Jiang Xiang, Zhong Rongzhou, Dai Weifan, Huang Hui, Yu Qinyuan, Zhang Jiji Alexander, Cai Yanrong
Department of Orthopaedics and Rehabilitation, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University School of Medicine, Shanghai, 201619, People's Republic of China.
Department of Digital Hub, Decathlon International, Shanghai, 200131, People's Republic of China.
Int J Gen Med. 2021 Sep 29;14:6201-6213. doi: 10.2147/IJGM.S325561. eCollection 2021.
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.
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.
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.
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.
代谢综合征（MS）在导致骨关节炎（OA）发病机制中的作用已得到越来越多的认识，OA是最常见的以关节功能障碍为特征的关节炎。然而，OA与MS之间的具体机制仍不清楚。
从基因表达综合数据库（GEO）中检索OA和MS的基因表达谱及临床信息数据。通过加权基因共表达网络分析（WGCNA）确定MS关键模块中的基因，该基因与对照和MS样本之间的差异表达基因（DEG）相交，以获得MS的枢纽基因。通过基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析检测枢纽基因的潜在功能和途径。通过维恩分析，将对照和OA样本之间不同KEGG途径中涉及的基因与两组之间的DEG重叠，以获得受MS影响的OA的枢纽基因（MOHG）。此外，对MOHG进行最小绝对收缩和选择算子（LASSO）分析，以建立每种疾病的诊断模型。
共鉴定出61个MS的枢纽基因，这些基因在血小板活化、补体和凝血级联以及造血细胞谱系中显著富集。此外，在6个MOHG中筛选出4个候选基因（ELOVL7、F2RL3、GP9和ITGA2B）构建诊断模型，该模型在区分对照与MS和OA患者方面表现良好。基因集富集分析（GSEA）表明，这些诊断基因与免疫反应、脂肪细胞因子信号传导、脂肪酸代谢、细胞周期和血小板活化密切相关。
综上所述，我们鉴定出4种潜在的基因生物标志物用于诊断MS和OA患者，为MS和OA的诊断及治疗靶点提供了理论依据和参考。
