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Article in Chinese | WPRIM | ID: wpr-927927

ABSTRACT

This study aims to explore the molecular mechanism of Ganoderma against gastric cancer based on network pharmacology, molecular docking, and cell experiment. The active components and targets of Ganoderma were retrieved from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP), and gastric cancer-related targets from GeneCards and Online Mendelian Inheritance in Man(OMIM). The protein-protein interaction(PPI) network of the common targets was constructed with STRING, followed by Gene Ontology(GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis of the common genes based on Bioconductor and R language. The medicinal-disease-component-target network and medicinal-disease-component-target-pathway network were established by Cytoscape. Molecular docking was performed between β-sitosterol(the key component in Ganoderma) and the top 15 targets in the PPI network. Cell experiment was performed to verify the findings. A total of 14 active components and 28 targets of Ganoderma were retrieved, and the medicinal and the disease shared 25 targets, including caspase-3(CASP3), caspase-8(CASP8), caspase-9(CASP9), and B-cell lymphoma-2(BCL2). The common targets involved 72 signaling pathways and apoptosis and p53 signaling pathway may play a crucial role in the effect of Ganoderma against gastric cancer. β-sitosterol had strong binding activity to the top 15 targets in the PPI network. The in vitro cell experiment demonstrated that β-sitosterol inhibited gastric cancer AGS cell proliferation by inducing cell apoptosis and cell cycle arrest in the S phase, which might be related to the regulation of the p53 pathway. This study shows the multi-component, multi-target, and multi-pathway characteristics of Ganoderma against gastric cancer, which lays a scientific basis for further research on the molecular mechanism.


Subject(s)
Humans , Ganoderma , Medicine, Chinese Traditional , Molecular Docking Simulation , Network Pharmacology , Stomach Neoplasms/genetics
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