ArticleHeliyon2024
Identification of diagnostic biomarkers of rheumatoid arthritis based on machine learning-assisted comprehensive bioinformatics and its correlation with immune cells.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Discovering potential key biomarkers and molecular mechanisms in Chronic Obstructive Pulmonary Disease and rheumatoid arthritis using integrated bioinformatics and machine learning approaches.Journal, genetic engineering & biotechnology · 2026Article
- Computational approaches to multimodal data integration in rheumatoid arthritis: from data landscape to clinical translation.Briefings in bioinformatics · 2026Review
- Exploratory Machine Learning Prioritizes Shared Blood Transcriptional Candidate Genes and Immune Correlates Across Antiphospholipid Syndrome and Systemic Sclerosis.BioMed research international · 2026Article
- Targeting eRNA-Producing Super-Enhancers Regulates TNFα Expression and Mitigates Chronic Inflammation in Mice and Patient-Derived Immune Cells.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Identification of Proteomic Biomarkers and Therapeutic Targets for Vitiligo Using a Two-Sample Proteome-Wide Mendelian Randomization Approach.Journal of cosmetic dermatology · 2025Article
- Article
- Therapeutic gene targets and epigenetic modifications in rheumatoid arthritis: Insights from MTX, JAK inhibitors, and LLDT-8.Biochemistry and biophysics reports · 2025Article
- Unveiling the crucial role of CD8Journal of translational autoimmunity · 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
- Systems Pharmacology-based Drug Discovery and Active Mechanism ofCurrent pharmaceutical design · 2025Article
- Exploring the relationship between per- and polyfluoroalkyl substances exposure and rheumatoid arthritis risk using interpretable machine learning.Frontiers in public health · 2025Article
- Cytoplasmic DNA and AIM2 inflammasome in RA: where they come from and where they go?Frontiers in immunology · 2024Review
- PANoptosis in autoimmune diseases interplay between apoptosis, necrosis, and pyroptosis.Frontiers in immunology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by inflammatory cell infiltration, which can lead to chronic disability, joint destruction and loss of function. At present, the pathogenesis of RA is still unclear. The purpose of this study is to explore the potential biomarkers and immune molecular mechanisms of rheumatoid arthritis through machine learning-assisted bioinformatics analysis, in order to provide reference for the early diagnosis and treatment of RA disease. Methods: RA gene chips were screened from the public gene GEO database, and batch correction of different groups of RA gene chips was performed using Strawberry Perl. DEGs were obtained using the limma package of R software, and functional enrichment analysis such as gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), disease ontology (DO), and gene set (GSEA) were performed. Three machine learning methods, least absolute shrinkage and selection operator regression (LASSO), support vector machine recursive feature elimination (SVM-RFE) and random forest tree (Random Forest), were used to identify potential biomarkers of RA. The validation group data set was used to verify and further confirm its expression and diagnostic value. In addition, CIBERSORT algorithm was used to evaluate the infiltration of immune cells in RA and control samples, and the correlation between confirmed RA diagnostic biomarkers and immune cells was analyzed. Results: Through feature screening, 79 key DEGs were obtained, mainly involving virus response, Parkinson's pathway, dermatitis and cell junction components. A total of 29 hub genes were screened by LASSO regression, 34 hub genes were screened by SVM-RFE, and 39 hub genes were screened by Random Forest. Combined with the three algorithms, a total of 12 hub genes were obtained. Through the expression and diagnostic value verification in the validation group data set, 7 genes that can be used as diagnostic biomarkers for RA were preliminarily confirmed. At the same time, the correlation analysis of immune cells found that γδT cells, CD4 Conclusions: The results of novel characteristic gene analysis of RA showed that
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.