【摘要】 目的 旨在基于生物信息学方法筛选钠过载性坏死(necrosis by sodium overload,NECSO)相关基因,并探讨其与膝骨关节炎(knee osteoarthritis,KOA)的潜在关联。方法 本研究从GEO数据库中获取了KOA相关数据集,并结合GeneCards数据库中的NECSO相关基因集,通过加权基因共表达网络分析(weighted gene co-expression network analysis,WGCNA)、最小绝对收缩与选择算子回归分析及随机森林算法筛选出与KOA相关的关键基因。进一步通过免疫浸润分析评估免疫细胞浸润特征,探讨基因与免疫微环境之间的关系。通过外部数据集验证筛选出的基因在KOA中的潜在诊断效能。通过动物模型和细胞模型验证这些基因的表达趋势。最终,通过外部数据集验证筛选出的基因在KOA中的潜在诊断效能。结果 通过差异表达基因分析和WGCNA构建的基因网络,筛选出MYC、JUN、CA4和CSN1S14个关键基因。免疫浸润分析表明,这些基因与多种免疫细胞类型的浸润水平存在显著相关性。通过列线图模型和受试者工作特征曲线(receiver operating characteristic curve,ROC)分析,这些基因在KOA的诊断中显示出较好的预测性能。动物实验和细胞模型的结果进一步验证了MYC、JUN和CA4在KOA中的表达显著下调,而CSN1S1显著上调,这与生物信息学分析结果一致。结论 本研究鉴定出MYC、JUN、CA4、CSN1S1为KOA关键致病基因,明确其在KOA中的核心生物学功能;基于四基因构建的诊断模型具备良好预测效能,可为KOA早期诊断与精准治疗提供新型分子标志物;同时证实上述基因表达与免疫微环境重塑密切相关,为阐释KOA病理机制及免疫调控提供了全新研究方向。更多还原

【Abstract】 Objective To screen genes related to necrosis by sodium overload (NECSO) using bioinformatic methods,and to explore their potential association with knee osteoarthritis (KOA).Methods This study obtained KOA-related datasets from the GEO database and combined them with NECSO-related gene sets from the GeneCards database.Key genes associated with KOA were screened using weighted gene co-expression network analysis(WGCNA),least absolute shrinkage and selection operator (LASSO) regression,and random forest algorithm.Immune infiltration analysis was performed to evaluate immune cell infiltration characteristics and explore the relationship between genes and the immune microenvironment.The diagnostic potential of the screened genes in KOA was verified using external datasets,and their expression trends were validated in animal and cell models.Results Four key genes,MYC,JUN,CA4,and CSN1S1,were identified through differentially expressed gene analysis and WGCNA.Immune infiltration analysis revealed significant correlations among these genes and the infiltration levels of multiple immune cell types.Nomogram and receiver operating characteristic (ROC) curve analyses demonstrated that these genes exhibited favorable predictive performance for KOA diagnosis.Animal and cell experiments further confirmed that the expression of MYC,JUN,and CA4 was significantly downregulated,while CSN1S1 was significantly upregulated in KOA,consistent with the bioinformatics results.Conclusions This study identifies MYC,JUN,CA4,and CSN1S1 as key genes in KOA.The four-gene diagnostic model has good predictive value and may serve as promising biomarkers for KOA.These genes are closely related to the immune microenvironment,providing new directions for exploring KOA pathogenesis and immunity.更多还原