Identification and validation of susceptibility modules and hub genes in polyarticular juvenile idiopathic arthritis using WGCNA and machine learning.

Background: Juvenile idiopathic arthritis (JIA), superseding juvenile rheumatoid arthritis (JRA), is a chronic autoimmune disease affecting children and characterized by various types of childhood arthritis. JIA manifests clinically with joint inflammation, swelling, pain, and limited mobility, potentially leading to long-term joint damage if untreated. This study aimed to identify genes associated with the progression and prognosis of JIA polyarticular to enhance clinical diagnosis and treatment.

Methods: We analyzed the gene expression omnibus (GEO) dataset GSE1402 to screen for differentially expressed genes (DEGs) in peripheral blood single nucleated cells (PBMCs) of JIA polyarticular patients. Weighted gene co-expression network analysis (WGCNA) was applied to identify key gene modules, and protein-protein interaction networks (PPIs) were constructed to select hub genes. The random forest model was employed for biomarker gene screening. Functional enrichment analysis was conducted using David's online database, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to annotate and identify potential JIA pathways. Hub genes were validated using the receiver operating characteristic (ROC) curve.

Results: PHLDA1, EGR3, CXCL2, and PF4V1 were identified as significantly associated with the progression and prognosis of JIA polyarticular phenotype, demonstrating high diagnostic and prognostic assessment value.

Conclusion: These genes can be utilized as potential molecular biomarkers, offering valuable insights for the early diagnosis and personalized treatment of JIA polyarticular patients.