A pilot study on identifying gene signatures as markers for predicting patient response to antiseizure medications

Abstract

The majority of the biomarkers were associated with the diagnosis of epilepsy and few of them can be applied to predict the response to antiseizure medications (ASMs). In this study, we identified 26 significantly up-regulated genes and 32 down-regulated genes by comparing the gene expression profiles of patients with epilepsy that responded to valproate with those without applying any ASM. The results of gene set enrichment analysis indicated that the ferroptosis pathway was significantly impacted (p = 0.0087) in patients who responded to valproate. Interestingly, the gene NCOA4 in this pathway exhibited significantly different expression levels between the two groups, indicating that NCOA4 could serve as a potential biomarker to better understand the mechanism of valproate resistance. In addition, six up-regulated genes SF3A2, HMGN2, PABPN1, SSBP3, EFTUD2, and CREB3L2 as well as six down-regulated genes ZFP36L1, ACRC, SUB1, CALM2, TLK1, and STX2 also showed significantly different expression patterns between the two groups. Moreover, based on the gene expression profiles of the patients with the treatment of valproate, carbamazepine, and phenytoin, we proposed a strategy for predicting the response to the ASMs by using the Connectivity Map scoring method. Our findings could be helpful for better understanding the mechanisms of drug resistance of ASMs and improving the clinical treatment of epilepsy.

Keywords: Antiseizure medications; Drug response; Epilepsy; Gene expression profiles; RNA sequencing; Valproate.