Dataset Information

Functional annotation of rheumatoid arthritis and osteoarthritis associated genes by integrative genome-wide gene expression profiling analysis.

ABSTRACT:

Background

Rheumatoid arthritis (RA) and osteoarthritis (OA) are two major types of joint diseases that share multiple common symptoms. However, their pathological mechanism remains largely unknown. The aim of our study is to identify RA and OA related-genes and gain an insight into the underlying genetic basis of these diseases.

Methods

We collected 11 whole genome-wide expression profiling datasets from RA and OA cohorts and performed a meta-analysis to comprehensively investigate their expression signatures. This method can avoid some pitfalls of single dataset analyses.

Results and conclusion

We found that several biological pathways (i.e., the immunity, inflammation and apoptosis related pathways) are commonly involved in the development of both RA and OA. Whereas several other pathways (i.e., vasopressin-related pathway, regulation of autophagy, endocytosis, calcium transport and endoplasmic reticulum stress related pathways) present significant difference between RA and OA. This study provides novel insights into the molecular mechanisms underlying this disease, thereby aiding the diagnosis and treatment of the disease.

SUBMITTER: Li ZC

PROVIDER: S-EPMC3925090 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Functional annotation of rheumatoid arthritis and osteoarthritis associated genes by integrative genome-wide gene expression profiling analysis.

Li Zhan-Chun ZC Xiao Jie J Peng Jin-Liang JL Chen Jian-Wei JW Ma Tao T Cheng Guang-Qi GQ Dong Yu-Qi YQ Wang Wei-Li WL Liu Zu-De ZD

PloS one 20140214 2

<h4>Background</h4>Rheumatoid arthritis (RA) and osteoarthritis (OA) are two major types of joint diseases that share multiple common symptoms. However, their pathological mechanism remains largely unknown. The aim of our study is to identify RA and OA related-genes and gain an insight into the underlying genetic basis of these diseases.<h4>Methods</h4>We collected 11 whole genome-wide expression profiling datasets from RA and OA cohorts and performed a meta-analysis to comprehensively investiga ...[more]

PMID: 24551036

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Project description:The present study aimed to identify potential novel biomarkers in synovial tissue obtained from patients with Rheumatoid Arthritis (RA) and Osteoarthritis (OA) for differential diagnosis. The genome?wide expression profiling datasets of synovial tissues from RA and OA cohorts, including GSE55235, GSE55457 and GSE55584 datasets, were retrieved and used to identify differentially expressed genes (DEGs; P<0.05; false discovery rate <0.05 and Fold Change >2) between RA and OA using R software. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of DEGs were performed to determine molecular and biochemical pathways associated with the identified DEGs, and a protein?protein interaction (PPI) network of the DEGs was constructed using Cytoscape software. Significant modules in the PPI network and candidate driver genes were screened using the Molecular Complex Detection Algorithm. Potential biomarkers were evaluated by receiver operating characteristic and logistic regression analyses. Large numbers of DEGs were detected, including 273, 205 and 179 DEGs in the GSE55235, GSE55457 and GSE55584 datasets, respectively. Among them, 80 DEGs exhibited identical expression trends in all the three datasets, including 49 upregulated and 31 downregulated genes in patients with RA. DEGs in patients suffering from RA compared with patients suffering from OA were predominantly associated with the primary immunodeficiency pathway, including interleukin 7 receptor (IL7R) and signal transducer activator of transcription 1 (STAT1). The sensitivity of IL7R + STAT1 to differentiate RA from OA was 93.94% with a specificity of 80.77%. The results generated from analyses of the GSE36700 dataset were closely associated with results generated from analyses of GSE55235, GSE55457 and GSE55584 datasets, which further verified the reliability of the aforementioned results. The results of the present study suggested that increased expression of IL7R and STAT1 in synovial tissue as well as in the primary immunodeficiency may be associated with RA occurrence. These identified novel biomarkers may be used to predict disease occurrence and clinically differentiate RA from OA.

Project description:ObjectiveRheumatoid arthritis (RA) is a complex autoimmune disease. Using a gene-based association research strategy, the present study aims to detect unknown susceptibility to RA and to address the ethnic differences in genetic susceptibility to RA between European and Asian populations.MethodsGene-based association analyses were performed with KGG 2.5 by using publicly available large RA datasets (14,361 RA cases and 43,923 controls of European subjects, 4,873 RA cases and 17,642 controls of Asian Subjects). For the newly identified RA-associated genes, gene set enrichment analyses and protein-protein interactions analyses were carried out with DAVID and STRING version 10.0, respectively. Differential expression verification was conducted using 4 GEO datasets. The expression levels of three selected 'highly verified' genes were measured by ELISA among our in-house RA cases and controls.ResultsA total of 221 RA-associated genes were newly identified by gene-based association study, including 71'overlapped', 76 'European-specific' and 74 'Asian-specific' genes. Among them, 105 genes had significant differential expressions between RA patients and health controls at least in one dataset, especially for 20 genes including 11 'overlapped' (ABCF1, FLOT1, HLA-F, IER3, TUBB, ZKSCAN4, BTN3A3, HSP90AB1, CUTA, BRD2, HLA-DMA), 5 'European-specific' (PHTF1, RPS18, BAK1, TNFRSF14, SUOX) and 4 'Asian-specific' (RNASET2, HFE, BTN2A2, MAPK13) genes whose differential expressions were significant at least in three datasets. The protein expressions of two selected genes FLOT1 (P value = 1.70E-02) and HLA-DMA (P value = 4.70E-02) in plasma were significantly different in our in-house samples.ConclusionOur study identified 221 novel RA-associated genes and especially highlighted the importance of 20 candidate genes on RA. The results addressed ethnic genetic background differences for RA susceptibility between European and Asian populations and detected a long list of overlapped or ethnic specific RA genes. The study not only greatly increases our understanding of genetic susceptibility to RA, but also provides important insights into the ethno-genetic homogeneity and heterogeneity of RA in both ethnicities.

Dataset Information

Functional annotation of rheumatoid arthritis and osteoarthritis associated genes by integrative genome-wide gene expression profiling analysis.

Background

Methods

Results and conclusion

Publications

Functional annotation of rheumatoid arthritis and osteoarthritis associated genes by integrative genome-wide gene expression profiling analysis.

Similar Datasets

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