Construction and validation of a predictive model for the efficacy of valproic acid monotherapy in epilepsy based on Lasso-logistic regression

TL;DR

Researchers developed a model to predict valproic acid effectiveness in epilepsy patients using gene data, identifying SNORD3A as a potential biomarker.

Contribution

A novel predictive model using Lasso-logistic regression and SNORD3A as a biomarker for valproic acid efficacy in epilepsy.

Findings

A predictive model with an AUC of 0.70 was developed to predict valproic acid resistance in epilepsy patients.

SNORD3A gene expression showed significant differences between responders and non-responders, validating its potential as a biomarker.

86 genes were identified as related to valproic acid response, with three (NELL2, SNORD3A, mir-1974) selected for the final model.

Abstract

•valproic acid (Depakene) efficacy varies; a predictive model was developed.•86 genes linked to VPA response: 3 genes (NELL2, SNORD3A, mir-1974) in final model.•AUC of 0.70 indicates good predictive ability for VPA resistance.•qPCR validation confirms SNORD3A as a potential biomarker for VPA efficacy.

valproic acid (Depakene) efficacy varies; a predictive model was developed.

86 genes linked to VPA response: 3 genes (NELL2, SNORD3A, mir-1974) in final model.

AUC of 0.70 indicates good predictive ability for VPA resistance.

qPCR validation confirms SNORD3A as a potential biomarker for VPA efficacy.

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.

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