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Identification of BMX as a Potential Biomarker for Rheumatoid Arthritis Based on WGCNA, Machine Learning, and
Xinmin Huang1, Xu Cai1, Zhenbo Yan1
1Rheumatology, Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, China.
Immunological Investigations
|May 28, 2026
Summary
Biomarker BMX is upregulated in rheumatoid arthritis (RA) patients, correlating with disease activity. This suggests BMX as a potential diagnostic marker for RA, with possible links to STAT3 in disease development.
Area of Science:
- Biomarker discovery
- Genetics and genomics
- Immunology
Background:
- Rheumatoid arthritis (RA) is a chronic autoimmune disease requiring reliable biomarkers for diagnosis and management.
- Current diagnostic methods may lack specificity or sensitivity, necessitating the identification of novel biomarkers.
- Understanding the molecular underpinnings of RA is crucial for developing targeted therapies.
Purpose of the Study:
- To identify potential biomarkers for rheumatoid arthritis (RA) using Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning algorithms.
- To investigate the correlation of identified biomarkers with disease activity and STAT3 signaling.
- To validate the diagnostic potential of candidate biomarkers in RA patients.
Main Methods:
- Utilized Gene Expression Omnibus (GEO) datasets for comprehensive analysis.
- Applied WGCNA to identify gene modules associated with RA.
- Employed machine learning techniques (LASSO, SVM, Boruta) to screen for core genes.
- Validated gene expression using Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR).
- Assessed correlations with clinical parameters (DAS28, CCP, RF, ESR) and diagnostic accuracy using Receiver Operating Characteristic (ROC) curves.
Main Results:
- WGCNA identified 11 gene modules, with the 'black' module showing the strongest correlation with RA.
- Machine learning methods identified BMX as a common core gene.
- BMX expression was significantly upregulated in RA patients.
- BMX expression positively correlated with RA disease activity markers (DAS28, RF) and STAT3.
- ROC analysis indicated a diagnostic accuracy (AUC) of 0.789 for BMX.
Conclusions:
- BMX is upregulated in RA patients and correlates with disease activity, positioning it as a potential diagnostic biomarker.
- The observed correlation between BMX and STAT3 suggests a potential role in RA pathogenesis, warranting further investigation.
- BMX represents a promising target for future research into RA diagnosis and therapeutic strategies.
