Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis
IntroductionCocaine 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.MethodsIn this study, we proposed a centrality algori...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-07-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1201897/full |
| _version_ | 1827908953900580864 |
|---|---|
| author | Xu Wang Shibin Sun Hongwei Chen Bei Yun Zihan Zhang Xiaoxi Wang Yifan Wu Junjie Lv Yuehan He Wan Li Lina Chen |
| author_facet | Xu Wang Shibin Sun Hongwei Chen Bei Yun Zihan Zhang Xiaoxi Wang Yifan Wu Junjie Lv Yuehan He Wan Li Lina Chen |
| author_sort | Xu Wang |
| collection | DOAJ |
| description | IntroductionCocaine 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.MethodsIn 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.ResultsFour 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.DiscussionThis study identified key genes and potential therapeutic drugs for cocaine addiction, which provided new ideas for the research of the mechanism of cocaine addiction. |
| first_indexed | 2024-03-13T01:30:36Z |
| format | Article |
| id | doaj.art-f39ff32f9ca54f6083fedd1cc165ce3e |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-13T01:30:36Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-f39ff32f9ca54f6083fedd1cc165ce3e2023-07-04T08:19:52ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-07-011710.3389/fnins.2023.12018971201897Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysisXu WangShibin SunHongwei ChenBei YunZihan ZhangXiaoxi WangYifan WuJunjie LvYuehan HeWan LiLina ChenIntroductionCocaine 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.MethodsIn 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.ResultsFour 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.DiscussionThis study identified key genes and potential therapeutic drugs for cocaine addiction, which provided new ideas for the research of the mechanism of cocaine addiction.https://www.frontiersin.org/articles/10.3389/fnins.2023.1201897/fullcocaine addictionkey genesPPI networkbiomarkercentrality algorithm |
| spellingShingle | Xu Wang Shibin Sun Hongwei Chen Bei Yun Zihan Zhang Xiaoxi Wang Yifan Wu Junjie Lv Yuehan He Wan Li Lina Chen Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis Frontiers in Neuroscience cocaine addiction key genes PPI network biomarker centrality algorithm |
| 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 | cocaine addiction key genes PPI network biomarker centrality algorithm |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1201897/full |
| work_keys_str_mv | AT xuwang identificationofkeygenesandtherapeuticdrugsforcocaineaddictionusingintegratedbioinformaticsanalysis AT shibinsun identificationofkeygenesandtherapeuticdrugsforcocaineaddictionusingintegratedbioinformaticsanalysis AT hongweichen identificationofkeygenesandtherapeuticdrugsforcocaineaddictionusingintegratedbioinformaticsanalysis AT beiyun identificationofkeygenesandtherapeuticdrugsforcocaineaddictionusingintegratedbioinformaticsanalysis AT zihanzhang identificationofkeygenesandtherapeuticdrugsforcocaineaddictionusingintegratedbioinformaticsanalysis AT xiaoxiwang identificationofkeygenesandtherapeuticdrugsforcocaineaddictionusingintegratedbioinformaticsanalysis AT yifanwu identificationofkeygenesandtherapeuticdrugsforcocaineaddictionusingintegratedbioinformaticsanalysis AT junjielv identificationofkeygenesandtherapeuticdrugsforcocaineaddictionusingintegratedbioinformaticsanalysis AT yuehanhe identificationofkeygenesandtherapeuticdrugsforcocaineaddictionusingintegratedbioinformaticsanalysis AT wanli identificationofkeygenesandtherapeuticdrugsforcocaineaddictionusingintegratedbioinformaticsanalysis AT linachen identificationofkeygenesandtherapeuticdrugsforcocaineaddictionusingintegratedbioinformaticsanalysis |