Mechanisms of Actinidia chinensis Planch in treating colon cancer based on the integration of network pharmacology, molecular docking, and experimental verification.

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.

Exploring the components and mechanism of Solanum nigrum L. for colon cancer treatment based on network pharmacology and molecular docking.

Background: Solanum 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. Methods: The 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. Results: A 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. Conclusions: Quercetin, 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.

Acteoside and ursolic acid synergistically protects H2O2-induced neurotrosis by regulation of AKT/mTOR signalling: from network pharmacology to experimental validation.

CONTEXT: Ursolic acid (UA) and acteoside (ATS) are important active components that have been used to treat Alzheimer's disease (AD) because of their neuroprotective effects, but the exact mechanism is still unclear. OBJECTIVE: Network pharmacology was used to explore the mechanism of UA + ATS in treating AD, and cell experiments were used to verify the mechanism. MATERIALS AND METHODS: UA + ATS targets and AD-related genes were retrieved from TCMSP, STITCH, SwissTargetPrediction, GeneCards, DisGeNET and GEO. Key targets were obtained by constructing protein interaction network through STRING. The neuroprotective effects of UA + ATS were verified in H2O2-treated PC12 cells. The subsequent experiments were divided into Normal, Model (H2O2 pre-treatment for 4 h), Control (H2O2+ solvent pre-treatment), UA (5 µM), ATS (40 µM), UA (5 µM) + ATS (40 µM). Then apoptosis, mitochondrial membrane potential, caspase-3 activity, ATG5, Beclin-1 protein expression and Akt, mTOR phosphorylation levels were detected. RESULTS: The key targets of UA + ATS-AD network were mainly enriched in Akt/mTOR pathway. Cell experiments showed that UA (ED50: 5 µM) + ATS (ED50: 40 µM) could protect H2O2-induced (IC50: 250 µM) nerve damage by enhancing cells viability, combating apoptosis, restoring MMP, reducing the activation of caspase-3, lessening the phosphorylation of Akt and mTOR, and increasing the expression of ATG5 and Beclin-1. CONCLUSIONS: ATS and UA regulates multiple targets, bioprocesses and signal pathways against AD pathogenesis. ATS and UA synergistically protects H2O2-induced neurotrosis by regulation of AKT/mTOR signalling.

Pyroptosis-related prognosis model, immunocyte infiltration characterization, and competing endogenous RNA network of glioblastoma.

BACKGROUND: Glioblastoma (GBM) has a high incidence rate, invasive growth, and easy recurrence, and the current therapeutic effect is less than satisfying. Pyroptosis plays an important role in morbidity and progress of GBM. Meanwhile, the tumor microenvironment (TME) is involved in the progress and treatment tolerance of GBM. In the present study, we analyzed prognosis model, immunocyte infiltration characterization, and competing endogenous RNA (ceRNA) network of GBM on the basis of pyroptosis-related genes (PRGs). METHODS: The transcriptome and clinical data of 155 patients with GBM and 120 normal subjects were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). Lasso (Least absolute shrinkage and selection operator) Cox expression analysis was used in predicting prognostic markers, and its predictive ability was tested using a nomogram. A prognostic risk score formula was constructed, and CIBERSORT, ssGSEA algorithm, Tumor IMmune Estimation Resource (TIMER), and TISIDB database were used in evaluating the immunocyte infiltration characterization and tumor immune response of differential risk samples. A ceRNA network was constructed with Starbase, mirtarbase, and lncbase, and the mechanism of this regulatory axis was explored using Gene Set Enrichment Analysis (GSEA). RESULTS: Five PRGs (CASP3, NLRP2, TP63, GZMB, and CASP9) were identified as the independent prognostic biomarkers of GBM. Prognostic risk score formula analysis showed that the low-risk group had obvious survival advantage compared with the high-risk group, and significant differences in immunocyte infiltration and immune related function score were found. In addition, a ceRNA network of messenger RNA (CASP3, TP63)-microRNA (hsa-miR-519c-5p)-long noncoding RNA (GABPB1-AS1) was established. GSEA analysis showed that the regulatory axis played a considerable role in the extracellular matrix (ECM) and immune inflammatory response. CONCLUSIONS: Pyroptosis and TME-related independent prognostic markers were screened in this study, and a prognosis risk score formula was established for the first time according to the prognosis PRGs. TME immunocyte infiltration characterization and immune response were assessed using ssGSEA, CIBERSORT algorithm, TIMER, and TISIDB database. Besides a ceRNA network was built up. This study not only laid foundations for further exploring pyroptosis and TME in improving prognosis of GBM, but also provided a new idea for more effective guidance on clinical immunotherapy to patients and developing new immunotherapeutic drugs.