Platelets-related signature based diagnostic model in rheumatoid arthritis using WGCNA and machine learning - Liu, Yuchen; Jiang, Haixu; Kang, Tianlun; Shi, Xiaojun; Liu, Xiaoping; Li, Chen; Hou, Xiujuan; Li, Meiling (Frontiers in Immunology, 2023)
Background and aim: Rheumatoid arthritis (RA) is an autoinflammatory disease that may lead to severe disability. The diagnosis of RA is limited due to the need for biomarkers with both reliability and efficiency. Platelets are deeply involved in the pathogenesis of RA. Our study aims to identify the underlying mechanism and screening for related biomarkers. Methods: We obtained two microarray datasets (GSE93272 and GSE17755) from the GEO database. We performed Weighted correlation network analysis (WGCNA) to analyze the expression modules in differentially expressed genes identified from GSE93272. We used KEGG, GO and GSEA enrichment analysis to elucidate the platelets-relating signatures (PRS). We then used the LASSO algorithm to develop a diagnostic model. We then used GSE17755 as a validation cohort to assess the diagnostic performance by operating Receiver Operating Curve (ROC). Results: The application of WGCNA resulted in the identification of 11 distinct co-expression modules. Notably, Module 2 exhibited a prominent association with platelets among the differentially expressed genes (DEGs) analyzed. Furthermore, a predictive model consisting of six genes (MAPK3, ACTB, ACTG1, VAV2, PTPN6, and ACTN1) was constructed using LASSO coefficients. The resultant PRS model demonstrated excellent diagnostic accuracy in both cohorts, as evidenced by area under the curve (AUC) values of 0.801 and 0.979. Conclusion: We elucidated the PRSs occurred in the pathogenesis of RA and developed a diagnostic model with excellent diagnostic potential.