Identifying Diagnostic Biomarkers for Electroacupuncture Treatment of Rheumatoid Arthritis Using Bioinformatic...

Area of Science:

• Biomedical research
• Molecular biology
• Bioinformatics

Background:

• Rheumatoid arthritis (RA) is a chronic inflammatory disease with complex molecular mechanisms.
• Electroacupuncture (EA) shows therapeutic potential for RA, but its underlying molecular pathways are not fully understood.

Purpose of the Study:

• To identify diagnostic biomarkers for RA using bioinformatics and machine learning.
• To elucidate the molecular targets of EA in RA treatment.

Main Methods:

• Utilized Gene Expression Omnibus (GEO) datasets for RA patients and EA-treated RA patients.
• Applied LASSO, Random Forest, and SVM-REF machine learning algorithms to identify biomarkers.
• Validated biomarker expression and EA's impact using a Complete Freund's Adjuvant (CFA)-induced rat RA model and quantitative real-time PCR.

Main Results:

• Identified 26 differentially expressed genes post-EA treatment.
• Convergent identification of ARHGAP17 and VEGFB as robust diagnostic biomarkers for RA (AUC > 0.75) across multiple cohorts.
• EA treatment improved pain response and upregulated ARHGAP17 and VEGFB expression in CFA-induced RA rats.

Conclusions:

• Successfully identified ARHGAP17 and VEGFB as potential diagnostic biomarkers for RA.
• Demonstrated EA's favorable regulatory effect on these biomarkers in an animal model.
• These findings offer novel therapeutic targets for EA-based RA treatment.