Dataset Information

Bioinformatics Analysis and Identification of Genes and Molecular Pathways Involved in Synovial Inflammation in Rheumatoid Arthritis.

ABSTRACT: BACKGROUND Rheumatoid arthritis (RA) has a high prevalence in the elderly population. The genes and pathways in the inflamed synovium in patients with RA are poorly understood. This study aimed to identify differentially expressed genes (DEGs) linked to the progression of synovial inflammation in RA using bioinformatics analysis. MATERIAL AND METHODS Gene expression profiles of datasets GSE55235 and GSE55457 were acquired from the Gene Expression Omnibus (GEO) database. DEGs were identified using Morpheus software, and co-expressed DEGs were identified with Venn diagrams. Protein-protein interaction (PPI) networks were assembled with Cytoscape software and separated into subnetworks using the Molecular Complex Detection (MCODE) algorithm. The functions of the top module were assessed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. RESULTS DEGs that were upregulated were significantly enhanced in protein binding, the cell cytosol, organization of the extracellular matrix (ECM), regulation of RNA transcription, and cell adhesion. DEGs that were downregulated were associated with control of the immune response, B-cell and T-cell receptor signaling pathway regulation. KEGG pathway analysis showed that upregulated DEGs enhanced pathways associated with the cell adherens junction, osteoclast differentiation, and hereditary cardiomyopathies. Downregulated DEGs were enriched in primary immunodeficiency, cell adhesion molecules (CAMs), cytokine-cytokine receptor interaction, and hematopoietic cell lineages. CONCLUSIONS The findings from this bioinformatics network analysis study identified molecular mechanisms and the key hub genes that may contribute to synovial inflammation in patients with RA.

SUBMITTER: Xiong Y

PROVIDER: S-EPMC6448456 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Bioinformatics Analysis and Identification of Genes and Molecular Pathways Involved in Synovial Inflammation in Rheumatoid Arthritis.

Xiong Yuan Y Mi Bo-Bin BB Liu Meng-Fei MF Xue Hang H Wu Qi-Peng QP Liu Guo-Hui GH

Medical science monitor : international medical journal of experimental and clinical research 20190327

BACKGROUND Rheumatoid arthritis (RA) has a high prevalence in the elderly population. The genes and pathways in the inflamed synovium in patients with RA are poorly understood. This study aimed to identify differentially expressed genes (DEGs) linked to the progression of synovial inflammation in RA using bioinformatics analysis. MATERIAL AND METHODS Gene expression profiles of datasets GSE55235 and GSE55457 were acquired from the Gene Expression Omnibus (GEO) database. DEGs were identified usin ...[more]

PMID: 30916045

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Project description:ObjectiveRheumatoid arthritis (RA) is one of the most prevalent inflammatory arthritis worldwide. However, the genes and pathways associated with macrophages from synovial fluids in RA patients still remain unclear. This study aims to screen and verify differentially expressed genes (DEGs) related to identifying candidate genes related to synovial macrophages in rheumatoid arthritis by bioinformatics analysis.MethodsWe searched the Gene Expression Omnibus (GEO) database, and GSE97779 and GSE10500 with synovial macrophages expression profiling from multiple RA microarray dataset were selected to conduct a systematic analysis. GSE97779 included nine macrophage samples from synovial fluids of RA patients and five macrophage samples from primary human blood of HC. GSE10500 included five macrophage samples from synovial fluids of RA patients and three macrophage samples from primary human blood of HC. Functional annotation of DEGs was performed, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein-protein interaction (PPI) network of DEGs was established using the STRING database. CytoHubba was used to identify hub genes. MCODE was used to determine gene clusters in the interactive network.ResultsThere were 2638 DEGs (1425 upregulated genes and 1213 downregulated ones) and 889 DEGs (438 upregulated genes and 451 downregulated ones) selected from GSE97779 and GSE10500, respectively. Venn diagrams showed that 173 genes were upregulated and 106 downregulated in both two datasets. The top 10 hub genes, including FN1, VEGFA, HGF, SERPINA1, MMP9, PPBP, CD44, FPR2, IGF1, and ITGAM, were identified using the PPI network.ConclusionThis study provides new insights for the potential biomarkers and the relevant molecular mechanisms in RA patients. Our findings suggest that the 10 candidate genes might be used in diagnosis, prognosis, and therapy of RA in the future. However, further studies are required to confirm the expression of these genes in synovial macrophages in RA and control specimen.

Project description:ObjectiveRheumatoid arthritis (RA) is a nonspecific, chronic, systemic autoimmune disease characterized by symmetric polyarticular synovitis. Bioinformatics analysis of potential biomarkers, mRNA-miRNA-lncRNA axes, and signaling pathways in the pathogenesis of RA provides potential targets and theoretical basis for further research on RA.MethodsThe GSE1919 and GSE77298 datasets were downloaded from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo). Perl was used to perform data merging, and R was used to perform batch correction. The "limma" package of R was used to screen differentially expressed genes, and the "clusterProfiler" package was used to perform enrichment analysis of the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Search Tool for the Retrieval of Interacting Genes/Proteins was used to construct the protein-protein interaction network, Cytoscape was used for module analysis, and R was used to screen for hub genes. GraphPad Prism was used to plot the receiver operating characteristic curve of the hub genes. Gene set enrichment analysis and competitive endogenous RNA network analysis were performed on hub genes with the greatest diagnostic values. The hub gene with the greatest diagnostic value was verified using immunohistochemical staining.ResultsWe obtained nine hub genes (ITGB2, VAMP8, HLA-A, PTAFR, SYK, FCER1G, HLA-DPB1, LCP2, and ACTR2) and four mRNA-miRNA-lncRNA axes (ITGB2-hsa-miR-486-3p-SNHG3, ITGB2-hsa-miR-338-5p-XIST, ITGB2-hsa-miR-5581-3p-XIST, and ITGB2-hsa-miR-1226-5p-XIST) related to the pathogenesis of RA. The nine hub genes were highly expressed, and ITGB2 had the highest diagnostic value for RA. We also identified signaling pathways related to the pathogenesis of RA: Fc epsilon Rl and chemokine signaling pathways. The immunohistochemical results showed that ITGB2 expression was significantly upregulated in RA.ConclusionThe hub genes, mRNA-miRNA-lncRNA axes, and signaling pathways related to RA pathogenesis identified in this study provide a new research direction for the mechanism, diagnosis, and treatment of RA.

Project description: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.

Dataset Information

Bioinformatics Analysis and Identification of Genes and Molecular Pathways Involved in Synovial Inflammation in Rheumatoid Arthritis.

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Bioinformatics Analysis and Identification of Genes and Molecular Pathways Involved in Synovial Inflammation in Rheumatoid Arthritis.

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