Unknown

Dataset Information

0

Mechanism Prediction of Astragalus membranaceus against Cisplatin-Induced Kidney Damage by Network Pharmacology and Molecular Docking.


ABSTRACT:

Background

Cisplatin is a frequently used and effective chemotherapy drug in clinical practice, but severe side effects limit its use, among which nephrotoxicity is considered the most serious and prolonged damage to the body. Astragalus membranaceus (AM) is a well-known herbal medicine, and modern pharmacological studies have confirmed its antioxidant, immunomodulatory, and antiapoptotic effects. Clinical studies have shown that AM and its active components can attenuate cisplatin-induced kidney damage, but the molecular mechanism has not been fully expounded.

Materials and methods

First, the components and targets information of AM were collected from the TCMSP, and the relevant targets of cisplatin-induced kidney damage were accessed from the GeneCards and OMIM databases. Then, the core targets were selected by the Venn diagram and network topology analysis, which was followed by GO and KEGG pathway enrichment analysis. Finally, we construct a component-target-pathway network. Furthermore, molecular docking was carried out to identify the binding activity between active components and key targets.

Results

A total of 20 active components and 200 targets of AM and 646 targets related to cisplatin-induced kidney damage were obtained. 91 intersection targets were found between AM and cisplatin-induced kidney damage. Then, 16 core targets were identified, such as MAPK1, TNF-α, and p53. Furthermore, GO and KEGG pathway enrichment analysis suggested that MAPK, Toll-like receptor, and PI3K-Akt signaling pathways may be of significance in the treatment of cisplatin-induced kidney damage by AM. Molecular docking indicated that quercetin and kaempferol had high binding affinities with many core targets.

Conclusion

In summary, the active components, key targets, and signaling pathways of AM in the treatment of cisplatin-induced kidney damage were predicted in this study, which contributed to the development and application of AM.

SUBMITTER: Jia C 

PROVIDER: S-EPMC8390139 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC10671347 | biostudies-literature
| S-EPMC10470703 | biostudies-literature
| S-EPMC7079250 | biostudies-literature
| S-EPMC8522094 | biostudies-literature
| S-EPMC7652614 | biostudies-literature
| S-EPMC8820867 | biostudies-literature
| S-EPMC7340787 | biostudies-literature
| S-EPMC9499798 | biostudies-literature
| S-EPMC8812605 | biostudies-literature
| S-EPMC9239660 | biostudies-literature