Bioinformatic analysis identifies potential key genes of epilepsy.

<h4>Background</h4>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.<h4>Methods...

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Autores principales:	Yike Zhu, Dan Huang, Zhongyan Zhao, Chuansen Lu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f1d1989a55514306b41020e29353ec3d
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spelling	oai:doaj.org-article:f1d1989a55514306b41020e29353ec3d2021-12-02T20:14:15ZBioinformatic analysis identifies potential key genes of epilepsy.1932-620310.1371/journal.pone.0254326https://doaj.org/article/f1d1989a55514306b41020e29353ec3d2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254326https://doaj.org/toc/1932-6203<h4>Background</h4>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.<h4>Methods</h4>In our study, we downloaded the Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE143272. Differentially expressed genes (DEGs) with a fold change (FC) >1.2 and a P-value <0.05 were identified by GEO2R and grouped in male, female and overlapping DEGs. Functional enrichment analysis and Protein-Protein Interaction (PPI) network analysis were performed.<h4>Results</h4>In total, 183 DEGs overlapped (77 ups and 106 downs), 302 DEGs (185 ups and 117 downs) in the male dataset, and 750 DEGs (464 ups and 286 downs) in the female dataset were obtained from the GSE143272 dataset. These DEGs were markedly enriched under various Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. 16 following hub genes were identified based on PPI network analysis: ADCY7, C3AR1, DEGS1, CXCL1 in male-specific DEGs, TOLLIP, ORM1, ELANE, QPCT in female-specific DEGs and FCAR, CD3G, CLEC12A, MOSPD2, CD3D, ALDH3B1, GPR97, PLAUR in overlapping DEGs.<h4>Conclusion</h4>This discovery-driven study may be useful to provide a novel insight into the diagnosis and treatment of epilepsy. However, more experiments are needed in the future to study the functional roles of these genes in epilepsy.Yike ZhuDan HuangZhongyan ZhaoChuansen LuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0254326 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yike Zhu Dan Huang Zhongyan Zhao Chuansen Lu Bioinformatic analysis identifies potential key genes of epilepsy.
description	<h4>Background</h4>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.<h4>Methods</h4>In our study, we downloaded the Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE143272. Differentially expressed genes (DEGs) with a fold change (FC) >1.2 and a P-value <0.05 were identified by GEO2R and grouped in male, female and overlapping DEGs. Functional enrichment analysis and Protein-Protein Interaction (PPI) network analysis were performed.<h4>Results</h4>In total, 183 DEGs overlapped (77 ups and 106 downs), 302 DEGs (185 ups and 117 downs) in the male dataset, and 750 DEGs (464 ups and 286 downs) in the female dataset were obtained from the GSE143272 dataset. These DEGs were markedly enriched under various Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. 16 following hub genes were identified based on PPI network analysis: ADCY7, C3AR1, DEGS1, CXCL1 in male-specific DEGs, TOLLIP, ORM1, ELANE, QPCT in female-specific DEGs and FCAR, CD3G, CLEC12A, MOSPD2, CD3D, ALDH3B1, GPR97, PLAUR in overlapping DEGs.<h4>Conclusion</h4>This discovery-driven study may be useful to provide a novel insight into the diagnosis and treatment of epilepsy. However, more experiments are needed in the future to study the functional roles of these genes in epilepsy.
format	article
author	Yike Zhu Dan Huang Zhongyan Zhao Chuansen Lu
author_facet	Yike Zhu Dan Huang Zhongyan Zhao Chuansen Lu
author_sort	Yike Zhu
title	Bioinformatic analysis identifies potential key genes of epilepsy.
title_short	Bioinformatic analysis identifies potential key genes of epilepsy.
title_full	Bioinformatic analysis identifies potential key genes of epilepsy.
title_fullStr	Bioinformatic analysis identifies potential key genes of epilepsy.
title_full_unstemmed	Bioinformatic analysis identifies potential key genes of epilepsy.
title_sort	bioinformatic analysis identifies potential key genes of epilepsy.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f1d1989a55514306b41020e29353ec3d
work_keys_str_mv	AT yikezhu bioinformaticanalysisidentifiespotentialkeygenesofepilepsy AT danhuang bioinformaticanalysisidentifiespotentialkeygenesofepilepsy AT zhongyanzhao bioinformaticanalysisidentifiespotentialkeygenesofepilepsy AT chuansenlu bioinformaticanalysisidentifiespotentialkeygenesofepilepsy
_version_	1718374740716945408

Bioinformatic analysis identifies potential key genes of epilepsy.

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