Identification and Experimental Validation of Key Biomarkers for Rheumatoid Arthritis Based on Bioinformatics...

Area of Science:

• Immunology
• Bioinformatics
• Genetics

Background:

• Rheumatoid arthritis (RA) is a chronic autoimmune disease with complex molecular underpinnings.
• Current diagnostic and therapeutic strategies for RA are limited by a lack of comprehensive understanding of key molecular players and their immune microenvironment interactions.
• Systematic biomarker screening with multi-algorithm validation is needed for RA.

Purpose of the Study:

• To identify novel biomarkers for rheumatoid arthritis (RA) using bioinformatics and machine learning.
• To investigate the link between identified biomarkers and the RA immune microenvironment.
• To support the development of targeted therapies and improve RA diagnosis.

Main Methods:

• Integrated multiple RA transcriptomic datasets from the GEO database.
• Applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify RA-associated gene modules.
• Utilized 11 machine learning approaches with 107 permutations for feature gene screening.
• Validated candidate biomarkers using ROC curves, DCA, confusion matrices, and a collagen-induced arthritis (CIA) rat model with immunohistochemistry.

Main Results:

• Identified FOSL2, JUN, and EGR1 as key potential biomarkers for RA through integrated analysis.
• These biomarkers demonstrated high individual diagnostic accuracy (AUC > 0.8).
• Found significant enrichment of mast cells in the RA microenvironment and confirmed high expression of FOSL2, JUN, and EGR1 in CIA rat models.

Conclusions:

• FOSL2, JUN, and EGR1 are identified as promising biomarkers for rheumatoid arthritis (RA).
• These genes may play significant roles in RA pathogenesis.
• The findings support the potential clinical application of these biomarkers for RA diagnosis and targeted therapy.