ArticleInternational journal of general medicine2021
Exploring Diagnostic Biomarkers and Comorbid Pathogenesis for Osteoarthritis and Metabolic Syndrome via Bioinformatics Approach.
Article in International journal of general medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 9 citations in OpenAlex.
- Investigating Angiogenesis-Related Biomarkers in Osteoarthritis Patients Through Transcriptomic Profiling.Journal of inflammation research · 2024Article
- Biomarkers of Osteoarthritis-A Narrative Review on Causal Links with Metabolic Syndrome.Life (Basel, Switzerland) · 2023Review
- Identification of immune-associated genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning.Frontiers in immunology · 2023Article
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMetabolic 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.
methodsThe 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.
resultsA 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.
conclusionTaken 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.
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