ArticleFrontiers in immunology2024
Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Meta-analysis and systematic review: the association between rheumatoid arthritis and the risk of thyroid cancer.Frontiers in immunology · 2026Pooled it
- Artificial intelligence in rheumatoid arthritis: current applications and future perspectives.Frontiers in medicine · 2026Pooled it
- Identification of Diagnostic Biomarkers Among Metabolism-related Genes in Rheumatoid Arthritis: Insights into Immune Landscape and Molecular Subtype Characterization.Applied biochemistry and biotechnology · 2026Article
- Identification of Cellular Senescence-Related Hub Genes in Rheumatoid Arthritis from Bioinformatics Analysis Through Machine Learning up to Verifications in Mouse Macrophages and Tests in Patients.International journal of molecular sciences · 2026Article
- Identification and Experimental Validation of Key Biomarkers for Rheumatoid Arthritis Based on Bioinformatics Analysis and Machine Learning.Journal of inflammation research · 2026Article
- RNA sequencing-derived gene co-expression and drug-gene interaction analysis reveal STAT1 as a potential therapeutic target in thrombotic antiphospholipid syndrome.Frontiers in immunology · 2026Article
- Identification of diagnostic genes in rheumatoid arthritis using integrated bioinformatics, machine learning, and experimental validation.Frontiers in medicine · 2026Article
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- Emerging Artificial Intelligence Innovations in Rheumatoid Arthritis and Challenges to Clinical Adoption.Current rheumatology reports · 2025Review
- Therapeutic gene targets and epigenetic modifications in rheumatoid arthritis: Insights from MTX, JAK inhibitors, and LLDT-8.Biochemistry and biophysics reports · 2025Article
- Identification biomarkers and therapeutic targets of disulfidptosis-related in rheumatoid arthritis via bioinformatics, molecular dynamics simulation, and experimental validation.Scientific reports · 2025Article
- Machine learning and gene network integration reveal prognostic subnetworks and biomarkers in pancreatic cancer.Computational and structural biotechnology journal · 2025Article
- Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.Frontiers in immunology · 2025Article
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Authors and funding
5 authors.
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Abstract
Background: Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration. Methods: We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO) were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis. Results: Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 ( Conclusion:
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