Network Pharmacology and Molecular Docking Validation to Reveal the Pharmacological Mechanisms of Kangai Injection against Colorectal Cancer.


Journal

BioMed research international
ISSN: 2314-6141
Titre abrégé: Biomed Res Int
Pays: United States
ID NLM: 101600173

Informations de publication

Date de publication:
2022
Historique:
received: 05 01 2022
revised: 26 04 2022
accepted: 29 07 2022
entrez: 1 9 2022
pubmed: 2 9 2022
medline: 8 9 2022
Statut: epublish

Résumé

Kangai injection is a traditional Chinese medicine (TCM) mixed by extracts from astragalus, ginseng, and kurorinone with modern technology. It is a commonly used antitumor injection in China, but the mechanism of Kangai injection in the treatment of colorectal cancer (CRC) is still unclear. The purpose of this study is to explore the mechanism of Kangai injection against CRC using network pharmacology and molecular docking technology. Targets of Kangai injection in CRC were predicted by SwissTargetPrediction and DisGeNET databases. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed by using the DAVID database. A component-disease-target gene-pathway network was constructed by Cytoscape 3.8.0 software. 114 overlapping targets of Kangai injection and CRC were used to construct a PPI network, and the top 10 hub targets of Kangai injection were rated from high to low as Network pharmacology intuitively showed the multicomponent, multiple targets, and multiple pathways of Kangai injection in the treatment of CRC. The molecular docking experiment verified that compounds of Kangai injection had good binding ability with top 10 hub target proteins as well.

Sections du résumé

Background UNASSIGNED
Kangai injection is a traditional Chinese medicine (TCM) mixed by extracts from astragalus, ginseng, and kurorinone with modern technology. It is a commonly used antitumor injection in China, but the mechanism of Kangai injection in the treatment of colorectal cancer (CRC) is still unclear. The purpose of this study is to explore the mechanism of Kangai injection against CRC using network pharmacology and molecular docking technology.
Methods UNASSIGNED
Targets of Kangai injection in CRC were predicted by SwissTargetPrediction and DisGeNET databases. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed by using the DAVID database. A component-disease-target gene-pathway network was constructed by Cytoscape 3.8.0 software.
Results UNASSIGNED
114 overlapping targets of Kangai injection and CRC were used to construct a PPI network, and the top 10 hub targets of Kangai injection were rated from high to low as
Conclusion UNASSIGNED
Network pharmacology intuitively showed the multicomponent, multiple targets, and multiple pathways of Kangai injection in the treatment of CRC. The molecular docking experiment verified that compounds of Kangai injection had good binding ability with top 10 hub target proteins as well.

Identifiants

pubmed: 36046463
doi: 10.1155/2022/3008842
pmc: PMC9420643
doi:

Substances chimiques

Drugs, Chinese Herbal 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

3008842

Informations de copyright

Copyright © 2022 Bo-Bo Zheng et al.

Déclaration de conflit d'intérêts

The authors have no conflicts of interest to declare.

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Auteurs

Bo-Bo Zheng (BB)

Department of General Surgery, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.

Quan Wang (Q)

Laboratory of Surgical Oncology, Peking University People's Hospital, Beijing, China.

Yumin Yue (Y)

Department of General Surgery, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.

Jiang Li (J)

National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Xiao-Jun Li (XJ)

Department of General Surgery, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.

Xin Wang (X)

Department of Gastroenterology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China.

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Classifications MeSH