Identification of diagnostic genes and drug prediction in metabolic syndrome-associated rheumatoid arthritis by integrated bioinformatics analysis, machine learning, and molecular docking.

ABSTRACT

Background: Interactions between the immune and metabolic systems may play a crucial role in the pathogenesis of metabolic syndrome-associated rheumatoid arthritis (MetS-RA). The purpose of this study was to discover candidate biomarkers for the diagnosis of RA patients who also had MetS. Methods: Three RA datasets and one MetS dataset were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms including Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) were employed to identify hub genes in MetS-RA. Enrichment analysis was used to explore underlying common pathways between MetS and RA. Receiver operating characteristic curves were applied to assess the diagnostic performance of nomogram constructed based on hub genes. Protein-protein interaction, Connectivity Map (CMap) analyses, and molecular docking were utilized to predict the potential small molecule compounds for MetS-RA treatment. qRT-PCR was used to verify the expression of hub genes in fibroblast-like synoviocytes (FLS) of MetS-RA. The effects of small molecule compounds on the function of RA-FLS were evaluated by wound-healing assays and angiogenesis experiments. The CIBERSORT algorithm was used to explore immune cell infiltration in MetS and RA. Results: MetS-RA key genes were mainly enriched in immune cell-related signaling pathways and immune-related processes. Two hub genes (TYK2 and TRAF2) were selected as candidate biomarkers for developing nomogram with ideal diagnostic performance through machine learning and proved to have a high diagnostic value (area under the curve, TYK2, 0.92; TRAF2, 0.90). qRT-PCR results showed that the expression of TYK2 and TRAF2 in MetS-RA-FLS was significantly higher than that in non-MetS-RA-FLS (nMetS-RA-FLS). The combination of CMap analysis and molecular docking predicted camptothecin (CPT) as a potential drug for MetS-RA treatment. In vitro validation, CPT was observed to suppress the cell migration capacity and angiogenesis capacity of MetS-RA-FLS. Immune cell infiltration results revealed immune dysregulation in MetS and RA. Conclusion: Two hub genes were identified in MetS-RA, a nomogram for the diagnosis of RA and MetS was established based on them, and a potential therapeutic small molecule compound for MetS-RA was predicted, which offered a novel research perspective for future serum-based diagnosis and therapeutic intervention of MetS-RA.