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Bioinformatic analysis identifies potential key genes of epilepsy

Yike Zhu, Dan Huang, Zhongyan Zhao, Chuansen Lu

Epilepsy is one of the most common brain disorders worldwide. It is usually hard to be identified properly, and a third of patients are drug-resistant. Genes related to the progression and prognosis of epilepsy are particularly needed to be identified.

Epilepsy, one of the most common brain conditions including both genetic and acquired disorders, affects at least 46 million people worldwide [1]. As a complex diagnosis consisting of multiple subtypes, it is usually hard to be identified properly. People with epilepsy have varied symptoms such as strange sensations, emotions, and behavior or convulsions, muscle spasms, and loss of consciousness when the brain sends out the wrong signals. Antiepileptic drugs are the main treatment and increasing nowadays.

Materials and methods:

Microarray data
We downloaded the microarray expression profiling dataset GSE143272 as peripheral blood expression profiles of patients with epilepsy, deposited by Rawat C et al., from the GEO (

Identification of DEGs
We downloaded the dataset GSE143272 from the GEO database, using GEO2R to analyze the DEGs between drug-free epilepsy patients and normal individuals. Epilepsy patients consisted of idiopathic, cryptogenic, and symptomatic epilepsy.

In this study, we performed bioinformatics analysis to search for the potential key genes associated with epilepsy. Male and female epilepsy were compared to healthy controls respectively on the hypothesis that epilepsy in different genders had different mechanisms. The results showed that 302 male-specific DEGs, 750 female-specific DEGs, and 183 overlapping DEGs were successfully identified. Those DEGs were put into multi-step bioinformatic functional annotations, including GO, KEGG, and PPI analysis..

Citation: Zhu Y, Huang D, Zhao Z, Lu C (2021) Bioinformatic analysis identifies potential key genes of epilepsy. PLoS ONE 16(9): e0254326.

Editor: Ming-Chang Chiang, Fu Jen Catholic University, TAIWAN

Received: June 9, 2021; Accepted: August 31, 2021; Published: September 23, 2021.

Copyright: © 2021 Zhu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

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