Integrative analysis of gene expression associated with epilepsy in human epilepsy and animal models

Chen,Hengling; Xu,Guozheng; Du,Hao; Yi,Minhan; Li,Chenhong

doi:10.3892/mmr.2016.5122

Integrative analysis of gene expression associated with epilepsy in human epilepsy and animal models

Authors:
- Hengling Chen
- Guozheng Xu
- Hao Du
- Minhan Yi
- Chenhong Li
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Affiliations: The Laboratory of Membrane Ion Channels and Medicine, Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis and Treatment, College of Biomedical Engineering, South‑Central University for Nationalities, Wuhan, Hubei 430074, P.R. China, Department of Neurosurgery, Wuhan General Hospital of Guangzhou Military Command, Wuhan, Hubei 430070, P.R. China, The State Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410078, P.R. China
Published online on: April 13, 2016 https://doi.org/10.3892/mmr.2016.5122
Pages: 4920-4926

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Abstract

Epilepsy is a severe neuropsychiatric disorder, the cause of which remains to be elucidated. Genome‑wide association studies, DNA microarrays and proteomes have been widely applied to identify the candidate genes involved in epileptogenesis, and integrative analyses are often capable of extracting more detailed biological information from the data. In the present study, a total number of 1,065 genes in different animal models were collected to construct an epilepsy candidate gene database. Further analyses suggested that the response to organic substances, the intracellular signaling cascade and neurological system processes were significantly enriched biological processes, and the mitogen-activated protein kinase pathway was identified as a putative epileptogenic signaling pathway. In addition, the five key genes, growth factor receptor bound 2, amyloid β (A4) precursor protein, transforming growth factor‑β, vascular endothelial growth factor and cyclin‑dependent kinase inhibitor 1, were identified as being critical as central nodes in the protein networks. Reverse transcription‑quantitative polymerase chain reaction analysis revealed that these genes were all upregulated at the mRNA level in the epileptic loci compared with the resection margin of tissue samples from the same patients diagnosed with epilepsy. The data mining performed in the present study thus was shown to be a useful tool, which may contribute to obtaining further information on epileptic disorders and delineating the molecular mechanism of the associated genes.

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