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2022, Vol. 26 ›› Issue (12): 1907-1914

Screening of differentially expressed genes in osteoarthritis by gene chip technique and verification using quantitative real-time PCR

Chen Cai1, Zeng Ping2, Liu Jinfu1, Qian Xiaofen1, Lu Guanyu1, Xiong Bo1, Chen Lihua1, Huang Yue1   

  1. 1Graduate School of Guangxi University of Chinese Medicine, Nanning 530299, Guangxi Zhuang Autonomous Region, China; 2the First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning 530023, Guangxi Zhuang Autonomous Region, China

  • Received:2021-04-26 Revised:2021-04-27 Accepted:2021-06-15 Online:2022-04-28 Published:2021-12-14

  • Contact: Zeng Ping, MD, Professor, the First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning 530023, Guangxi Zhuang Autonomous Region, China

  • About author:Chen Cai, Master candidate, Graduate School of Guangxi University of Chinese Medicine, Nanning 530299, Guangxi Zhuang Autonomous Region, China

  • Supported by:

    the National Natural Science Foundation of China, No. 81960876 (to ZP); College-level Project of the First Affiliated Hospital of Guangxi University of Chinese Medicine, No. 2017ZD002 (to ZP); Guangxi Traditional Chinese Medicine Appropriate Technology Development and Promotion Project, No. GZSY21-14 (to ZP)



Abstract: BACKGROUND: The incidence of osteoarthritis is increasing as the population ages, and there is still no effective treatment. Gene chip technology has been widely used in the diagnosis and treatment of diseases.
OBJECTIVE: To identify the key genes in osteoarthritis by using integrative bioinformatics and weighted gene co-expression network analysis (WGCNA) and to investigate its mechanism.
METHODS: Four original data sets of synovial tissue chips in osteoarthritis patients were downloaded from Gene Expression Omnibus database, and the immune infiltration was analyzed by CIBERSORT algorithm. Differentially expressed genes were screened by R-language software. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the selected differentially expressed genes. The text files of these differentially expressed genes were obtained through the online tool STRING (http://string-db.org/) and were imported into the Cytoscape software to edit the visual protein-protein interaction network. R-language software was used to build a bar chart of differentially expressed genes to obtain the top 10 differentially expressed genes in degree values. The four chip data sets were integrated to screen out the genes with the highest correlation of gene co-expression module by WGCNA algorithm, and the intersection genes were screened by Venn analysis method. The synovial tissues of four patients with osteoarthritis and four patients with joint trauma were collected for quantitative real-time PCR verification.  

RESULTS AND CONCLUSION: (1)A total of 107 differentially expressed genes were screened out, including 46 up-regulated genes and 61 down-regulated genes. (2)The gene ontology enrichment results showed that differentially expressed genes were mainly involved in biological processes such as positive regulation of ossification and cell adhesion. KEGG pathways were mainly enriched in rheumatoid arthritis pathways, nuclear factor-κB signaling pathways, and receptor protein tyrosine kinase B signaling pathways. Immune infiltration results indicated that the contents of M0 macrophages and M2 macrophages were relatively high in the synovial tissue of patients with osteoarthritis. (3)The obtained intersection genes included matrix metalloproteinase 1, tissue inhibitor of metalloproteinase 4, laminin subunit alpha 3, and follistatin 3. The results of quantitative real-time PCR confirmed that there were significant differences in the expression levels of tissue inhibitor of metalloproteinase 4, laminin subunit alpha 3, and follistatin 3 between the synovial tissue samples from patients with osteoarthritis and those with joint trauma. (4)Therefore, tissue inhibitor of metalloproteinase 4, laminin subunit alpha 3, and follistatin 3 may be therapeutic targets for osteoarthritis.

Key words:gene chip, bioinformatics, weighted gene co-expression network analysis, osteoarthritis, synovium, differentially expressed gene, immune cell, signaling pathway


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