Bioinformatics analysis to identify key genes and pathways influencing synovial inflammation in osteoarthritis

Abstract

Osteoarthritis (OA) is a chronic arthropathy that occurs in the middle‑aged and elderly population. The present study aimed to identify gene signature differences between synovial cells from OA synovial membrane with and without inflammation, and to explain the potential mechanisms involved. The differentially expressed genes (DEGs) between 12 synovial membrane with inflammation and 12 synovial membrane without inflammation from the dataset GSE46750 were identified using the Gene Expression Omnibus 2R. The DEGs were subjected to enrichment analysis, protein‑protein interaction (PPI) analysis and module analysis. The analysis results were compared with text‑mining results. A total of 174 DEGs were identified. Gene Ontology enrichment results demonstrated that functional molecules encoded by the DEGs primarily had extracellular location, molecular functions predominantly involving 'chemokine activity' and 'cytokine activity', and were associated with biological processes, including 'inflammatory response' and 'immune response'. The Kyoto Encyclopedia of Genes and Genomes results demonstrated that DEGS may function through pathways associated with 'rheumatoid arthritis', 'chemokine signaling pathway', 'complement and coagulation cascades', 'TNF signaling pathway', 'intestinal immune networks for IgA production', 'cytokine‑cytokine receptor interaction', 'allograft rejection', 'Toll‑like receptor signaling pathway' and 'antigen processing and presentation'. The top 10 hub genes [interleukin (IL)6, IL8, matrix metallopeptidase (MMP)9, colony stimulating factor 1 receptor, FOS proto‑oncogene, AP1 transcription factor subunit, insulin‑like growth factor 1, TYRO protein tyrosine kinase binding protein, MMP3, cluster of differentiation (CD)14 and CD163] and four gene modules were identified from the PPI network using Cytoscape. In addition, text‑mining was used to identify the commonly used drugs and their targets for the treatment of OA. It was initially verified whether the results of the present study were useful for the study of OA treatment targets and pathways. The present study provided insight for the molecular mechanisms of OA synovitis. The hub genes and associated pathways derived from analysis may be targets for OA treatment. IL8 and MMP9, which were validated by text‑mining, may be used as molecular targets for the OA treatment, while other hub genes require further validation.

Figure 1.

Heat map of 100 genes from all samples. Red indicates higher gene expression and blue indicates lower gene expression.

Figure 2.

Volcanic map of all genes. Red dots indicate upregulated genes, blue dots indicate downregulated genes and gray dots indicate genes that are not regulated.

Figure 3.

The top 10 biological processes, cellular component and molecular function analysis in differentially expressed genes between non-inflammatory synovial cells and inflammatory synovial cells in osteoarthritis.

Figure 4.

Kyoto Encyclopedia of Genes and Genomes pathways in differentially expressed genes between non-inflammatory synovial cells and inflammatory synovial cells in osteoarthritis.

Figure 5.

Protein-protein interaction network constructed using Cytoscape. Sizes of dots are proportional to the score. Red dots indicate upregulation, blue dots indicate downregulation and gray edges indicate protein interactions.

Figure 6.

Commonly used drugs for the treatment of osteoarthritis and their target genes. The yellow triangles indicate drugs and the blue diamonds indicate the target genes.