Dataset Information

Identification of biomarkers associated with synovitis in rheumatoid arthritis by bioinformatics analyses.

ABSTRACT: OBJECTIVES:Rheumatoid arthritis (RA) is the most common inflammatory arthritis in the world, but its underlying mechanism is still unclear. The present study aims to screen and verify the potential biomarkers of RA. METHODS:We searched the Gene Expression Omnibus (GEO) database for synovial expression profiling from different RA microarray studies to perform a systematic analysis. Functional annotation of differentially expressed genes (DEGs) was conducted, including GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The protein-protein interaction (PPI) networks of the DEGs were constructed based on data from the STRING database. The expression levels of the hub genes in normal membranes and RA synovium were detected by quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot system. RESULTS:A total of 444 differential expression genes were identified, including 172 up-regulated and 272 down-regulated genes in RA synovium compared with normal controls. The top ten hub genes; protein tyrosine phosphatase receptor type C (PTPRC), LCK proto-oncogene (LCK), cell division cycle 20 (CDC20), Jun proto-oncogene (JUN), cyclin-dependent kinase 1 (CDK1), kinesin family member 11 (KIF11), epidermal growth factor receptor (epidermal growth factor receptor (EGFR), vascular endothelial growth factor A (VEGFA), mitotic arrest deficient 2 like 1 (MAD2L1), and signal transducer and activator of transcription 1 (STAT1) were identified from the PPI network, and the expression level of VEGFA and EGFR was significantly increased in RA membranes (P<0.05). CONCLUSION:Our results indicate that the hub genes VEGFA and EGFR may have essential effects during the development of RA and can be used as potential biomarkers of RA.

SUBMITTER: Li Z

PROVIDER: S-EPMC7502692 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of biomarkers associated with synovitis in rheumatoid arthritis by bioinformatics analyses.

Li Zhaoyan Z Xu Meng M Li Ronghang R Zhu Zhengqing Z Liu Yuzhe Y Du Zhenwu Z Zhang Guizhen G Song Yang Y

Bioscience reports 20200901 9

<h4>Objectives</h4>Rheumatoid arthritis (RA) is the most common inflammatory arthritis in the world, but its underlying mechanism is still unclear. The present study aims to screen and verify the potential biomarkers of RA.<h4>Methods</h4>We searched the Gene Expression Omnibus (GEO) database for synovial expression profiling from different RA microarray studies to perform a systematic analysis. Functional annotation of differentially expressed genes (DEGs) was conducted, including GO enrichment ...[more]

PMID: 32840301

Similar Datasets

Project description:BackgroundRheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by inflammation and gradual joint degeneration, resulting in function disability. Recently, ferroptosis, a novel form of regulated cell death that involves iron-dependent lipid peroxidation, has been implicated in the pathogenesis of RA. However, the underlying molecular mechanisms and key genes involved in ferroptosis in RA remain largely unknown.MethodsThe GSE134420 and GSE77298 datasets were downloaded and DEGs were identified using R software. The DEGs were then mapped to the dataset of 619 ferroptosis-related genes obtained from the GeneCards database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to investigate the possible biological functions. Protein-protein interaction (PPI) networks were constructed to identify the hub genes. The relationship between hub genes and immune infiltration was estimated using the CIBERSORT algorithms. Gene Set Enrichment Analysis (GSEA) was used to explore the underlying signaling pathways of hub genes. Genome-wide association studies (GWAS) analysis was performed to confirm the pathogenic regions of the hub genes. RcisTarget and Gene-motif ranking databases were used to identify transcription factors (TFs) associated with the hub genes. The miRcode databases were utilized to construct the microRNA (miRNA)-messenger RNA (mRNA) network. Single-cell analysis was utilized to cluster cells and display the expression of hub genes in cell clusters. Finally, the expression and potential mechanism of hub genes were investigated in human and experimental samples.ResultsThree hub genes PTGS2, ENO1, and GRN highly associated with ferroptosis were identified. Four pathogenic genes HLA-B, MIF, PSTPIP, TLR1 were identified that were significantly and positively correlated with the expression levels of hub genes. The results of the GSEA showed that the hub genes were significantly enriched in pathways related to immunity, lysosome, phagocytosis and infection. ENO1 and PTGS2 were enriched in the TF-binding motif of cisbp_M5493. The hub genes were validated in experimental and patient samples and highly level of ENO1 expression was found to inhibit ACO1, which reduces ferroptosis in proliferating fibroblast-like synoviocytes (FLS).ConclusionPTGS2, ENO1 and GRN were identified and validated as potential ferroptosis-related biomarkers. Our work first revealed that ENO1 is highly expressed in RA synovium and that ferroptosis may be regulated by the ENO1-ACO1 axis, advancing the understanding of the underlying ferroptosis-related mechanisms of synovial proliferation and providing potential diagnostic and therapeutic targets for RA.

Dataset Information

Identification of biomarkers associated with synovitis in rheumatoid arthritis by bioinformatics analyses.

Publications

Identification of biomarkers associated with synovitis in rheumatoid arthritis by bioinformatics analyses.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets