Table2_Analysis and Construction of a Molecular Diagnosis Model of Drug-Resistant Epilepsy Based on Bioinformatics.XLSX

Background: Epilepsy is a complex chronic disease of the nervous system which influences the health of approximately 70 million patients worldwide. In the past few decades, despite the development of novel antiepileptic drugs, around one-third of patients with epilepsy have developed drug-resistant epilepsy. We performed a bioinformatic analysis to explore the underlying diagnostic markers and mechanisms of drug-resistant epilepsy.
Methods: Weighted correlation network analysis (WGCNA) was applied to genes in epilepsy samples downloaded from the Gene Expression Omnibus database to determine key modules. The least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to screen the genes resistant to carbamazepine, phenytoin, and valproate, and sensitivity of the three-class classification SVM model was verified through the receiver operator characteristic (ROC) curve. A protein–protein interaction (PPI) network was utilized to analyze the protein interaction relationship. Finally, ingenuity pathway analysis (IPA) was adopted to conduct disease and function pathway and network analysis.
Results: Through WGCNA, 72 genes stood out from the key modules related to drug resistance and were identified as candidate resistance genes. Intersection analysis of the results of the LASSO and SVM-RFE algorithms selected 11, 4, and 5 drug-resistant genes for carbamazepine, phenytoin, and valproate, respectively. Subsequent union analysis obtained 17 hub resistance genes to construct a three-class classification SVM model. ROC showed that the model could accurately predict patient resistance. Expression of 17 hub resistance genes in healthy subjects and patients was significantly different. The PPI showed that there are six resistance genes (CD247, CTSW, IL2RB, MATK, NKG7, and PRF1) that may play a central role in the resistance of epilepsy patients. Finally, IPA revealed that resistance genes (PRKCH and S1PR5) were involved in “CREB signaling in Neurons.”
Conclusion: We obtained a three-class SVM model that can accurately predict the drug resistance of patients with epilepsy, which provides a new theoretical basis for research and treatment in the field of drug-resistant epilepsy. Moreover, resistance genes PRKCH and S1PR5 may cooperate with other resistance genes to exhibit resistance effects by regulation of the cAMP-response element-binding protein (CREB) signaling pathway.

背景：癫痫是一种复杂的神经系统慢性疾病，全球约有7000万患者的健康受其影响。近数十年来，尽管新型抗癫痫药物不断研发问世，但仍有约三分之一的癫痫患者发展为耐药性癫痫。本研究通过生物信息学分析，旨在探索耐药性癫痫潜在的诊断标志物及发病机制。
方法：将加权基因共表达网络分析（Weighted correlation network analysis, WGCNA）应用于从基因表达综合数据库（Gene Expression Omnibus, GEO）下载的癫痫样本中的基因，以筛选关键模块。采用最小绝对收缩和选择算子回归（Least absolute shrinkage and selection operator, LASSO）与支持向量机-递归特征消除（support vector machine-recursive feature elimination, SVM-RFE）算法，筛选针对卡马西平、苯妥英钠及丙戊酸盐的耐药基因，并通过受试者工作特征（Receiver Operating Characteristic, ROC）曲线验证三分类支持向量机模型的分类效能。构建蛋白质相互作用（Protein-Protein Interaction, PPI）网络以分析蛋白间的相互作用关系。最后，采用Ingenuity通路分析（Ingenuity Pathway Analysis, IPA）开展疾病及功能通路与网络分析。
结果：通过WGCNA分析，从与耐药性相关的关键模块中筛选出72个基因，将其鉴定为候选耐药基因。对LASSO与SVM-RFE算法的结果取交集，分别筛选出11、4、5个针对卡马西平、苯妥英钠及丙戊酸盐的耐药基因。随后通过并集分析得到17个核心耐药基因，用于构建三分类支持向量机模型。ROC曲线分析显示，该模型可精准预测患者的耐药状态。17个核心耐药基因在健康受试者与癫痫患者中的表达水平存在显著差异。PPI网络分析显示，CD247、CTSW、IL2RB、MATK、NKG7及PRF1这6个耐药基因可能在癫痫患者的耐药过程中发挥核心作用。最后，IPA分析显示，PRKCH与S1PR5这两个耐药基因参与了"神经元内CREB信号通路"。
结论：本研究构建了可精准预测癫痫患者耐药状态的三分类支持向量机模型，为耐药性癫痫领域的研究与治疗提供了新的理论依据。此外，PRKCH与S1PR5可与其他耐药基因协同作用，通过调控环腺苷酸应答元件结合蛋白（cAMP-response element-binding protein, CREB）信号通路发挥耐药效应。