ArticleFrontiers in immunology2021
Identifying Immune Cell Infiltration and Effective Diagnostic Biomarkers in Rheumatoid Arthritis by Bioinformatics Analysis.
Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 3 of them syntheses that pooled it.
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89 citing papers in PubMed, 3 syntheses or guidelines pooled it, 117 citations in OpenAlex.
- Meta-analysis and systematic review: the association between rheumatoid arthritis and the risk of thyroid cancer.Frontiers in immunology · 2026Pooled it
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- Investigating the Mechanism of Edible Medicinal Plants Against Squamous Cell Carcinomas Based on Network Pharmacology, Bioinformatics, and Molecular Dynamics Simulation.International journal of molecular sciences · 2026Article
- Neutrophil percentage-to-albumin ratio is associated with low muscle mass risk in rheumatoid arthritis: evidence from a hospital-based cohort and NHANES validation.Frontiers in immunology · 2026Article
- Network pharmacology-based investigation of potential targets of mulberry twig acting on polycystic ovary syndrome.Medicine · 2025Article
- Potential mechanisms of acupuncture treatment for rheumatoid arthritis: a study based on network topology and machine learning.Chinese medicine · 2025Article
- Differential gene expression and immune profiling in Parkinson's disease: unveiling potential candidate biomarkers.BMC neurology · 2025Article
- Identification and validation of biomarkers associated with glycolysis in polycystic ovarian syndrome.Scientific reports · 2025Article
- Longitudinal Evaluation of Immunological Biomarkers in Previously Untreated/Minimally Treated Patients With Severe and Moderately Severe Haemophilia A During Exposure to Factor VIII: Results From the HEMFIL Study.Haemophilia : the official journal of the World Federation of Hemophilia · 2025Article
- Cohort-based nomogram for forensic prediction of SCD: a single-center pilot study.Forensic science, medicine, and pathology · 2025Article
- T-Follicular Helper Cells and Their Role in Autoimmune Diseases.Life (Basel, Switzerland) · 2025Review
- Machine learning analysis of FOSL2 and RHoBTB1 as central immunological regulators in knee osteoarthritis synovium.The Journal of international medical research · 2025Article
- Identification biomarkers and therapeutic targets of disulfidptosis-related in rheumatoid arthritis via bioinformatics, molecular dynamics simulation, and experimental validation.Scientific reports · 2025Article
- Identification of ferroptosis-related key genes in tuberculosis by bioinformatics methods.AMB Express · 2025Article
- Advanced Machine Learning for Comparative Synovial Fluid Analysis in Osteoarthritis and Rheumatoid Arthritis.Metabolites · 2025Article
- Effectively alleviate rheumatoid arthritis via maintaining redox balance, inducing macrophage repolarization and restoring homeostasis of fibroblast-like synoviocytes by metformin-derived carbon dots.Journal of nanobiotechnology · 2025Article
- Individualized diagnosis of rheumatoid arthritis: A rank-based qualitative T cell-related signature.PloS one · 2025Article
29 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
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Abstract
Background: Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by inflammatory cell infiltration, leading to persistent synovitis and joint destruction. The pathogenesis of RA remains unclear. This study aims to explore the immune molecular mechanism of RA through bioinformatics analysis. Methods: Five microarray datasets and a high throughput sequencing dataset were downloaded. CIBERSORT algorithm was performed to evaluate immune cell infiltration in synovial tissues between RA and healthy control (HC). Wilcoxon test and Least Absolute Shrinkage and Selection Operator (LASSO) regression were conducted to identify the significantly different infiltrates of immune cells. Differentially expressed genes (DEGs) were screened by "Batch correction" and "RobustRankAggreg" methods. Functional correlation of DEGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Candidate biomarkers were identified by cytoHubba of Cytoscape, and their diagnostic effectiveness was predicted by Receiver Operator Characteristic Curve (ROC) analysis. The association of the identified biomarkers with infiltrating immune cells was explored using Spearman's rank correlation analysis in R software. Results: Ten significantly different types of immune cells between RA and HC were identified. A total of 202 DEGs were obtained by intersection of DEGs screened by two methods. The function of DEGs were significantly associated with immune cells. Five hub genes (CXCR4, CCL5, CD8A, CD247, and GZMA) were screened by R package "UpSet". CCL5+CXCR4 and GZMA+CD8A were verified to have the capability to diagnose RA and early RA with the most excellent specificity and sensitivity, respectively. The correlation between immune cells and biomarkers showed that CCL5 was positively correlated with M1 macrophages, CXCR4 was positively correlated with memory activated CD4+ T cells and follicular helper T (Tfh) cells, and GZMA was positively correlated with Tfh cells. Conclusions: CCL5, CXCR4, GZMA, and CD8A can be used as diagnostic biomarker for RA. GZMA-Tfh cells, CCL5-M1 macrophages, and CXCR4- memory activated CD4+ T cells/Tfh cells may participate in the occurrence and development of RA, especially GZMA-Tfh cells for the early pathogenesis of RA.
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