ArticleClinical rheumatology2022
Machine learning to identify immune-related biomarkers of rheumatoid arthritis based on WGCNA network.
Article in Clinical rheumatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
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- Identification of Ifitm1 as a Pivotal Gene in Mouse Spinal Cord Injury Using Comprehensive Machine Learning Algorithms.Mediators of inflammation · 2025Article
- Identification and Validation of Pivotal Genes in Osteoarthritis Combined with WGCNA Analysis.Journal of inflammation research · 2025Article
- Unraveling the mechanisms underlying diabetic cataracts: insights from Mendelian randomization analysis.Redox report : communications in free radical research · 2024Article
- From molecular subgroups to molecular targeted therapy in rheumatoid arthritis: A bioinformatics approach.Heliyon · 2024Article
- Identification of steroid-induced osteonecrosis of the femoral head biomarkers based on immunization and animal experiments.BMC musculoskeletal disorders · 2024Article
- Prediction of immune molecules activity during burn wound healing among elderly patients: in-silico analyses: experimental research.Annals of medicine and surgery (2012) · 2024Article
- Unveiling the link between lactate metabolism and rheumatoid arthritis through integration of bioinformatics and machine learning.Scientific reports · 2024Article
- Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets.Lupus science & medicine · 2024Article
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- Identifying Hub Genes for Glaucoma based on Bulk RNA Sequencing Data and Multi-machine Learning Models.Current medicinal chemistry · 2024Article
- Identification of important modules and biomarkers in tuberculosis based on WGCNA.Frontiers in microbiology · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study was designed to identify the potential diagnostic biomarkers of rheumatoid arthritis (RA) and to explore the potential pathological relevance of immune cell infiltration in this disease.
methodsThree previously published datasets containing gene expression data from 35 RA patients and 29 controls (GSE55235, GSE55457, and GSE12021) were downloaded from the GEO database, after which a weighted correlation network analysis (WGCNA) approach was utilized to clarify differentially abundant genes. Candidate biomarkers of RA were then identified via the use of a LASSO regression model and support vector machine recursive feature elimination (SVM-RFE) analyses. Data were validated based upon the area under the receiver operating characteristic curve (AUC) values, with hub genes being identified as those with an AUC > 85% and a P value < 0.05. Lastly, the CIBERSORT algorithm was used to assess immune cell infiltration of RA tissues, and correlations between immune cell infiltration and disease-related diagnostic biomarkers were assessed.
resultsThe green-yellow module containing 87 genes was found to be highly correlated with RA positivity. FADD, CXCL2, and CXCL8 were identified as potential RA diagnostic biomarkers (AUC > 0.85), and these results were validated using the GSE77298 dataset. Immune cell infiltration analyses revealed the expression of hub genes to be correlated with mast cells, monocytes, activated NK cells, CD8 T cells, resting dendritic cells, and plasma cells.
conclusionThese data indicate that FADD, CXCL2, and CXCL8 are valuable diagnostic biomarkers of RA, offering new insight that can guide future studies of RA incidence and progression.
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