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Utilizing network pharmacology and molecular docking to explore the underlying mechanism of Guizhi Fuling Wan in treating endometriosis

Authors: Wang, Haoxian; Zhou, Gang; Zhuang, Mingyan; Wang, Wei; Fu, Xianyun;

Utilizing network pharmacology and molecular docking to explore the underlying mechanism of Guizhi Fuling Wan in treating endometriosis

Abstract

Background Guizhi Fuling Wan (GZFLW) is a widely used classical Chinese herbal formulae prescribed for the treatment of endometriosis (EMs). This study aimed to predict the key targets and mechanisms of GZFLW in the treatment of EMs by network pharmacology and molecular docking. Methods Firstly, related compounds and targets of GZFLW were identified through the TCMSP, BATMAN-TCM and CASC database. Then, the EMs target database was built by GeneCards. The overlapping targets between GZFLW and EMs were screened out, and then data of the PPI network was obtained by the STRING Database to analyze the interrelationship of these targets. Furthermore, a topological analysis was performed to screen the hub targets. After that, molecular docking technology was used to confirm the binding degree of the main active compounds and hub targets. Finally, the DAVID database and Metascape database were used for GO and KEGG enrichment analysis. Results A total of 89 GZFLW compounds and 284 targets were collected. One hundred one matching targets were picked out as the correlative targets of GZFLW in treating EMs. Among these, 25 significant hub targets were recognized by the PPI network. Coincidently, molecular docking simulation indicated that the hub targets had a good bonding activity with most active compounds (69.71%). Furthermore, 116 items, including the inflammatory reaction, RNA polymerase, DNA transcription, growth factor activity, and steroid-binding, were selected by GO enrichment analysis. Moreover, the KEGG enrichment analysis results included 100 pathways focused on the AGE-RAGE pathway, HIF pathway, PI3K Akt pathway, MAPK pathway, and TP53 pathway, which exposed the potential mechanisms of GZFLW in treating EMs. Also, the MTT colorimetric assay indicated that the cell proliferation could be inhibited by GZFLW. Compared with the control group, the protein levels of P53, BAX, and caspase3 in the drug groups were all increased in Western blotting results. The results of flow cytometry showed that the percentage of apoptotic cells in the GZFLW group was significantly higher than that in the control group. Conclusion Through the exploration of network pharmacology and molecular docking technology, GZFLW has a therapeutic effect on EMs through multi-target mechanism. This study provided a good foundation for further experimental research.

Related Organizations
Keywords

QH301-705.5, Bioinformatics, Endometriosis, R, Natural product, Molecular docking, Medicine, Herbal medicine, Biology (General), Complementary medicine, Network pharmacology

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Top 10%
Average
Top 10%
Green
gold