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Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis
INTRODUCTION: Cocaine is a highly addictive drug that is abused due to its excitatory effect on the central nervous system. It is critical to reveal the mechanisms of cocaine addiction and identify key genes that play an important role in addiction. METHODS: In this study, we proposed a centrality a...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352680/ https://www.ncbi.nlm.nih.gov/pubmed/37469839 http://dx.doi.org/10.3389/fnins.2023.1201897 |
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author | Wang, Xu Sun, Shibin Chen, Hongwei Yun, Bei Zhang, Zihan Wang, Xiaoxi Wu, Yifan Lv, Junjie He, Yuehan Li, Wan Chen, Lina |
author_facet | Wang, Xu Sun, Shibin Chen, Hongwei Yun, Bei Zhang, Zihan Wang, Xiaoxi Wu, Yifan Lv, Junjie He, Yuehan Li, Wan Chen, Lina |
author_sort | Wang, Xu |
collection | PubMed |
description | INTRODUCTION: Cocaine is a highly addictive drug that is abused due to its excitatory effect on the central nervous system. It is critical to reveal the mechanisms of cocaine addiction and identify key genes that play an important role in addiction. METHODS: In this study, we proposed a centrality algorithm integration strategy to identify key genes in a protein–protein interaction (PPI) network constructed by deferential genes from cocaine addiction-related datasets. In order to investigate potential therapeutic drugs for cocaine addiction, a network of targeted relationships between nervous system drugs and key genes was established. RESULTS: Four key genes (JUN, FOS, EGR1, and IL6) were identified and well validated using CTD database correlation analysis, text mining, independent dataset analysis, and enrichment analysis methods, and they might serve as biomarkers of cocaine addiction. A total of seventeen drugs have been identified from the network of targeted relationships between nervous system drugs and key genes, of which five (disulfiram, cannabidiol, dextroamphetamine, diazepam, and melatonin) have been shown in the literature to play a role in the treatment of cocaine addiction. DISCUSSION: This study identified key genes and potential therapeutic drugs for cocaine addiction, which provided new ideas for the research of the mechanism of cocaine addiction. |
format | Online Article Text |
id | pubmed-10352680 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103526802023-07-19 Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis Wang, Xu Sun, Shibin Chen, Hongwei Yun, Bei Zhang, Zihan Wang, Xiaoxi Wu, Yifan Lv, Junjie He, Yuehan Li, Wan Chen, Lina Front Neurosci Neuroscience INTRODUCTION: Cocaine is a highly addictive drug that is abused due to its excitatory effect on the central nervous system. It is critical to reveal the mechanisms of cocaine addiction and identify key genes that play an important role in addiction. METHODS: In this study, we proposed a centrality algorithm integration strategy to identify key genes in a protein–protein interaction (PPI) network constructed by deferential genes from cocaine addiction-related datasets. In order to investigate potential therapeutic drugs for cocaine addiction, a network of targeted relationships between nervous system drugs and key genes was established. RESULTS: Four key genes (JUN, FOS, EGR1, and IL6) were identified and well validated using CTD database correlation analysis, text mining, independent dataset analysis, and enrichment analysis methods, and they might serve as biomarkers of cocaine addiction. A total of seventeen drugs have been identified from the network of targeted relationships between nervous system drugs and key genes, of which five (disulfiram, cannabidiol, dextroamphetamine, diazepam, and melatonin) have been shown in the literature to play a role in the treatment of cocaine addiction. DISCUSSION: This study identified key genes and potential therapeutic drugs for cocaine addiction, which provided new ideas for the research of the mechanism of cocaine addiction. Frontiers Media S.A. 2023-07-04 /pmc/articles/PMC10352680/ /pubmed/37469839 http://dx.doi.org/10.3389/fnins.2023.1201897 Text en Copyright © 2023 Wang, Sun, Chen, Yun, Zhang, Wang, Wu, Lv, He, Li and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Wang, Xu Sun, Shibin Chen, Hongwei Yun, Bei Zhang, Zihan Wang, Xiaoxi Wu, Yifan Lv, Junjie He, Yuehan Li, Wan Chen, Lina Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis |
title | Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis |
title_full | Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis |
title_fullStr | Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis |
title_full_unstemmed | Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis |
title_short | Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis |
title_sort | identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352680/ https://www.ncbi.nlm.nih.gov/pubmed/37469839 http://dx.doi.org/10.3389/fnins.2023.1201897 |
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