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Early diagnosis and monitoring of rheumatoid arthritis (RA) progress are critical for effective treatment. In clinic, the detection of rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA) are usually combined to diagnose early RA. However, the poor specificity of RF and high heterogeneity of ACPA make the early diagnosis of RA still challenging. Bioinformatics analysis based on high-throughput omics is an emerging method to identify novel and effective biomarkers, which has been widely used in many diseases. Herein, utilizing an integrated strategy based on expression correlation analysis and weighted gene coexpression network analysis (WGCNA), we identified 76 RA-trait different expression genes (DEGs). Combined with protein-protein interaction (PPI) network construction and clustering, new hub genes associated in RA synovia, CD3D, GZMK, and KLRB1, were identified. We verified the specificity of these genes in the synovium of RA patients through three external datasets. We also observed high sensitivity and specificity of them for ACPA-negative patients. CD3D, GZMK, and KLRB1 are potentially key mediators of RA pathogenesis and markers for RA diagnosis.

frontiers in pharmacology

CD3D, GZMK, and KLRB1 Are Potential Markers for Early Diagnosis of Rheumatoid Arthritis, Especially in Anti-Citrullinated Protein Antibody-Negative Patients