Potential diagnostic markers and biological mechanism for osteoarthritis with obesity based on bioinformatics analysis

• Published in: PloS one Vol. 18; no. 12; p. e0296033
• Main Authors: Li, Qiu, Tang, Xijie, Li, Weihua
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 21.12.2023
• Subjects: Algorithms; Analysis; Arthritis; B cells; Bioinformatics; Biological markers; Biology and Life Sciences; Biomarkers; Care and treatment; CD8 antigen; Comorbidity; Complications and side effects; Computer and Information Sciences; Data mining; Datasets; Diagnosis; Diagnostic systems; Functional analysis; Gene expression; Genes; Genomes; Immunoregulation; Infiltration analysis; Inflammation; Lymphocytes; Lymphocytes T; Machine learning; Macrophages; Medical diagnosis; Medicine and Health Sciences; Molecular modelling; Network analysis; Nomograms; Obesity; Observational studies; Ontology; Osteoarthritis; Oxidative stress; Pathogenesis; Physical Sciences; Proteins; Research and Analysis Methods; Risk factors; Superoxide dismutase; T cells; Therapeutic targets; China; Taiwan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0296033

Numerous observational studies have shown that obesity (OB) is a significant risk factor in the occurrence and progression of osteoarthritis (OA), but the underlying molecular mechanism between them remains unclear. The study aimed to identify the key genes and pathogeneses for OA with OB. We obtained two OA and two OB datasets from the gene expression omnibus (GEO) database. First, the identification of differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were used to identify key genes for diagnosing OA with OB, and then the nomogram and receiver operating characteristic (ROC) curve were conducted to assess the diagnostic value of key genes. Second, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to explore the pathogenesis of OA with OB. Third, CIBERSORT was created to investigate immunocyte dysregulation in OA and OB. In this study, two genes (SOD2, ZNF24) were finally identified as key genes for OA with OB. These two key genes had high diagnostic values via nomogram and ROC curve calculation. Additionally, functional analysis emphasized that oxidative stress and inflammation response were shared pathogenesis of OB and AD. Finally, in OA and OB, immune infiltration analysis showed that SOD2 closely correlated to M2 macrophages, regulatory T cells, and CD8 T cells, and ZNF24 correlated to regulatory T cells. Overall, our findings might be new biomarkers or potential therapeutic targets for OA and OB comorbidity.