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. 2023 Jan 21;18(1):58.
doi: 10.1186/s13018-023-03541-x.

Mechanism of immune infiltration in synovial tissue of osteoarthritis: a gene expression-based study

Qingyu Zhang  1 , Chao Sun  1 , Xuchang Liu  1 , Chao Zhu  1 , Chuncheng Ma  1 , Rongjie Feng  2
Affiliations

Mechanism of immune infiltration in synovial tissue of osteoarthritis: a gene expression-based study

Qingyu Zhang et al. J Orthop Surg Res. .

Abstract

Background: Osteoarthritis is a chronic degenerative joint disease, and increasing evidences suggest that the pathogenic mechanism involves immune system and inflammation.

Aims: The aim of current study was to uncover hub genes linked to immune infiltration in osteoarthritis synovial tissue using comprehensive bioinformatics analysis and experimental confirmation.

Methods: Multiple microarray datasets (GSE55457, GSE55235, GSE12021 and GSE1919) for osteoarthritis in Gene Expression Omnibus database were downloaded for analysis. Differentially expressed genes (DEGs) were identified using Limma package in R software, and immune infiltration was evaluated by CIBERSORT algorithm. Then weighted gene co-expression network analysis (WGCNA) was performed to uncover immune infiltration-associated gene modules. Protein-protein interaction (PPI) network was constructed to select the hub genes, and the tissue distribution of these genes was analyzed using BioGPS database. Finally, the expression pattern of these genes was confirmed by RT-qPCR using clinical samples.

Results: Totally 181 DEGs between osteoarthritis and normal control were screened. Macrophages, mast cells, memory CD4 T cells and B cells accounted for the majority of immune cell composition in synovial tissue. Osteoarthritis synovial showed high abundance of infiltrating resting mast cells, B cells memory and plasma cells. WGCNA screened 93 DEGs related to osteoarthritis immune infiltration. These genes were involved in TNF signaling pathway, IL-17 signaling pathway, response to steroid hormone, glucocorticoid and corticosteroid. Ten hub genes including MYC, JUN, DUSP1, NFKBIA, VEGFA, ATF3, IL-6, PTGS2, IL1B and SOCS3 were selected by using PPI network. Among them, four genes (MYC, JUN, DUSP1 and NFKBIA) specifically expressed in immune system were identified and clinical samples revealed consistent change of these four genes in synovial tissue retrieved from patients with osteoarthritis.

Conclusion: A 4-gene-based diagnostic model was developed, which had well predictive performance in osteoarthritis. MYC, JUN, DUSP1 and NFKBIA might be biomarkers and potential therapeutic targets in osteoarthritis.

Keywords: Biomarkers; Immune infiltration; Osteoarthritis; Synovial; Weighed gene co-expression network analysis.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Differential expression analysis. A Volcano plot showing the differentially expressed genes between osteoarthritis vs. normal control; B Heatmap showing the expression pattern of genes in between osteoarthritis versus normal control
Fig. 2
Fig. 2
Evaluation of immune cells infiltration. A Landscape of immune cells infiltration in synovial membrane tissue of normal control (left) or osteoarthritis patients (right); B Correlation analysis among 22 immune cells in tumor samples; and C Violin plot showing the differences on infiltration abundance of 22 immune cells in synovial membrane tissue of normal control (blue) and osteoarthritis patients (yellow)
Fig. 3
Fig. 3
Weighed gene co-expression network analysis. A The scale independence (left) and mean connectivity (right) for various soft threshold powers; B Cluster dendrogram. Each color branch represents a color-coded module containing a highly interconnected set of genes; C Disease status correlated co-expression modules. X-axis represents different gene modules, and Y-axis represents the overall correlation coefficient between genes in each module and disease status; and D Module–trait relationships and P values for selected traits (osteoarthritis and immune cells)
Fig. 4
Fig. 4
Functional enrichment analysis. A Venn diagram showing the common genes between gene modules and DEGs; B The significantly enriched KEGG pathways for common genes identified from Venn analysis; and C The significantly enriched biological processes terms for common genes identified from Venn analysis
Fig. 5
Fig. 5
Protein–protein interaction network. A The protein–protein interaction network for common genes identified from Venn analysis. Pink nodes and yellow nodes represent the genes identified from pink or yellow modules in WGCNA; B Identification of hub genes from the network by cytoHubba algorithm
Fig. 6
Fig. 6
Regulatory network for feature genes. The lncRNA/circRNA–miRNA–target regulatory network. Yellow nodes represent feature genes; red nodes represent miRNAs; blue nodes represent lncRNAs; and green nodes represent circRNAs
Fig. 7
Fig. 7
Validation of feature genes expression in GSE1919 dataset and clinical samples. A Boxplots showing the expression of feature genes in synovial membrane tissue of normal control and osteoarthritis patients. *P < 0.05; **P < 0.01; ***P < 0.01; B Expression of MYC, JUN and DUSP1 in synovial tissues of osteoarthritis patients and normal controls was determined by RT-qPCR. GAPDH was used as internal reference
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References

    1. Abramoff B, Caldera FE. Osteoarthritis: pathology, diagnosis, and treatment options. Med Clin N Am. 2020;104:293–311. doi: 10.1016/j.mcna.2019.10.007. - DOI - PubMed
    1. Barnett R. Osteoarthritis. Lancet (London, England) 2018;391:1985. doi: 10.1016/s0140-6736(18)31064-x. - DOI - PubMed
    1. Katz JN, Arant KR, Loeser RF. Diagnosis and treatment of hip and knee osteoarthritis: a review. JAMA. 2021;325:568–578. doi: 10.1001/jama.2020.22171. - DOI - PMC - PubMed
    1. Parekh SM, Fernandes GS, Moses JP, et al. Risk factors for knee osteoarthritis in retired professional footballers: a cross-sectional study. Clin J Sport Med Off J Can Acad Sport Med. 2021;31:281–288. doi: 10.1097/jsm.0000000000000742. - DOI - PMC - PubMed
    1. Allen KD, Thoma LM, Golightly YM. Epidemiology of osteoarthritis. Osteoarthr Cartil. 2022;30:184–195. doi: 10.1016/j.joca.2021.04.020. - DOI - PMC - PubMed

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