Exploring Diagnostic Biomarkers and Comorbid Pathogenesis for Osteoarthritis and Metabolic Syndrome via Bioinformatics Approach

Abstract

Xiang Jiang,1,* Rongzhou Zhong,1,* Weifan Dai,2,* Hui Huang,1 Qinyuan Yu,1 Jiji Alexander Zhang,3 Yanrong Cai3 1Department of Orthopaedics and Rehabilitation, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University School of Medicine, Shanghai, 201619, People’s Republic of China; 2Department of Digital Hub, Decathlon International, Shanghai, 200131, People’s Republic of China; 3Department of Medicine, Heidelberg University Hospital, University of Heidelberg, Heidelberg, 69120, Germany*These authors contributed equally to this workCorrespondence: Yanrong CaiDepartment of Medicine, Heidelberg University Hospital, University of Heidelberg, Heidelberg, 69120, GermanyEmail [email protected]: Metabolic syndrome (MS) has grown in recognition to contribute to the pathogenesis of osteoarthritis (OA), which is the most prevalent arthritis characterized by joint dysfunction. However, the specific mechanism between OA and MS remains unclear.Methods: The gene expression profiles and clinical information data of OA and MS were retrieved from the Gene Expression Omnibus (GEO) database. The genes in the key module of MS were identified by weighted gene co-expression network analysis (WGCNA), which intersected with the differentially expressed genes (DEGs) between control and MS samples to obtain hub genes for MS. The potential functions and pathways of hub genes were detected through the Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) analyses. The genes involved in the different KEGG pathways between the control and OA samples overlapped with the DEGs between the two groups via the Venn analysis to gain the hub genes for OA affected by MS (MOHGs). Additionally, the least absolute shrinkage and selection operator (LASSO) was performed on the MOHGs to establish a diagnostic model for each disease.Results: A total of 61 hub genes for MS were identified that significantly enriched in platelet activation, complement and coagulation cascades, and hematopoietic cell lineage. Besides, 4 candidate genes (ELOVL7, F2RL3, GP9, and ITGA2B) were screened among the 6 MOHGs to construct a diagnostic model, showing good performance for distinguishing controls from patients with MS and OA. GSEA suggested that these diagnostic genes were closely associated with immune response, adipocytokine signaling, fatty acid metabolism, cell cycle, and platelet activation.Conclusion: Taken together, we identified 4 potential gene biomarkers for diagnosing MS and OA patients, providing a theoretical basis and reference for the diagnostics and treatment targets of MS and OA.Keywords: osteoarthritis, OA, metabolic syndrome, MS, diagnostic biomarkers, pathogenesis, bioinformatics

Keywords

• osteoarthritis
• oa
• metabolic syndrome
• ms
• diagnostic biomarkers
• pathogenesis
• bioinformatics