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• W3039774489 abstract "Abstract Astragalus membranaceus (HUANG QI, HQ) is a kind of traditional Chinese medicine. Researchers have widely concerned its antitumor effect. At present, there is still a lack of research on the treatment of laryngeal cancer with HQ. In this study, we integrated data from the weighted gene co-expression network of laryngeal cancer samples and the components and targets of HQ. A new method for dividing PPI network modules is proposed. Important targets of HQ treatment for laryngeal cancer were obtained through the screening of critical modules. These nodes performed differential expression analysis and survival analysis through external data sets. GSEA enrichment analysis reveals pathways for important targets participation. Finally, molecular docking screened active ingredients in HQ that could interact with important targets. Combined with the laryngeal cancer gene co expression network and HQ PPI network, we obtained the critical module related to laryngeal cancer. Among them, MMP1, MMP3, and MMP10 were chosen as important targets. External data sets demonstrate that their expression in tumor samples is significantly higher than in normal samples. The survival time of patients with high expression group was significantly shortened, which is a negative factor for prognosis. GSEA enrichment analysis found that they are mainly involved in tumor-related pathways such as ECM receptor interaction and Small cell lung cancer. The docking results show that the components that can well bind to important targets of HQ are quercetin, rutin, and Chlorogenic acid, which may be the primary mechanism of the anti-cancer effect of HQ. These findings provide a preliminary research basis for Chinese medicine treatment of laryngeal cancer and offer ideas to related drug design." @default.
• W3039774489 created "2020-07-10" @default.
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• W3039774489 date "2020-07-07" @default.
• W3039774489 modified "2023-10-05" @default.
• W3039774489 title "Mechanism of Astragalus membranaceus in the treatment of laryngeal cancer based on gene co-expression network and molecular docking" @default.
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• W3039774489 doi "https://doi.org/10.1038/s41598-020-68093-0" @default.
• W3039774489 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7340787" @default.
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• W3039774489 hasPublicationYear "2020" @default.
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