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Updated: Jun 4, 2025

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Identification and validation of susceptibility modules and hub genes in polyarticular juvenile idiopathic arthritis

Junfeng Liu1,2, Jianhui Fan3, Hongxiang Duan4

  • 1Department of Orthopedics, Dazhou Central Hospital, Dazhou, China.

Autoimmunity
|December 19, 2024
PubMed

Insights

This study identified four key genes (PHLDA1, EGR3, CXCL2, PF4V1) associated with juvenile idiopathic arthritis (JIA) progression. These genes show promise as biomarkers for early diagnosis and personalized treatment of JIA.

Area of Science:

  • Immunology
  • Genetics
  • Computational Biology

Background:

  • Juvenile idiopathic arthritis (JIA) is a chronic autoimmune childhood disease causing joint inflammation and potential long-term damage.
  • Early identification of prognostic factors is crucial for effective management and preventing joint deterioration.

Purpose of the Study:

  • To identify genes linked to the progression and prognosis of polyarticular JIA.
  • To enhance clinical diagnosis and treatment strategies for JIA patients.

Main Methods:

  • Analysis of the Gene Expression Omnibus (GEO) dataset GSE1402 for differentially expressed genes (DEGs) in JIA polyarticular patients.
  • Application of Weighted Gene Co-expression Network Analysis (WGCNA) and protein-protein interaction (PPI) networks to identify key gene modules and hub genes.
  • Utilized random forest models for biomarker screening and ROC curves for validation, alongside GO and KEGG pathway analysis.

Main Results:

  • Identified PHLDA1, EGR3, CXCL2, and PF4V1 as significantly associated with JIA polyarticular progression and prognosis.
  • These genes demonstrated high diagnostic and prognostic assessment value in the study.
  • Functional enrichment analysis highlighted potential JIA-related pathways.

Conclusions:

  • PHLDA1, EGR3, CXCL2, and PF4V1 serve as potential molecular biomarkers for JIA polyarticular.
  • These biomarkers offer valuable insights for early diagnosis and personalized treatment approaches.
  • Further research can leverage these findings for improved JIA patient outcomes.
Abstract

Keywords:
Juvenile rheumatoid arthritisco-expression modulesprognostic genesweighted gene co-expression network analysis

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