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利用多个数据集探索癫痫背后的自噬相关分子机制。

Exploration of autophagy-related molecular mechanisms underlying epilepsy using multiple datasets.

作者信息

Wang Yongfei, Zeng Haoxuan, Liu Chongxu, Chen Jianjun, Huang Yihong, Zhou Xianju

机构信息

Department of Neurology, Houjie Hospital and Clinical College of Guangdong Medical University, China.

Department of Neurology, Southern Medical University Hospital of Integrated Traditional Chinese and Western Medicine, Southern Medical University, China.

出版信息

J Int Med Res. 2025 Aug;53(8):3000605251364784. doi: 10.1177/03000605251364784. Epub 2025 Aug 16.

Abstract

ObjectiveTo elucidate the molecular mechanisms underlying epilepsy, we investigated autophagy-related differentially expressed genes in epilepsy patients.MethodsWe analyzed GSE143272 and GSE4290 microarray datasets from the NCBI Gene Expression Omnibus database, which is established based on evaluations of peripheral blood samples. Using a bioinformatics approach, autophagy-related differentially expressed genes between epilepsy patients and healthy controls were identified. Further analyses including Least Absolute Shrinkage and Selection Operator regression, immune cell infiltration, and pathway enrichment were conducted. Experimental validation was performed using quantitative reverse transcription-polymerase chain reaction in a mouse epileptic model. Additionally, the Connectivity Map database was employed to predict potential drugs.ResultsIn total, 49 autophagy-related differentially expressed genes were identified. A Least Absolute Shrinkage and Selection Operator logistic model revealed four autophagy-related differentially expressed genes, namely, , , , and . Furthermore, a novel diagnostic model with robust validation metrics was established. Immune cell infiltration analysis underscored the significance of immune response in epilepsy, revealing distinct profiles in patients. Additionally, pathway enrichment analysis using gene set enrichment analysis and gene set variation analysis revealed that critical genes were implicated in diverse pathways, including metabolic and neurodegenerative diseases. The expression levels of these key genes were experimentally corroborated using quantitative reverse transcription-polymerase chain reaction in the hippocampus tissues of status epileptic mice. Finally, Connectivity Map analysis suggested three antiseizure drugs (cabergoline, capsazepine, and zolantidine).ConclusionsOur results provide insights into potential biomarker candidates, thus contributing to clinical diagnosis and the development of new antiseizure drugs.

摘要

目的

为阐明癫痫的分子机制,我们研究了癫痫患者中自噬相关的差异表达基因。

方法

我们分析了来自NCBI基因表达综合数据库的GSE143272和GSE4290微阵列数据集,该数据库基于对外周血样本的评估建立。采用生物信息学方法,鉴定癫痫患者与健康对照之间自噬相关的差异表达基因。进行了包括最小绝对收缩和选择算子回归、免疫细胞浸润和通路富集在内的进一步分析。在小鼠癫痫模型中使用定量逆转录-聚合酶链反应进行实验验证。此外,利用连通图数据库预测潜在药物。

结果

总共鉴定出49个自噬相关的差异表达基因。最小绝对收缩和选择算子逻辑模型揭示了4个自噬相关的差异表达基因,即 、 、 和 。此外,建立了一个具有稳健验证指标的新型诊断模型。免疫细胞浸润分析强调了免疫反应在癫痫中的重要性,揭示了患者的不同特征。此外,使用基因集富集分析和基因集变异分析进行的通路富集分析表明,关键基因涉及多种通路,包括代谢和神经退行性疾病。在癫痫持续状态小鼠的海马组织中使用定量逆转录-聚合酶链反应通过实验证实了这些关键基因的表达水平。最后,连通图分析提示了三种抗癫痫药物(卡麦角林、辣椒素和佐兰替丁)。

结论

我们的结果为潜在的生物标志物候选物提供了见解,从而有助于临床诊断和新抗癫痫药物的开发。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae2b/12357995/dde930b39a57/10.1177_03000605251364784-fig1.jpg

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