Unveiling potential diagnostic biomarkers for rheumatoid arthritis through integrated gene expression analysis - PubMed
7 hours ago
- #biomarkers
- #gene-expression
- #rheumatoid-arthritis
- Identified potential diagnostic biomarkers for rheumatoid arthritis (RA) through integrated gene expression analysis.
- Used Weighted Gene Correlation Network Analysis (WGCNA) and machine learning algorithms (RandomForest, SVM-REF, LASSO, CNN) to identify key genes.
- Found 543 differentially expressed genes (DEGs), narrowed down to 273 key genes involved in inflammatory response pathways.
- Selected five genes (GABARAPL1, FKBP5, PCDH9, SLAMF8) with high diagnostic potential based on AUC values.
- Constructed a predictive nomogram model and validated gene expression in RA synovial tissues.
- Immune infiltration analysis showed significant differences in immune cell levels between RA patients and healthy controls.
- Predicted potential therapeutic drugs targeting key genes, including (+)-chelidonine, daunorubicin, and bisacodyl.