BMC anesthesiology · 2023
Functional modular networks identify the pivotal genes associated with morphine addiction and potential drug therapies.
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СтатьяПоддержка исследования: вне правительства СШАОтозванная публикацияНазвание и аннотация: Перевод в очереди
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Jiang Yage, Wei Donglei, Xie Yubo
Аннотация
BACKGROUND: Chronic morphine usage induces lasting molecular and microcellular adaptations in distinct brain areas, resulting in addiction-related behavioural abnormalities, drug-seeking, and relapse. Nonetheless, the mechanisms of action of the genes responsible for morphine addiction have not been exhaustively studied.
METHODS: We obtained morphine addiction-related datasets from the Gene Expression Omnibus (GEO) database and screened for Differentially Expressed Genes (DEGs). Weighted Gene Co-expression Network Analysis (WGCNA) functional modularity constructs were analyzed for genes associated with clinical traits. Venn diagrams were filtered for intersecting common DEGs (CDEGs). Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for functional annotation. Protein-protein interaction network (PPI) and CytoHubba were used to screen for hub genes. Potential treatments for morphine addiction were figured out with the help of an online database.
RESULTS: Sixty-five common differential genes linked to morphine addiction were identified, and functional enrichment analysis showed that they were primarily involved in ion channel activity, protein transport, the oxytocin signalling pathway, neuroactive ligand-receptor interactions, and other signalling pathways. Based on the PPI network, ten hub genes (CHN2, OLIG2, UGT8A, CACNB2, TIMP3, FKBP5, ZBTB16, TSC22D3, ISL1, and SLC2A1) were checked. In the data set GSE7762, all of the Area Under Curve (AUC) values for the hub gene Receiver Operating Characteristic (ROC) curves were greater than 0.8. We also used the DGIdb database to look for eight small-molecule drugs that might be useful for treating morphine addiction.
CONCLUSIONS: The hub genes are crucial genes associated with morphine addiction in the mouse striatum. The oxytocin signalling pathway may play a vital role in developing morphine addiction.
METHODS: We obtained morphine addiction-related datasets from the Gene Expression Omnibus (GEO) database and screened for Differentially Expressed Genes (DEGs). Weighted Gene Co-expression Network Analysis (WGCNA) functional modularity constructs were analyzed for genes associated with clinical traits. Venn diagrams were filtered for intersecting common DEGs (CDEGs). Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for functional annotation. Protein-protein interaction network (PPI) and CytoHubba were used to screen for hub genes. Potential treatments for morphine addiction were figured out with the help of an online database.
RESULTS: Sixty-five common differential genes linked to morphine addiction were identified, and functional enrichment analysis showed that they were primarily involved in ion channel activity, protein transport, the oxytocin signalling pathway, neuroactive ligand-receptor interactions, and other signalling pathways. Based on the PPI network, ten hub genes (CHN2, OLIG2, UGT8A, CACNB2, TIMP3, FKBP5, ZBTB16, TSC22D3, ISL1, and SLC2A1) were checked. In the data set GSE7762, all of the Area Under Curve (AUC) values for the hub gene Receiver Operating Characteristic (ROC) curves were greater than 0.8. We also used the DGIdb database to look for eight small-molecule drugs that might be useful for treating morphine addiction.
CONCLUSIONS: The hub genes are crucial genes associated with morphine addiction in the mouse striatum. The oxytocin signalling pathway may play a vital role in developing morphine addiction.