缺血性脑卒中铁死亡特征基因NFE2L2的鉴定与验证  

Identification and validation of characterized gene NFE2L2 for ferroptosis in ischemic stroke

作　　者：王咪 马书杰 刘杨 齐瑞[1] Wang Mi;Ma Shujie;Liu Yang;Qi Rui(Department of Rehabilitation,Yueyang Hospital of Integrated Chinese and Western Medicine,Shanghai University of Traditional Chinese Medicine,Shanghai 200437,China;Shanghai University of Traditional Chinese Medicine,Shanghai 201203,China;Second Rehabilitation Hospital of Shanghai,Shanghai 200431,China)

机构地区：[1]上海中医药大学附属岳阳中西医结合医院康复医学科,上海市200437 [2]上海中医药大学,上海市201203 [3]上海市第二康复医院,上海市200431

出　　处：《中国组织工程研究》2025年第7期1466-1474,共9页Chinese Journal of Tissue Engineering Research

基　　金：国家自然科学基金项目(81603713),项目负责人:马书杰;宝山区医学重点专科项目(BSZK-2023-BZ12,BSZK-2023-BP08),项目负责人:马书杰;宝山区科技创新项目(20-E-43),项目负责人:马书杰。

摘　　要：背景:铁死亡与缺血性脑卒中的发病密切相关,靶向铁死亡是一种治疗缺血性脑卒中有前景的方案,但具体调控靶点尚不明确。目的:通过生物信息学和机器学习方法筛选缺血性脑卒中铁死亡相关特征基因,并通过细胞实验进行验证,探讨铁死亡在缺血性脑卒中的作用。方法:基于GEO数据库和FerrDb数据库选取符合条件的缺血性脑卒中相关数据集和铁死亡表达数据集,通过t检验筛选铁死亡相关差异基因。对铁死亡相关差异基因进行GO功能富集分析与KEGG信号通路富集分析。通过PPI网络分析和机器学习筛选缺血性脑卒中铁死亡的特征基因,利用ROC分析和GSEA分析探究特征基因的准确性和生物功能。然后进行细胞实验,将HT22细胞分为对照组与缺血性脑卒中组,对照组不作任何干预,缺血性脑卒中组加入0.1 mol/L的H_(2)O_(2)干预24 h诱导细胞氧化应激和铁死亡,通过实时荧光定量RT-PCR和Western Blot验证铁死亡的发生和特征基因表达。结果与结论:(1)共获取45个铁死亡相关差异基因,GO和KEGG富集分析发现差异基因与氧化应激、自噬、铁死亡、脂肪细胞因子信号通路和线粒体代谢密切相关。(2)通过PPI网络中的MCODE插件和cytoHubba插件与机器学习中的LASSO算法和SVM-RFE算法共鉴定出1个铁死亡特征基因核因子E2相关因子2(nuclear factor erythroid 2-related factor 2,NFE2L2)。(3)对NFE2L2进行ROC曲线分析,发现在训练集和验证集中构建的诊断预测模型具有良好的准确性与特异性;对NFE2L2进行GSEA分析,发现特征基因通过免疫、炎症反应、氨基酸代谢及神经因子调控等方面参与缺血性脑卒中发病机制的调控。(4)细胞实验的RT-PCR和Western Blot分析表明,与对照组对比,缺血性脑卒中组中的酰基辅酶A合成酶长链家族成员4 mRNA和蛋白表达水平显著增高(P<0.05),谷胱甘肽过氧化物酶4 mRNA和蛋白表达水平显著降低(P<0.05);与�BACKGROUND:Ferroptosis is closely associated with the pathogenesis of ischemic stroke,and targeting ferroptosis is a promising regimen for the treatment of ischemic stroke,but the specific regulatory targets are unclear.OBJECTIVE:To screen ferroptosis-related characterized genes in ischemic stroke by bioinformatics and machine learning methods and validate them by cellular experiments to investigate the role of ferroptosis in ischemic stroke.METHODS:Eligible ischemic stroke-related datasets and ferroptosis expression datasets were selected based on GEO database and FerrDb database,and ferroptosis-related differential genes were screened by t-test.GO functional enrichment analysis with KEGG signaling pathway enrichment analysis was performed for ferroptosis-related differential genes.Characterized genes for ferroptosis in ischemic stroke were screened by PPI network analysis and machine learning.The reliability and biological functions of the characterized genes were explored using ROC analysis and GSEA analysis,followed by cell experiment.HT22 cells were divided into control and ischemic stroke groups.No intervention was made in the control group,and 0.1 mM H_2O_(2)was added to the ischemic stroke group for 24 hours to simulate cellular oxidative stress injury and ferroptosis.The ferroptosis and the expression of characterized genes were verified by real-time fluorescence quantitative polymerase chain reaction(RT-PCR)and western blot assay.RESULTS AND CONCLUSION:(1)Forty-five ferroptosis-associated differential genes were obtained,and GO and KEGG enrichment analyses revealed that the differential genes were closely associated with oxidative stress,autophagy,ferroptosis,adipocytokine signaling pathway,and mitochondrial metabolism.(2)A total of one ferroptosis characterized gene,nuclear factor erythroid 2-related factor 2(NFE2L2),was identified by the MCODE plugin and cytoHubba plugin in the PPI network with the LASSO algorithm and SVM-RFE algorithm in machine learning.(3)Receiver operating characteristic curve ana

关 键 词：缺血性脑卒中 铁死亡 生物信息学 HT22细胞 机器学习 特征基因 细胞实验 NFE2L2 ASCL4 GPX4 

分 类 号：R452[医药卫生—治疗学] R363[医药卫生—临床医学] R364