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帕金森病中氧化应激相关枢纽基因的鉴定与验证

Identification and Validation of Oxidative Stress-Related Hub Genes in Parkinson's Disease.

作者信息

Zhu Lina, Chen Deng, Wang Xiangxiu, He Chengqi

机构信息

Rehabilitation Medicine Center and Institute of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China.

Key Laboratory of Rehabilitation Medicine in Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, China.

出版信息

Mol Neurobiol. 2025 May;62(5):5466-5483. doi: 10.1007/s12035-024-04622-6. Epub 2024 Nov 18.

Abstract

Accumulating evidence suggests that oxidative stress plays a crucial role in the pathogenesis of Parkinson's disease (PD). The aims of this study were to identify oxidative stress-related hub genes, validate them through the construction of a diagnostic model, explore their interactions with miRNAs and transcription factors (TFs) and predict potential drug targets. Differentially expressed genes (DEGs) in the substantia nigra of PD patients were identified by analyzing a combination of datasets selected from the GEO database, including GSE7621, GSE20141, GSE49036, and GSE20163. The candidate genes associated with oxidative stress were screened by determining the overlap among the DEGs, oxidative stress-related genes (OSGs) and genes in key modules with the highest cor values identified via weighted gene coexpression network analysis (WGCNA). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to perform functional enrichment analysis of these candidate genes. The hub genes were identified via protein-protein interaction (PPI) analysis, and receiver operating characteristic (ROC) curves were constructed to assess the diagnostic value of each hub gene. Then, a diagnostic model was constructed via least absolute shrinkage and selection operator (LASSO) regression with the hub genes identified above, and the model was further validated in external validation datasets (GSE20292 and GSE20164). Gene-miRNA and gene-TF regulatory networks were predicted via the miRNet database, whereas candidate drugs were predicted via the Drug-Gene Interaction database. After analysis of the intersection of the 7975 DEGs, 434 OSGs, and 3582 genes identified through WGCNA, 76 candidate genes were identified. A total of 9 hub genes (JUN, KEAP1, SRC, GPX5, MMP9, TXN, MAPK3, GPX2, and IL1A) were identified via PPI and ROC curve analyses. A diagnostic model with the ability to reliably predict PD on the basis of the identified hub genes (AUC = 0.925) was constructed. Further analysis of these 9 genes revealed 64 targeted miRNAs, 35 TFs in regulatory networks and 86 potential therapeutic agents. Nine hub genes related to oxidative stress in the pathogenesis of PD were identified. These genes show strong diagnostic performance and could serve as therapeutic targets. These findings might facilitate the development of promising candidate biomarkers and potential disease-modifying therapies for PD.

摘要

越来越多的证据表明,氧化应激在帕金森病(PD)的发病机制中起着关键作用。本研究的目的是识别与氧化应激相关的枢纽基因,通过构建诊断模型对其进行验证,探索它们与微小RNA(miRNA)和转录因子(TF)的相互作用,并预测潜在的药物靶点。通过分析从基因表达综合数据库(GEO数据库)中选择的数据集组合,包括GSE7621、GSE20141、GSE49036和GSE20163,确定PD患者黑质中的差异表达基因(DEG)。通过确定DEG、氧化应激相关基因(OSG)和通过加权基因共表达网络分析(WGCNA)确定的具有最高相关系数值的关键模块中的基因之间的重叠,筛选与氧化应激相关的候选基因。利用基因本体论(GO)和京都基因与基因组百科全书(KEGG)数据库对这些候选基因进行功能富集分析。通过蛋白质-蛋白质相互作用(PPI)分析确定枢纽基因,并构建受试者工作特征(ROC)曲线以评估每个枢纽基因的诊断价值。然后,使用上述确定的枢纽基因通过最小绝对收缩和选择算子(LASSO)回归构建诊断模型,并在外部验证数据集(GSE20292和GSE20164)中进一步验证该模型。通过miRNet数据库预测基因-miRNA和基因-TF调控网络,而通过药物-基因相互作用数据库预测候选药物。在分析7975个DEG、434个OSG和通过WGCNA鉴定的3582个基因的交集后,确定了76个候选基因。通过PPI和ROC曲线分析共鉴定出9个枢纽基因(JUN、KEAP1、SRC、GPX5、MMP9、TXN、MAPK3、GPX2和IL1A)。构建了一个基于已鉴定的枢纽基因能够可靠预测PD的诊断模型(曲线下面积[AUC]=0.925)。对这9个基因的进一步分析揭示了64个靶向miRNA、调控网络中的35个TF和86种潜在治疗药物。确定了9个与PD发病机制中氧化应激相关的枢纽基因。这些基因具有很强的诊断性能,可作为治疗靶点。这些发现可能有助于开发有前景的候选生物标志物和潜在的PD疾病修饰疗法。

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