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Abstract Background As an anticancer Chinese herbal medicine, the effective components and mechanism of Actinidia chinensis Planch (ACP, Tengligen) in the treatment of colon cancer are still unclear. In the present study, the integration of network pharmacology, molecular docking, and cell experiments was employed to study the effective mechanism of ACP against colon cancer. Methods The Venn diagram and STRING database were used to construct the protein–protein interaction network (PPI) of ACP-colon cancer, and further topological analysis was used to obtain the key target genes of ACP in colon cancer. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to visualize the related functions and pathways. Molecular docking between key targets and compounds was determined using software such as AutoDockTools. Finally, the effect of ACP on CT26 cells was observed in vitro. Results The study identified 40 ACP-colon key targets, including CASP3, CDK2, GSK3B, and PIK3R1. GO and KEGG enrichment analyses found that these genes were involved in 211 biological processes and 92 pathways, among which pathways in cancer, PI3K-Akt, p53, and cell cycle might be the main pathways of ACP against colon cancer. Molecular docking verified that the key components of ACP could stably bind to the corresponding targets. The experimental results showed that ACP could inhibit proliferation, induce apoptosis, and downregulate the phosphorylation of PIK3R1, Akt, and GSK3B in CT26 cells. Conclusion ACP is an anti-colon cancer herb with multiple components, and involvement of multiple target genes and signaling pathways. ACP can significantly inhibit proliferation and induce apoptosis of colon cancer cells, which may be closely related to the regulation of PI3K/AKT/GSK3B signal transduction.

BackgroundSolanum nigrum L. (SNL) (Longkui) is a Chinese herb that can be used to treat colon cancer. The present study explored the components and mechanisms of SNL in treating colon cancer by using network pharmacology and molecular docking.MethodsThe components of SNL were collected from the TCMSP, ETCM, HERB, and NPASS databases. Meanwhile, the target proteins of these ingredients were collected/predicted by the TCMSP, SEA, SwissTargetPrediction, and the STITCH databases colon cancer-related target genes were identified from TCGA and GTEx databases. The interaction networks were established via Cytoscape 3.7.2. Gene Ontology and KEGG pathways were enriched by using the David 6.8 online tool. Finally, the binding of key components and targets was verified by molecular docking, and the cellular thermal shift assay (CETSA) was used to detect the efficiency of apigenin and kaempferol binding to the AURKB protein in CT26 cells.ResultsA total of 37 SNL components, 796 SNL targets, 5,356 colon cancer genes, and 241 shared targets of SNL and colon cancer were identified. A total of 43 key targets were obtained through topology analysis. These key targets are involved in multiple biological processes, such as signal transduction and response to drug and protein phosphorylation. At the same time, 104 signaling pathways, such as pathways in cancer, human cytomegalovirus infection, and PI3K-Akt signaling pathway, are also involved. The binding of the four key components (i.e., quercetin, apigenin, kaempferol, and luteolin) and the key targets was verified by molecular docking. The CETSA results showed that apigenin and kaempferol were able to bind to the AURKB protein to exert anti-CRC effects.ConclusionsQuercetin, apigenin, kaempferol, and luteolin are the main components of SNL in treating colon cancer. SNL regulates multiple bioprocesses via signaling pathways, such as pathways in cancer, PI3K-Akt, and cell cycle signaling pathways.

Abstract Background Shanzhuyu (the dried mature sarcocarp of Cornus officinalis Sieb. et Zucc., DMSCO) is a Chinese herb that can be used for the treatment of Alzheimer’s disease (AD), but its mechanism remains unknown. The present study aimed to investigate the active ingredients and effective mechanisms of DMSCO for the treatment of AD based on a network pharmacology approach. Methods The active components of DMSCO were collected from the TCMSP and ETCM databases and the target proteins of these compounds were predicted using TCMSP, SwissTargetPrediction and the STITCH database. The AD-related target proteins were identified from the OMIM, DisGeNet, GEO and GeneCards databases. The network interaction model of the compound-target-disease was established and was used to obtain the key targets of DMSCO on AD through network topology analysis. Subsequently, gene enrichment in Gene Ontology (GO) and KEGG pathways were conducted using the David 6.8 online tool. Results A total of 30 DMSCO effective compounds and 209 effective drug targets were obtained. A total of 172 AD-related genes and 37 shared targets of DMSCO and AD were identified. A total of 43 key targets for the treatment of AD were obtained from the topological analysis of the DMSCO-AD target network. These key targets were involved in a variety of biological processes, including amyloid deposition, apoptosis, autophagy, inflammatory response and oxidative stress and pathways, such as the PI3K-AKT, MAPK and TNF pathways. Three key compounds, namely ursolic acid, anethole and β-sitosterol were obtained from the analysis of the key targets. Conclusions Ursolic acid, anethole and β-sitosterol may be the main active components of DMSCO in the treatment of AD. DMSCO can treat AD by regulating amyloid deposition, apoptosis, autophagy, inflammatory response and oxidative stress via the PI3K-AKT, MAPK and other signaling pathways.