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

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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.

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Data availability

This datasets analyzed during the current study are available in the Gene Expression Omnibus, accession number: GSE143272 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE143272).

Abbreviations

ASMs:

Antiseizure medications

VPA:

Valproate

CBZ:

Carbamazepine

PHT:

Phenytoin

CMap:

Connectivity Map

DEGs:

Differentially expressed genes

GEO:

Gene Expression Omnibus

DAVID:

Visualization and Integrated Discovery

SNM:

Supervised Normalization of Microarrays

WTCS:

Weighted connectivity score

AUC:

Area under the receiver operating characteristic curve

FTH1:

Ferritin heavy chain 1

NCOA4:

Nuclear receptor coactivator 4

PCBP1:

Poly r(C) binding protein 1

OPMD:

Oculopharyngeal muscular dystrophy

IQR:

Interquartile range

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Funding

This work was supported by grants from the National Natural Science Foundation of China (No. 81871018), the National Natural Science Foundation of China (No. 21575094), and Med-X Center for Informatics funding project (No. YGJC001).

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Author notes

  1. Yifei Duan and Liyuan Kang have contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Department of Neurology, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China

    Yifei Duan & Lei Chen

  2. College of Chemistry, Sichuan University, Chengdu, 610064, Sichuan, China

    Liyuan Kang, Yujie He, Menglong Li & Zhining Wen

  3. University of Arkansas at Little Rock and University of Arkansas for Medical Sciences Joint Bioinformatics Program, Little Rock, AK, 72204, USA

    Ting Li

  4. Medical Big Data Center, Sichuan University, Chengdu, 610064, Sichuan, China

    Zhining Wen

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  1. Yifei Duan
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by YD, LK, and YH. The first draft of the manuscript was written by YD, LK, and YH. All authors contributed to discussions regarding the results and the manuscript. LC, ZW, ML, and TL revised the manuscript. All authors read and approved the final manuscript.

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Correspondence to Zhining Wen or Lei Chen.

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Duan, Y., Kang, L., He, Y. et al. A pilot study on identifying gene signatures as markers for predicting patient response to antiseizure medications. Neurol Sci 44, 2137–2148 (2023). https://doi.org/10.1007/s10072-023-06605-2

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