway and osteoclast differentiation.Some genes

such as interleukin-6(IL-6)

JUN

vascular endothelial growth factor

(VEGFA)

FOS

MYC and early growth response gene-1(EGR-1)

activating transcription factor-3(ATF-3)

playing critical role in the process of OA were identified by protein-protein interaction.Some potential small molecular drugs for the treatment of OA have also been screened

such as lycorine and anisomycin.

Conclusion:

The selected key genes may be targets for the diagnosis of OA or potential targets for the treatment of OA

and the selected small molecular drugs can be developed as the key drugs for the treatment of OA.

关键词

Keywords

references

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