ORIGINAL RESEARCH article
Front. Immunol.
Sec. Autoimmune and Autoinflammatory Disorders : Autoimmune Disorders
Unveiling Potential Diagnostic Biomarkers for Rheumatoid Arthritis Through Integrated Gene Expression Analysis
Provisionally accepted
- 1Nanchong Central Hospital Affiliated to North Sichuan Medical College, Nanchong, China
- 2North Sichuan Medical College, Nanchong, China
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Objective Rheumatoid arthritis (RA) is a chronic autoimmune disorder that significantly impacts quality of life. Despite extensive research, its pathogenesis remains unclear. This study aims to identify potential diagnostic biomarkers and therapeutic targets for RA. Methods This study integrated patient data from three Gene Expression Omnibus (GEO) databases to analyze gene expression in RA. Using Weighted Gene Correlation Network Analysis (WGCNA), we identified key genes, which were then compared with differentially expressed genes (DEGs) to uncover RA-related genes. Functional enrichment analysis provided insights into the biological roles of these genes. To refine our findings, we applied three algorithms—RandomForest, SVM-REF, LASSO, and Convolutional Neural Networks (CNN)—to pinpoint a subset of core genes. We evaluated their diagnostic potential through receiver operating characteristic (ROC) curves and selected the top five genes with the highest area under the curve (AUC) values for constructing a predictive nomogram model. An interaction analysis was performed to investigate the relationship between these genes and immune cell infiltration. Finally, the expression of these core genes was validated in the synovial tissues of RA patients. Drug-protein interaction relationships were predicted using the DSigDB database. Results Differential expression analysis identified 543 DEGs. We subsequently applied WGCNA to compare these DEGs with significant module genes, resulting in the identification of 273 key genes. Functional enrichment analysis indicated that these genes were primarily involved in inflammatory response pathways. Further analysis using four machine learning algorithms identified 11 core genes. Of these, the five genes with the highest AUC values were selected to construct a robust nomogram model. Immune infiltration analysis revealed significant differences in
Keywords: Diagnostic biomarker, Immune infiltration, machine learning, Rheumatoid arthritis, WGCNA
Received: 11 Jun 2025; Accepted: 09 Jan 2026.
Copyright: © 2026 Zhiwei, fa, minggang, feng, wei and chenfei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: yang chenfei
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