This study, leveraging DEGs identified from RA transcriptomic data, overcomes the limitations of current RA research which predominantly relies on single-omics data analysis (e.g., transcriptomics or PPI networks) or conventional biostatistical methods for biomarker screening.[20,21] By integrating transcriptomics, PPI networks, and machine learning algorithms, we identified key MMRG-associated biomarkers (COX7B, NDUFB3, UQCRQ). Here, NDUFB3 is linked to rheumatoid arthritis.