Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis

被引:59
|
作者
Cai, Weisong [1 ]
Li, Haohuan [1 ]
Zhang, Yubiao [1 ]
Han, Guangtao [1 ]
机构
[1] Wuhan Univ, Dept Orthoped, Renmin Hosp, Wuhan, Peoples R China
来源
PEERJ | 2020年 / 8卷
基金
中国国家自然科学基金;
关键词
Osteoarthritis; Synovial; Immune infiltration; Bioinformatics analysis; CARTILAGE; PROGRESSION; EXPRESSION; MMP-1; RISK;
D O I
10.7717/peerj.8390
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background. Osteoarthritis (OA) is the most common chronic degenerative joint disease and is mainly characterized by cartilage degeneration, subcartilage bone hyperplasia, osteophyte formation and joint space stenosis. Recent studies showed that synovitis might also be an important pathological change of OA. However, the molecular mechanisms of synovitis in OA are still not well understood. Objective. This study was designed to identify key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis. Materials and Methods. The gene expression profiles of GSE12021, GSE55235 and GSE55457 were downloaded from the GEO database. The differentially expressed genes (DEGs) were identified by the LIMMA package in Bioconductor, and functional enrichment analyses were performed. A protein-protein interaction network (PPI) was constructed, and module analysis was performed using STRING and Cytoscape. The CIBERSORT algorithm was used to analyze the immune infiltration of synovial tissue between OA and normal controls. Results. A total of 106 differentially expressed genes, including 68 downregulated genes and 38 upregulated genes, were detected. The PPI network was assessed, and the most significant module containing 14 hub genes was identified. Gene Ontology analysis revealed that the hub genes were significantly enriched in immune cell chemotaxis and cytokine activity. KEGG pathway analysis showed that the hub genes were significantly enriched in the rheumatoid arthritis signaling pathway, IL-17 signaling pathway and cytokine-cytokine receptor interaction signaling pathway. The immune infiltration profiles varied significantly between osteoarthritis and normal controls. Compared with normal tissue, OA synovial tissue contained a higher proportion of memory B cells, naive CD4+ T cells, regulatory T cells, resting dendritic cells and resting mast cells, while naive CD4+ T cells, activated NK cells, activated mast cells and eosinophils contributed to a relatively lower portion (P > 0.05). Finally, the expression levels of 11 hub genes were confirmed by RT-PCR. Conclusion. The hub genes and the difference in immune infiltration in synovial tissue between osteoarthritis and normal controls might provide new insight for understanding OA development.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] Identification of differential key biomarkers in the synovial tissue between rheumatoid arthritis and osteoarthritis using bioinformatics analysis
    Zhang, Runrun
    Zhou, Xinpeng
    Jin, Yehua
    Chang, Cen
    Wang, Rongsheng
    Liu, Jia
    Fan, Junyu
    He, Dongyi
    CLINICAL RHEUMATOLOGY, 2021, 40 (12) : 5103 - 5110
  • [2] Identification of differential key biomarkers in the synovial tissue between rheumatoid arthritis and osteoarthritis using bioinformatics analysis
    Runrun Zhang
    Xinpeng Zhou
    Yehua Jin
    Cen Chang
    Rongsheng Wang
    Jia Liu
    Junyu Fan
    Dongyi He
    Clinical Rheumatology, 2021, 40 : 5103 - 5110
  • [3] Identification of key biomarkers and related immune cell infiltration in cervical cancer tissue based on bioinformatics analysis
    Zhu, Guang
    Xiong, Zhihui
    Chen, Wenzeng
    Zhu, Zhen
    Wang, Wei
    SCIENTIFIC REPORTS, 2023, 13 (01)
  • [4] Identification of key biomarkers and immune infiltration in the thoracic acute aortic dissection by bioinformatics analysis
    Luo, Jun
    Shi, Haoming
    Ran, Haoyu
    Zhang, Cheng
    Wu, Qingchen
    Shao, Yue
    BMC CARDIOVASCULAR DISORDERS, 2023, 23 (01)
  • [5] Identification of Key Diagnostic Markers and Immune Infiltration in Osteoarthritis
    Yan, Mingyue
    Zhao, Haibo
    Sun, Zewen
    Chen, Jinli
    Zhang, Yi
    Gao, Jiake
    Yu, Tengbo
    COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING, 2023, 26 (02) : 410 - 423
  • [6] Integrated bioinformatics and clinical data identify three novel biomarkers for osteoarthritis diagnosis and synovial immune
    Zhu, Zheng
    Tu, Bizhi
    Peng, Cheng
    Xu, Xun
    Lu, Peizhi
    Ning, Rende
    SCIENTIFIC REPORTS, 2025, 15 (01):
  • [7] Identification of Disease-Specific Hub Biomarkers and Immune Infiltration in Osteoarthritis and Rheumatoid Arthritis Synovial Tissues by Bioinformatics Analysis
    Sun, Fei
    Zhou, Jian Lin
    Peng, Pu Ji
    Qiu, Chen
    Cao, Jia Rui
    Peng, Hao
    DISEASE MARKERS, 2021, 2021
  • [8] Identification of aging-related biomarkers and immune infiltration characteristics in osteoarthritis based on bioinformatics analysis and machine learning
    Zhou, JiangFei
    Huang, Jian
    Li, ZhiWu
    Song, QiHe
    Yang, ZhenYu
    Wang, Lu
    Meng, QingQi
    FRONTIERS IN IMMUNOLOGY, 2023, 14
  • [9] Identification and verification of ferroptosis-related genes in the synovial tissue of osteoarthritis using bioinformatics analysis
    Xia, Lin
    Gong, Ningji
    FRONTIERS IN MOLECULAR BIOSCIENCES, 2022, 9
  • [10] Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis
    Xingwang Zhao
    Longlong Zhang
    Juan Wang
    Min Zhang
    Zhiqiang Song
    Bing Ni
    Yi You
    Journal of Translational Medicine, 19