Dataset Information

Screening of gene signatures for rheumatoid arthritis and osteoarthritis based on bioinformatics analysis.

ABSTRACT: The current study aimed to identify gene signatures during rheumatoid arthritis (RA) and osteoarthritis (OA), and used these to elucidate the underlying modular mechanisms. Using the Gene Expression Omnibus database, the present study obtained the GSE7669 mRNA expression microarray data from RA and OA synovial fibroblasts (n=6 each). The differentially expressed genes (DEGs) in RA synovial samples compared with OA samples were identified using the Linear Models for Microarray Analysis package. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using the Database for Annotation Visualization and Integrated Discovery. A protein?protein interaction network was constructed and the modules were further analyzed using the Molecular Complex Detection plugin of Cytoscape. A total of 181 DEGs were identified by comparing RA and OA synovial samples (96 up? and 85 downregulated genes). The significant DEGs in module 1, including collagen, type I, ? 1 (COL1A1), COL3A1, COL4A1 and COL11A1, were predominantly enriched in the extracellular matrix (ECM)?receptor interaction and focal adhesion pathways. Additionally, significant DEGs in module 2, including radical S?adenosyl methionine domain containing 2 (RSAD2), 2'?5'?oligoadenylate synthetase 2 (OAS2), myxovirus (influenza virus) resistance 1 (MX1) and ISG15 ubiquitin?like modifier (ISG15), were predominantly associated with immune function pathways. In conclusion, the present study indicated that RSAD2, OAS2, MX1 and ISG15 may be notable gene signatures in RA development via regulation of the immune response. COL3A1, COL4A1, COL1A1 and COL11A1 may be important gene signatures in OA development via involvement in the pathways of ECM-receptor interactions and focal adhesions.

SUBMITTER: He P

PROVIDER: S-EPMC4940106 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Screening of gene signatures for rheumatoid arthritis and osteoarthritis based on bioinformatics analysis.

He Peiheng P Zhang Ziji Z Liao Weiming W Xu Dongliang D Fu Ming M Kang Yan Y

Molecular medicine reports 20160623 2

The current study aimed to identify gene signatures during rheumatoid arthritis (RA) and osteoarthritis (OA), and used these to elucidate the underlying modular mechanisms. Using the Gene Expression Omnibus database, the present study obtained the GSE7669 mRNA expression microarray data from RA and OA synovial fibroblasts (n=6 each). The differentially expressed genes (DEGs) in RA synovial samples compared with OA samples were identified using the Linear Models for Microarray Analysis package. T ...[more]

PMID: 27356888

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Project description:Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10. Synovial tisssue from healthy joints, OA joints, and RA joints.

Project description:BackgroundOsteoarthritis (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.ObjectiveThis 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.MethodsGene 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.ResultsWe 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.ConclusionsWe 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.

Dataset Information

Screening of gene signatures for rheumatoid arthritis and osteoarthritis based on bioinformatics analysis.

Publications

Screening of gene signatures for rheumatoid arthritis and osteoarthritis based on bioinformatics analysis.

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