AUTHOR=Wang Xu , Sun Shibin , Chen Hongwei , Yun Bei , Zhang Zihan , Wang Xiaoxi , Wu Yifan , Lv Junjie , He Yuehan , Li Wan , Chen Lina 

TITLE=Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1201897

DOI=10.3389/fnins.2023.1201897

ISSN=1662-453X

ABSTRACT=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. In this paper, we proposed a centrality algorithm integration strategy and identified four key genes (JUN, FOS, EGR1 and IL6) in a protein-protein interaction (PPI) network constructed by differential genes from cocaine addiction-related datasets. Four key genes have been 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. In order to investigate potential therapeutic drugs for cocaine addiction, a network of targeted relationships between nervous system drugs and key genes was established. A total of seventeen drugs have been identified, 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. In conclusion, 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.