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Exploring the Core Genes of Schizophrenia Based on Bioinformatics Analysis
Schizophrenia is a clinical syndrome composed of a group of symptoms involving many obstacles such as perception, thinking, emotion, behavior, and the disharmony of mental activities. Schizophrenia is one of the top ten causes of disability globally, accounting for about 1% of the global population....
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223283/ https://www.ncbi.nlm.nih.gov/pubmed/35741729 http://dx.doi.org/10.3390/genes13060967 |
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author | Feng, Shunkang Sun, Ping Qu, Chunhui Wu, Xiaohui Yang, Lu Yang, Tao Wang, Shuo Fang, Yiru Chen, Jun |
author_facet | Feng, Shunkang Sun, Ping Qu, Chunhui Wu, Xiaohui Yang, Lu Yang, Tao Wang, Shuo Fang, Yiru Chen, Jun |
author_sort | Feng, Shunkang |
collection | PubMed |
description | Schizophrenia is a clinical syndrome composed of a group of symptoms involving many obstacles such as perception, thinking, emotion, behavior, and the disharmony of mental activities. Schizophrenia is one of the top ten causes of disability globally, accounting for about 1% of the global population. Previous studies have shown that schizophrenia has solid genetic characteristics. However, the diagnosis of schizophrenia mainly depends on symptomatic manifestations, and no gene can be used as a clear diagnostic marker at present. This study explored the hub genes of schizophrenia by bioinformatics analysis. Three datasets were selected and downloaded from the GEO database (GSE53987, GSE21138, and GSE27383). GEO2R, NCBI’s online analysis tool, is used to screen out significant gene expression differences. The genes were functionally enriched by GO and KEGG enrichment analysis. On this basis, the hub genes were explored through Cytoscape software, and the immune infiltration analysis and diagnostic value of the screened hub genes were judged. Finally, four hub genes (NFKBIA, CDKN1A, BTG2, GADD45B) were screened. There was a significant correlation between two hub genes (NFKBIA, BTG2) and resting memory CD4 T cells. The ROC curve results showed that all four hub genes had diagnostic value. |
format | Online Article Text |
id | pubmed-9223283 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92232832022-06-24 Exploring the Core Genes of Schizophrenia Based on Bioinformatics Analysis Feng, Shunkang Sun, Ping Qu, Chunhui Wu, Xiaohui Yang, Lu Yang, Tao Wang, Shuo Fang, Yiru Chen, Jun Genes (Basel) Article Schizophrenia is a clinical syndrome composed of a group of symptoms involving many obstacles such as perception, thinking, emotion, behavior, and the disharmony of mental activities. Schizophrenia is one of the top ten causes of disability globally, accounting for about 1% of the global population. Previous studies have shown that schizophrenia has solid genetic characteristics. However, the diagnosis of schizophrenia mainly depends on symptomatic manifestations, and no gene can be used as a clear diagnostic marker at present. This study explored the hub genes of schizophrenia by bioinformatics analysis. Three datasets were selected and downloaded from the GEO database (GSE53987, GSE21138, and GSE27383). GEO2R, NCBI’s online analysis tool, is used to screen out significant gene expression differences. The genes were functionally enriched by GO and KEGG enrichment analysis. On this basis, the hub genes were explored through Cytoscape software, and the immune infiltration analysis and diagnostic value of the screened hub genes were judged. Finally, four hub genes (NFKBIA, CDKN1A, BTG2, GADD45B) were screened. There was a significant correlation between two hub genes (NFKBIA, BTG2) and resting memory CD4 T cells. The ROC curve results showed that all four hub genes had diagnostic value. MDPI 2022-05-27 /pmc/articles/PMC9223283/ /pubmed/35741729 http://dx.doi.org/10.3390/genes13060967 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Feng, Shunkang Sun, Ping Qu, Chunhui Wu, Xiaohui Yang, Lu Yang, Tao Wang, Shuo Fang, Yiru Chen, Jun Exploring the Core Genes of Schizophrenia Based on Bioinformatics Analysis |
title | Exploring the Core Genes of Schizophrenia Based on Bioinformatics Analysis |
title_full | Exploring the Core Genes of Schizophrenia Based on Bioinformatics Analysis |
title_fullStr | Exploring the Core Genes of Schizophrenia Based on Bioinformatics Analysis |
title_full_unstemmed | Exploring the Core Genes of Schizophrenia Based on Bioinformatics Analysis |
title_short | Exploring the Core Genes of Schizophrenia Based on Bioinformatics Analysis |
title_sort | exploring the core genes of schizophrenia based on bioinformatics analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223283/ https://www.ncbi.nlm.nih.gov/pubmed/35741729 http://dx.doi.org/10.3390/genes13060967 |
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