ArticleArthritis & rheumatology (Hoboken, N.J.)2018
Identification of Three Rheumatoid Arthritis Disease Subtypes by Machine Learning Integration of Synovial Histologic Features and RNA Sequencing Data.
Article in Arthritis & rheumatology (Hoboken, N.J.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 127 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
127 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in rheumatoid arthritis: current applications and future perspectives.Frontiers in medicine · 2026Pooled it
- The preferred technique for knee synovium biopsy and synovial fluid arthrocentesis.Rheumatology international · 2023Pooled it
- Pooled it
- Trial
- Exploring inflammatory signatures in arthritic joint biopsies with Spatial Transcriptomics.Scientific reports · 2019Trial
- Article
- When inflammation is not the driver: Pauci-immune synovitis and the stromal basis of refractory arthritis.EULAR rheumatology open · 2026Review
- Use of Artificial Intelligence in Rheumatoid Arthritis: Advancements and Novel Perspectives.Journal of clinical medicine · 2026Review
- Targeting fibroblasts in immune mediated inflammatory diseases: a cellular basis for cure?Rheumatology (Oxford, England) · 2026Review
- Artificial Intelligence in Rheumatology: A Comprehensive Bibliometric Analysis and Current Scientific Mapping Research.Mediterranean journal of rheumatology · 2026Review
- P2X7R of synovial fibroblasts is a potential therapeutic target associated with refractory rheumatoid arthritis.Science advances · 2026Article
- Bridging the gap: combining treat-to-target and difficult-to-treat strategies in the management of rheumatoid arthritis.Nature reviews. Rheumatology · 2026Review
- Pathogenic Drivers of Difficult-to-Treat Rheumatoid Arthritis: Synovium and Beyond.International journal of molecular sciences · 2026Review
- Integrative multi-omics analysis reveals CXCL10-driven inflammation and TREM2 + macrophage-plasma cell survival niche as hallmarks of late-stage rheumatoid arthritis.Arthritis research & therapy · 2026Article
- Human Knee as an Organ: Joint Tissue Collection, Processing, and Scoring for Multimodal Analyses.Laboratory investigation; a journal of technical methods and pathology · 2026Review
- The value of contrast-enhanced ultrasound in synovial pathotypes of rheumatoid arthritis: an exploratory study.European radiology · 2026Article
- Biomarkers in Rheumatoid Arthritis: From Traditional Serology to Precision Medicine Integration.Diagnostics (Basel, Switzerland) · 2026Review
- Transcriptomics and AI-driven approaches to the diagnosis and treatment of rheumatoid arthritis.Frontiers in immunology · 2026Review
- From Systemic Autoimmunity to the Local Immune Niche of the Synovium: New Insights into the Development of Rheumatoid Arthritis.Journal of inflammation research · 2026Review
- Rheumatoid arthritis synovial fibroblasts modulate T cell activation.JCI insight · 2025Article
67 more citing papers are in PubMed but not listed here.
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Authors and funding
23 authors.
Funding
Abstract
objectiveIn this study, we sought to refine histologic scoring of rheumatoid arthritis (RA) synovial tissue by training with gene expression data and machine learning.
methodsTwenty histologic features were assessed in 129 synovial tissue samples (n = 123 RA patients and n = 6 osteoarthritis [OA] patients). Consensus clustering was performed on gene expression data from a subset of 45 synovial samples. Support vector machine learning was used to predict gene expression subtypes, using histologic data as the input. Corresponding clinical data were compared across subtypes.
resultsConsensus clustering of gene expression data revealed 3 distinct synovial subtypes, including a high inflammatory subtype characterized by extensive infiltration of leukocytes, a low inflammatory subtype characterized by enrichment in pathways including transforming growth factor β, glycoproteins, and neuronal genes, and a mixed subtype. Machine learning applied to histologic features, with gene expression subtypes serving as labels, generated an algorithm for the scoring of histologic features. Patients with the high inflammatory synovial subtype exhibited higher levels of markers of systemic inflammation and autoantibodies. C-reactive protein (CRP) levels were significantly correlated with the severity of pain in the high inflammatory subgroup but not in the others.
conclusionGene expression analysis of RA and OA synovial tissue revealed 3 distinct synovial subtypes. These labels were used to generate a histologic scoring algorithm in which the histologic scores were found to be associated with parameters of systemic inflammation, including the erythrocyte sedimentation rate, CRP level, and autoantibody levels. Comparison of gene expression patterns to clinical features revealed a potentially clinically important distinction: mechanisms of pain may differ in patients with different synovial subtypes.
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