ArticleFrontiers in immunology2022
Diagnostic gene signatures and aberrant pathway activation based on m6A methylation regulators in rheumatoid arthritis.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 38 citations in OpenAlex.
- Regulatory role and subtype analysis of m6A modifications in dermatomyositis.Global medical genetics · 2026Article
- Dysregulation of YTHDF2 promotes the over expression of interferon beta in anti-MDA5 antibody-positive dermatomyositis.Arthritis research & therapy · 2026Article
- Deep learning-assisted discovery of a potent and cell-active inhibitor of RNA NNature communications · 2026Article
- Epigenetic modifier m⁶A methylation: insights into the pathogenesis and therapeutic potential of autoimmune diseases.Journal of translational medicine · 2025Review
- A New N6-Methyladenosine Inhibitor, Celastrol, Alleviates Rheumatoid Arthritis via Targeting IGF2BP3.MedComm · 2025Article
- Crosstalk between N6-methyladenosine modification and ncRNAs in rheumatic diseases: therapeutic and diagnostic implications.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2025Review
- IGF2BP3-mediated mBone research · 2025Article
- Citrullinated IGF2BP1 promotes rheumatoid synovial aggression via increasing the mRNA stability of SEMA3D.Communications biology · 2025Article
- New Targets for Immune Inflammatory Response in Rheumatoid Arthritis: Focus on the Potential Significance of N6-Methyladenosine, Ferroptosis and Cuproptosis.Journal of inflammation research · 2025Review
- Evaluation of a PSA and transrectal prostate ultrasound video-based machine learning model as a tool for prostate cancer diagnosis.Frontiers in oncology · 2025Article
- Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- The role of m6A methylation genes in predicting poor prognosis in sepsis: identifying key biomarkers and therapeutic targets.European journal of medical research · 2024Article
- Exosomal Drug Delivery Systems: A Novel Therapy Targeting PD-1 in Septic-ALI.Stem cell reviews and reports · 2024Article
- N6‑methyladenosine methyltransferase METTL14 is associated with macrophage polarization in rheumatoid arthritis.Experimental and therapeutic medicine · 2024Article
- Review
- Methylation of T and B Lymphocytes in Autoimmune Rheumatic Diseases.Clinical reviews in allergy & immunology · 2024Review
- Recent advances of m6A methylation in skeletal system disease.Journal of translational medicine · 2024Review
- IGF2BP3 regulates the expression of RRM2 and promotes the progression of rheumatoid arthritis via RRM2/Akt/MMP-9 pathway.PloS one · 2024Article
- Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management.Frontiers in immunology · 2024Review
- Incidence and risk factors of prolonged recovery during procedural sedation in pediatrics.Frontiers in medicine · 2024Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Rheumatoid arthritis (RA) is a chronic autoimmune disease (AD) characterized by persistent synovial inflammation, bone erosion and progressive joint destruction. This research aimed to elucidate the potential roles and molecular mechanisms of N6-methyladenosine (m6A) methylation regulators in RA. Methods: An array of tissues from 233 RA and 126 control samples was profiled and integrated for mRNA expression analysis. Following quality control and normalization, the cohort was split into training and validation sets. Five distinct machine learning feature selection methods were applied to the training set and validated in validation sets. Results: Among the six models, the LASSO_λ-1se model not only performed better in the validation sets but also exhibited more stringent performance. Two m6A methylation regulators were identified as significant biomarkers by consensus feature selection from all four methods. IGF2BP3 and YTHDC2, which are differentially expressed in patients with RA and controls, were used to predict RA diagnosis with high accuracy. In addition, IGF2BP3 showed higher importance, which can regulate the G2/M transition to promote RA-FLS proliferation and affect M1 macrophage polarization. Conclusion: This consensus of multiple machine learning approaches identified two m6A methylation regulators that could distinguish patients with RA from controls. These m6A methylation regulators and their target genes may provide insight into RA pathogenesis and reveal novel disease regulators and putative drug targets.
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