An advanced network pharmacology study to explore the novel molecular mechanism of Compound Kushen Injection for treating hepatocellular carcinoma by bioinformatics and experimental verification

Abstract

Background Compound Kushen Injection (CKI) is a Chinese patent drug that exerts curative effects in the clinical treatment of hepatocellular carcinoma (HCC). This study aimed to explore the targets and potential pharmacological mechanisms of CKI in the treatment of HCC. Methods In this study, network pharmacology was used in combination with molecular biology experiments to predict and verify the molecular mechanism of CKI in the treatment of HCC. The constituents of CKI were identified by UHPLC-MS/MS and literature search. The targets corresponding to these compounds and the targets related to HCC were collected based on public databases. To screen out the potential hub targets of CKI in the treatment of HCC, a compound-HCC target network was constructed. The underlying pharmacological mechanism was explored through the subsequent enrichment analysis. Interactive Gene Expression Profiling Analysis and Kaplan-Meier plotter were used to examine the expression and prognostic value of hub genes. Furthermore, the effects of CKI on HCC were verified through molecular docking simulations and cell experiments in vitro. Results Network analysis revealed that BCHE, SRD5A2, EPHX2, ADH1C, ADH1A and CDK1 were the key targets of CKI in the treatment of HCC. Among them, only CDK1 was highly expressed in HCC tissues, while the other 5 targets were lowly expressed. Furthermore, the six hub genes were all closely related to the prognosis of HCC patients in survival analysis. Molecular docking revealed that there was an efficient binding potential between the constituents of CKI and BCHE. Experiments in vitro proved that CKI inhibited the proliferation of HepG2 cells and up-regulated SRD5A2 and ADH1A, while down-regulated CDK1 and EPHX2. Conclusions This study revealed and verified the targets of CKI on HCC based on network pharmacology and experiments and provided a scientific reference for further mechanism research.

Multiple gene-drug prediction tool reveals Rosiglitazone based treatment pathway for non-segmental vitiligo

“…Similarly, the Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web-based tool containing more than 40,000 genes and 10,000 drug-passes, which helps to find drug-gene interactions by integrating and analyzing resources from papers, databases, and networks to uncover lists of compounds with gene expression patterns highly relevant to the phenotype of interest and to identify novel pathways or genes involved in related biological processes [ 22 , 23 ]. To date, these databases have been successfully used to identify promising compounds and combination therapies in several areas, such as bone and joint diseases [ 24 ], kidney disease [ 25 ], ovarian cancer [ 26 ], and liver cancer [ 27 ]. The DGIdb database integrates gene-drug interaction information from several databases, including DrugBank, PharmGKB, Chembl, Drug Target Commons, Therapeutic Target Database (TTD), and others.…”