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

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. This study was designed to identify key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis. 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. 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. 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.

Cai W ，Li H ，Zhang Y ，Han G ... -  《PeerJ》

Identification of Disease-Specific Hub Biomarkers and Immune Infiltration in Osteoarthritis and Rheumatoid Arthritis Synovial Tissues by Bioinformatics Analysis.

Osteoarthritis (OA) and rheumatoid arthritis (RA) are well-known cause of joint disability. Although they have shown the analogous clinical features involving chronic synovitis that progresses to cartilage and bone destruction, the pathogenesis that initiates and perpetuates synovial lesions between RA and OA remains elusive. This study is aimed at identifying disease-specific hub genes, exploring immune cell infiltration, and elucidating the underlying mechanisms associated with RA and OA synovial lesion. Gene expression profiles (GSE55235, GSE55457, GSE55584, and GSE12021) were selected from Gene Expression Omnibus for analysis. Differentially expressed genes (DEGs) were identified by the "LIMMA" package in Bioconductor. The DEGs were identified by Gene Ontology (GO) and KEGG pathway analysis. A protein-protein interaction network was constructed to identify candidate hub genes by using STRING and Cytoscape. Hub genes were identified by validating from GSE12021. Furthermore, we employed the CIBERSORT website to assess immune cell infiltration between OA and RA. Finally, we explored the correlation between the levels of hub genes and relative proportion of immune cells in OA and RA. We identified 68 DEGs which were mainly enriched in immune response and chemokine signaling pathway. Six hub genes with a cutoff of AUC > 0.80 by ROC analysis and relative expression of P < 0.05 were identified successfully. Compared with OA, the RA synovial tissues consisted of a higher proportion of 7 immune cells, whereas 4 immune cells were found in relatively lower proportion (P < 0.05). In addition, the levels of 6 hub genes were closely associated with relative proportion of 11 immune cells in OA and RA. We used bioinformatics analysis to identify hub genes and explored immune cell infiltration of immune microenvironment in synovial tissues. Our results should offer insights into the underlying molecular mechanisms of synovial lesion and provide potential target for immune-based therapies of OA and RA.

Sun F ，Zhou JL ，Peng PJ ，Qiu C ，Cao JR ，Peng H ... -  《-》

Screening and identification of potential hub genes and immune cell infiltration in the synovial tissue of rheumatoid arthritis by bioinformatic approach.

Rheumatoid arthritis (RA) is an autoimmune disease that affects individuals of all ages. The basic pathological manifestations are synovial inflammation, pannus formation, and erosion of articular cartilage, bone destruction will eventually lead to joint deformities and loss of function. However, the specific molecular mechanisms of synovitis tissue in RA are still unclear. Therefore, this study aimed to screen and explore the potential hub genes and immune cell infiltration in RA. Three microarray datasets (GSE12021, GSE55457, and GSE55235), from the Gene Expression Omnibus (GEO) database, have been analyzed to explore the potential hub genes and immune cell infiltration in RA. First, the LIMMA package was used to screen the differentially expression genes (DEGs) after removing the batch effect. Then the clusterProfiler package was used to perform functional enrichment analyses. Second, through weighted coexpression network analysis (WGCNA), the key module was identified in the coexpression network of the gene set. Third, the protein-protein interaction (PPI) network was constructed through STRING website and the module analysis was performed using Cytoscape software. Fourth, the CIBERSORT and ssGSEA algorithm were used to analyze the immune status of RA and healthy synovial tissue, and the associations between immune cell infiltration and RA-related diagnostic biomarkers were evaluated. Fifth, we used the quantitative reverse transcription-polymerase chain reaction (qRT-PCR) to validate the expression levels of the hub genes, and ROC curve analysis of hub genes for discriminating between RA and healthy tissue. Finally, the gene-drug interaction network was constructed using DrugCentral database, and identification of drug molecules based on hub genes using the Drug Signature Database (DSigDB) by Enrichr. A total of 679 DEGs were identified, containing 270 downregulated genes and 409 upregulated genes. DEGs were primarily enriched in immune response and chemokine signaling pathways, according to functional enrichment analysis of DEGs. WGCNA explored the co-expression network of the gene set and identified key modules, the blue module was selected as the key module associated with RA. Seven hub genes are identified when PPI network and WGCNA core modules are intersected. Immune infiltration analysis using CIBERSORT and ssGSEA algorithms revealed that multiple types of immune infiltration were found to be upregulated in RA tissue compared to normal tissue. Furthermore, the levels of 7 hub genes were closely related to the relative proportions of multiple immune cells in RA. The results of the qRT-PCR demonstrated that the relative expression levels of 6 hub genes (CD27, LCK, CD2, GZMB, IL7R, and IL2RG) were up-regulated in RA synovial tissue, compared with normal tissue. Simultaneously, ROC curves indicated that the above 6 hub genes had strong biomarker potential for RA (AUC >0.8). Through bioinformatics analysis and qRT-PCR experiment, our study ultimately discovered 6 hub genes (CD27, LCK, CD2, GZMB, IL7R, and IL2RG) that closely related to RA. These findings may provide valuable direction for future RA clinical diagnosis, treatment, and associated research.

Feng ZW ，Tang YC ，Sheng XY ，Wang SH ，Wang YB ，Liu ZC ，Liu JM ，Geng B ，Xia YY ... -  《Heliyon》

Identification of differential key biomarkers in the synovial tissue between rheumatoid arthritis and osteoarthritis using bioinformatics analysis.

Rheumatoid arthritis (RA) and osteoarthritis (OA) are two common joint diseases with similar clinical manifestations. Our study aimed to identify differential gene biomarkers in the synovial tissue between RA and OA using bioinformatics analysis and validation. GSE36700, GSE1919, GSE12021, GSE55235, GSE55584, and GSE55457 datasets were downloaded from the Gene Expression Omnibus database. A total of 57 RA samples and 46 OA samples were included. The differentially expressed genes (DEGs) were identified. The Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were also performed. Protein-protein interaction (PPI) network of DEGs and the hub genes were constructed and visualized via Search Tool for the Retrieval of Interacting Genes/Proteins, Cytoscape, and R. Selected hub genes were validated via reverse transcription-polymerase chain reaction. A total of 41 DEGs were identified. GO functional enrichment analysis showed that DEGs were enriched in immune response, signal transduction, regulation of immune response for biological process, in plasma membrane and extracellular region for cell component, and antigen binding and serine-type endopeptidase activity for molecular function. KEGG pathway analysis showed that DEGs were enriched in cytokine-cytokine receptor interaction and chemokine signaling pathway. PPI network analysis established 70 nodes and 120 edges and 15 hub genes were identified. The expression of CXCL13, CXCL10, and ADIPOQ was statistically different between RA and OA synovial tissue. Differential expression of CXCL13, CXCL10, and ADIPOQ between RA and OA synovial tissue may provide new insights for understanding the RA development and difference between RA and OA. Key Points • Bioinformatics analysis was used to identify the differentially expressed genes in the synovial tissue between rheumatoid arthritis and osteoarthritis. • CXCL13, CXCL10, and ADIPOQ might provide new insight for understanding the differences between RA and OA.

Zhang R ，Zhou X ，Jin Y ，Chang C ，Wang R ，Liu J ，Fan J ，He D ... -  《-》

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

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

Zhang Q ，Sun C ，Liu X ，Zhu C ，Ma C ，Feng R ... -  《Journal of Orthopaedic Surgery and Research》