Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China.
Department of Acupuncture, Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.
Sci Rep. 2024 Sep 28;14(1):22437. doi: 10.1038/s41598-024-73188-z.
Knee osteoarthritis (KOA) is a common chronic joint disease globally. Synovial inflammation plays a pivotal role in its pathogenesis, preceding cartilage damage. Identifying biomarkers in osteoarthritic synovial tissues holds promise for early diagnosis and targeted interventions. Gene expression profiles were obtained from the Gene Expression Omnibus database. Subsequent analyses included differential expression gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) on the combined datasets. We performed functional enrichment analysis on the overlapping genes between DEGs and module genes and constructed a protein-protein interaction network. Using Cytoscape software, we identified hub genes related to the disease and conducted gene set enrichment analysis on these hub genes. The CIBERSORT algorithm was employed to evaluate the correlation between hub genes and the abundance of immune cells within tissues. Finally, Mendelian randomization analysis was utilized to assess the potential of these hub genes as biomarkers. We identified 46 differentially expressed genes (DEGs), comprising 20 upregulated and 26 downregulated genes. Using WGCNA, we constructed a gene co-expression network and selected the most relevant modules, resulting in 24 intersecting genes with the DEGs. KEGG enrichment analysis of the intersecting genes identified the IL-17 signaling pathway, associated with inflammation, as the most significant pathway. Cytoscape software was utilized to rank the candidate genes, with JUN, ATF3, FOSB, NR4A2, and IL6 emerging as the top five based on the Degree algorithm. A nomogram model incorporating these five genes, supported by ROC curve analysis, validated their diagnostic efficacy. Immune infiltration and correlation analysis revealed that macrophages were significantly associated with JUN (p < 0.01), FOSB (p < 0.01), and NR4A2 (p < 0.05). Additionally, T follicular helper cells showed significant associations with ATF3 (p < 0.05), FOSB (p < 0.05), and JUN (p < 0.05). Mendelian randomization analysis provided strong evidence linking JUN (IVW: OR = 0.910, p = 0.005) and IL6 (IVW: OR = 1.024, p = 0.026) with KOA. Through the utilization of various bioinformatics analysis methods, we have pinpointed key hub genes relevant to knee osteoarthritis. These findings hold promise for advancing pre-symptomatic diagnostic strategies and enhancing our understanding of the biological underpinnings behind knee osteoarthritis susceptibility genes.
膝关节骨关节炎（KOA）是一种常见的全球慢性关节疾病。滑膜炎症在其发病机制中起着关键作用，先于软骨损伤。在骨关节炎滑膜组织中识别生物标志物有望实现早期诊断和靶向干预。从基因表达综合数据库中获得基因表达谱。随后的分析包括对合并数据集进行差异表达基因（DEG）分析和加权基因共表达网络分析（WGCNA）。我们对 DEG 和模块基因之间的重叠基因进行了功能富集分析，并构建了蛋白质-蛋白质相互作用网络。使用 Cytoscape 软件，我们确定了与疾病相关的关键基因，并对这些关键基因进行了基因集富集分析。使用 CIBERSORT 算法评估了关键基因与组织中免疫细胞丰度的相关性。最后，使用 Mendelian 随机分析评估了这些关键基因作为生物标志物的潜力。我们鉴定了 46 个差异表达基因（DEG），包括 20 个上调基因和 26 个下调基因。使用 WGCNA 构建基因共表达网络，并选择最相关的模块，得到与 DEG 有 24 个交集的基因。对交集基因的 KEGG 富集分析发现，与炎症相关的 IL-17 信号通路是最重要的通路。使用 Cytoscape 软件对候选基因进行排名，根据 Degree 算法，JUN、ATF3、FOSB、NR4A2 和 IL6 成为前五名。一个包含这五个基因的列线图模型，通过 ROC 曲线分析验证了其诊断效果。免疫浸润和相关性分析表明，巨噬细胞与 JUN（p<0.01）、FOSB（p<0.01）和 NR4A2（p<0.05）显著相关。此外，滤泡辅助 T 细胞与 ATF3（p<0.05）、FOSB（p<0.05）和 JUN（p<0.05）显著相关。Mendelian 随机分析提供了强有力的证据表明 JUN（IVW：OR=0.910，p=0.005）和 IL6（IVW：OR=1.024，p=0.026）与 KOA 相关。通过使用各种生物信息学分析方法，我们确定了与膝关节骨关节炎相关的关键枢纽基因。这些发现有望推进膝关节骨关节炎的无症状诊断策略，并增进我们对膝关节骨关节炎易感基因生物学基础的理解。
