Xing Qichang, Liu Zheng, Lei Haibo, Liu Renzhu, Liu Xiang, Chen Jia
Clinical Pharmacy Department, Xiangtan Central Hospital (The Affiliated Hospital of Hunan University), Xiangtan, PR China; Zhou Honghao Research Institute Xiangtan, Xiangtan, PR China.
Clinical Pharmacy Department, Xiangtan Central Hospital (The Affiliated Hospital of Hunan University), Xiangtan, PR China.
Clinics (Sao Paulo). 2025 Jul 10;80:100684. doi: 10.1016/j.clinsp.2025.100684.
Valproic acid (VPA) is a broad-spectrum antiepileptic drug; but its therapeutic efficacy varies significantly among individuals. The objective of this study is to identify the specific biomarkers that can predict the efficacy of VPA.
The GSE143272 dataset from the Gene Expression Omnibus (GEO) was utilized to identify Differentially Expressed Genes (DEGs) between responders and non-responders to VPA. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify genes related to the non-responder phenotype. Intersection genes were selected to obtain the core genes affecting VPA tolerance. Lasso regression was applied to determine the core genes that influence the VPA effect. Lasso regression was applied to screen these core genes, using their expression values as independent variables and VPA response as the dependent variable in constructing a univariate logistic regression model. Peripheral blood samples from epileptic patients treated solely with VPA were collected according to nano-discharge standard. The expression levels of target genes were determined by qPCR to validate the accuracy of the model.
86 genes were closely related to the response phenotype through WGCNA. 13 intersection genes were obtained by intersection with 97 DEGs, which mainly involve mRNA splicing function and transport pathway. Ultimately, 3 genes-NELL2, SNORD3A and mir-1974 were included in the final model. The Area Under Curve (AUC) for this predictive model was found to be 0.70 (95 % CI: 0.70). qPCR analysis revealed a significant difference in the relative expression of the SNORD3A gene between the responder and non-responder groups.
Epilepsy patients are at an increased risk of developing drug resistance when undergoing VPA monotherapy. The risk prediction model based on Lasso-Logistic regression demonstrates strong predictive capabilities. The SNORD3A gene may serve as a valuable biomarker for predicting the likelihood of VPA resistance.
丙戊酸（VPA）是一种广谱抗癫痫药物；但其治疗效果在个体间存在显著差异。本研究的目的是确定能够预测VPA疗效的特定生物标志物。
利用来自基因表达综合数据库（GEO）的GSE143272数据集，鉴定VPA反应者与无反应者之间的差异表达基因（DEGs）。采用加权基因共表达网络分析（WGCNA）来鉴定与无反应者表型相关的基因。选择交集基因以获得影响VPA耐受性的核心基因。应用套索回归来确定影响VPA效果的核心基因。在构建单变量逻辑回归模型时，以这些核心基因的表达值作为自变量，VPA反应作为因变量，应用套索回归来筛选这些核心基因。按照纳升放电标准收集仅接受VPA治疗的癫痫患者的外周血样本。通过qPCR测定靶基因的表达水平，以验证模型的准确性。
通过WGCNA确定了86个与反应表型密切相关的基因。与97个DEGs进行交集分析后获得了13个交集基因，这些基因主要涉及mRNA剪接功能和转运途径。最终，3个基因——NELL2、SNORD3A和mir-1974被纳入最终模型。该预测模型的曲线下面积（AUC）为0.70（95%置信区间：0.70）。qPCR分析显示，反应者组与无反应者组之间SNORD3A基因的相对表达存在显著差异。
癫痫患者在接受VPA单药治疗时发生耐药的风险增加。基于套索-逻辑回归的风险预测模型具有较强的预测能力。SNORD3A基因可能是预测VPA耐药可能性的有价值生物标志物。
