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Aberrant expression of BDNF might serve as a candidate target for cocaine-induced psychosis: insights from bioinformatics analysis and microarray validation

BACKGROUND: Cocaine use disorder (CUD) and associated psychosis are major public health issues worldwide, along with high relapse outcome and limited treatment options. Exploring the molecular mechanisms underlying cocaine-induced psychosis (CIP) could supply integrated insights for understanding th...

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Detalles Bibliográficos
Autores principales: Zhu, Youwei, Zhao, Yan, Xu, Xiaomin, Su, Hang, Li, Xiaotong, Zhong, Na, Jiang, Haifeng, Du, Jiang, Zhao, Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8506846/
https://www.ncbi.nlm.nih.gov/pubmed/34723091
http://dx.doi.org/10.1136/gpsych-2021-100587
Descripción
Sumario:BACKGROUND: Cocaine use disorder (CUD) and associated psychosis are major public health issues worldwide, along with high relapse outcome and limited treatment options. Exploring the molecular mechanisms underlying cocaine-induced psychosis (CIP) could supply integrated insights for understanding the pathogenic mechanism and potential novel therapeutic targets. AIMS: The aim of the study was to explore common alterations of CUD-schizophrenia-target genes and identify core risk genes contributing to CIP through data mining and network pharmacology approach. METHODS: Target genes of CUD were obtained from GeneCards, Comparative Toxicogenomics Database, Swiss Target Prediction platform and PubChem. Schizophrenia-related target genes were derived from DisGeNET, GeneCards, MalaCards and Online Mendelian Inheritance in Man databases. Then, the overlap genes of these two sets were regarded as risk genes contributing to CIP. Based on these CUD-schizophrenia-target genes, functional annotation and pathway analysis were performed using the clusterProfiler package in R. Protein–protein interaction network construction and module detection were performed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software. Gene expression datasets GSE54839 and GSE93577 were applied for data validation and diagnostic capacity evaluation of interested hub genes. RESULTS: A total of 165 CUD-schizophrenia-target genes were obtained. These genes were mainly contributing to chemical synaptic transmission, neuropeptide hormone activity, postsynaptic membrane and neuroactive ligand–receptor interaction pathway. Network analysis and validation analysis indicated that BDNF might serve as an important risk gene in mediating CIP. CONCLUSIONS: This study generates a holistic view of CIP and provides a basis for the identification of potential CUD-schizophrenia-target genes involved in the development of CIP. The abnormal expression of BDNF would be a candidate therapeutic target underlying the pathogenesis of CUD and associated CIP.