中国组织工程研究 ›› 2023, Vol. 27 ›› Issue (28): 4525-4532.doi: 10.12307/2023.567

• 组织构建临床实践 clinical practice in tissue construction • 上一篇    下一篇

基于生物信息学对骨关节炎Hub基因的筛选与验证

吴素雯1,陈  政1,蒋元康1,陈雷雷2   

  1. 1广州中医药大学第三临床医学院,广东省广州市  510080;2广州中医药大学第三附属医院关节骨科,广东省广州市  510145
  • 收稿日期:2022-07-21 接受日期:2022-09-15 出版日期:2023-10-08 发布日期:2023-01-29
  • 通讯作者: 陈雷雷,医学博士,副主任医师,广州中医药大学第三附属医院,广东省广州市 510145
  • 作者简介:吴素雯,女,1996年生,广东省广州市人,汉族,广州中医药大学第三临床医学院中医骨伤科学在读硕士,主要从事骨关节炎方面的研究。
  • 基金资助:
    国家自然科学基金资助项目(81673999),项目负责人:陈雷雷;广东省杰出青年科学基金项目(2015A030306037),项目负责人:陈雷雷

Screening and identification of Hub genes in osteoarthritis based on bioinformatics

Wu Suwen1, Chen Zheng1, Jiang Yuankang1, Chen Leilei2   

  1. 1The Third Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510080, Guangdong Province, China; 2Department of Joint, The Third Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510145, Guangdong Province, China
  • Received:2022-07-21 Accepted:2022-09-15 Online:2023-10-08 Published:2023-01-29
  • Contact: Chen Leilei, MD, Associate chief physician, Department of Joint, The Third Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510145, Guangdong Province, China
  • About author:Wu Suwen, Master candidate, The Third Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510080, Guangdong Province, China
  • Supported by:
    the National Natural Science Foundation of China, No. 81673999 (to CLL); Science Foundation for Outstanding Youth of Guangdong Province, No. 2015A030306037 (to CLL)

摘要:

文题释义:

Hub基因: 是基于生物信息学分析筛选出的关键基因,在疾病发生发展中发挥至关重要的作用,常作为疾病的潜在治疗靶点。
生物信息学:是一门新兴的交叉学科,结合了生物学与计算科学,在揭示疾病的分子机制中具有重要意义。

背景:骨关节炎是以关节疼痛、僵硬、肿胀为主要临床表现的一种退行性疾病,目前关于骨关节炎的发病机制尚不明确。
目的:基于生物信息学筛选骨关节炎相关数据集的Hub基因,再用细胞实验加以验证,筛选骨关节炎的关键生物标志物。
方法:从GEO数据库搜索骨关节炎相关的数据集,通过GEO2R分析筛选差异表达基因,对差异表达基因进行GO、KEGG富集分析,同时对数据集所有基因进行GESA分析,使用String网站构建差异表达基因的PPI网络,利用Cytoscape软件中的MCODE插件对PPI网络进行功能模块分析,使用插件CytoHubba筛选评分最高的10个Hub基因。提取SD大鼠膝关节半月板细胞,实验组以白细胞介素1β(10 ng/mL)干预细胞24 h复制骨关节炎症模型,以未干预组为对照,采用RT-qPCR法检测Hub基因的表达。

结果与结论:①筛选出的差异表达基因中上调基因147个、下调基因212个,GO、KEGG和GSEA分析结果显示,富集的通路及生物过程主要涉及胶原纤维组织、细胞外基质的相互作用、Th17细胞分化和白细胞介素17等;利用Cytoscape软件中的插件CytoHubba筛选MCC算法中评分最高的10个Hub基因,分别是COL1A1、COL3A1、COL5A1、COL5A2、COL6A3、LOX、LOXL1、LOXL2、POSTN、PLOD1;②RT-qPCR检测结果显示,与对照组相比,实验组COL1A1、COL3A1、COL5A1、COL5A2、COL6A3、LOXL1、LOXL2的mRNA表达降低(P < 0.000 1),LOX和POSTN的mRNA表达升高(P < 0.000 1),两组PLOD1的mRNA表达无明显差异(P > 0.05);③结果显示,骨关节炎的Hub基因表达差异可能为日后了解骨关节炎的发展提供新见解。

https://orcid.org/0000-0001-6247-7195(吴素雯)

中国组织工程研究杂志出版内容重点:组织构建;骨细胞;软骨细胞;细胞培养;成纤维细胞;血管内皮细胞;骨质疏松;组织工程

关键词: 骨关节炎, 半月板细胞, 生物信息学, Hub基因, 半月板组织

Abstract: BACKGROUND: Osteoarthritis is a degenerative disease characterized by joint pain, stiffness and swelling. At present, the pathogenesis of osteoarthritis is not clear.
OBJECTIVE: To screen out Hub genes in osteoarthritis-related data sets based on bioinformatics and then identify them using cell experiments to screen the key biomarkers of osteoarthritis.
METHODS: Osteoarthritis-related data sets were searched from GEO database and differentially expressed genes were identified by GEO2R analysis. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed for differentially expressed genes. Meanwhile, a gene set enrichment analysis was performed for all genes in the data set. Protein-protein network was constructed by inputting differentially expressed genes on String website. The functional modules of the protein-protein network were analyzed by MCODE plug-in in Cytoscape software and the top 10 Hub genes were identified by CytoHubba plug-in. The knee meniscus cells of Sprague-Dawley rats were extracted. In the experimental group, interleukin-1β (10 ng/mL) was used to intervene the cells for 24 hours. Cells with no intervention were used as controls. The expression of Hub genes was detected by RT-qPCR.
RESULTS AND CONCLUSION: A total of 147 differentially expressed genes were up-regulated and 212 down-regulated. Gene ontology, Kyoto Encyclopedia of Genes and Genomes enrichment and gene set enrichment analyses showed that the enriched pathways and biological processes mainly involved collagen fiber organization, extracellular matrix interaction, Th17 cell differentiation and interleukin-17. CytoHubba, a plug-in in Cytoscape software, was used to identify the top 10 Hub genes in MCC algorithm, including COL1A1, COL3A1, COL5A1, COL5A2, COL6A3, LOX, LOXL1, LOXL2, POSTN, and PLOD1. RT-qPCR results showed that compared with the control group, the mRNA expression of COL1A1, COL3A1, COL5A1, COL5A2, COL6A3, LOXL1, and LOXL2 decreased (P < 0.000 1), while the mRNA expression of LOX and POSTN increased (P < 0.000 1). However, there was no significant difference in the PLOD1 expression between the two groups (P > 0.05). To conclude, differentially expressed Hub genes in osteoarthritis may provide new insights into the development of osteoarthritis in the future.   

Key words: osteoarthritis, meniscal cell, bioinformatics, Hub gene, meniscus

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