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

Journal: Autoimmunity

PMID: 39699225

Abstract

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.

Authors

Junfeng Liu

Indiana University-Purdue University, Indianapolis , 723 West Michigan St., SL 280, Indianapolis, Indiana 46202, United States.
Jianhui Fan

North Sichuan Medical College, Nanchong, China.
Hongxiang Duan

College of Public Health Sciences, Chulalongkorn University, Bangkok, Thailand.
Guoming Chen

Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao, China.
Weihua Zhang
Pingxi Wang

Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.

Keywords

Arthritis, Juvenile Biomarkers Child Computational Biology Databases, Genetic Gene Expression Profiling Gene Ontology Gene Regulatory Networks Genetic Predisposition to Disease Humans Machine Learning Prognosis Protein Interaction Maps Transcriptome

External Resources

View on PubMed Access via DOI PubMed (39699225)

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

Abstract

Authors

Keywords

External Resources

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