ArticleFrontiers in medicine2022
RNA-seq and Network Analysis Reveal Unique Chemokine Activity Signatures in the Synovial Tissue of Patients With Rheumatoid Arthritis.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Single cell immunoprofile of synovial fluid in rheumatoid arthritis with TNF/JAK inhibitor treatment.Nature communications · 2025Article
- Transcriptomic insights into the mechanism of action of telomere-related biomarkers in rheumatoid arthritis.Frontiers in immunology · 2025Article
- Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis.Frontiers in immunology · 2024Article
- Role of the granzyme family in rheumatoid arthritis: Current Insights and future perspectives.Frontiers in immunology · 2023Review
- Identification of immunological characterization and Anoikis-related molecular clusters in rheumatoid arthritis.Frontiers in molecular biosciences · 2023Article
- Circulating methylation level ofFrontiers in immunology · 2022Article
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study aimed to provide a comprehensive understanding of the genome-wide expression patterns in the synovial tissue samples of patients with rheumatoid arthritis (RA) to investigate the potential mechanisms regulating RA occurrence and development. Methods: Transcription profiles of the synovial tissue samples from nine patients with RA and 15 patients with osteoarthritis (OA) (control) from the East Asian population were generated using RNA sequencing (RNA-seq). Gene set enrichment analysis (GSEA) was used to analyze all the detected genes and the differentially expressed genes (DEGs) were identified using DESeq. To further analyze the DEGs, the Gene Ontology (GO) functional enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. The protein-protein interaction (PPI) network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and the hub genes were identified by topology clustering with the Molecular Complex Detection (MCODE)-Cytoscape. The most important hub genes were validated using quantitative real-time PCR (qRT-PCR). Results: Of the 17,736 genes detected, 851 genes were identified as the DEGs (474 upregulated and 377 downregulated genes) using the false discovery rate (FDR) approach. GSEA revealed that the significantly enriched gene sets that positively correlated with RA were CD40 signaling overactivation, Th1 cytotoxic module, overactivation of the immune response, adaptive immune response, effective vs. memory CD8+ T cells (upregulated), and naïve vs. effective CD8+ T cells (downregulated). Biological process enrichment analysis showed that the DEGs were significantly enriched for signal transduction ( Conclusion: This study identified and validated the DEGs in the synovial tissue samples of patients with RA, which highlighted the activity of a subset of chemokine genes, thereby providing novel insights into the molecular mechanisms of RA pathogenesis and identifying potential diagnostic and therapeutic targets for RA.
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