Platelets-related signature based diagnostic model in rheumatoid arthritis using WGCNA and machine learning.

ABSTRACT:

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 GSE17

SUBMITTER: Liu Y 

PROVIDER: S-EPMC10327425 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature