Determining novel candidate anti-hepatocellular carcinoma drugs using interaction networks and molecular docking between drug targets and natural compounds of SiNiSan

A systematic study on the treatment of hepatitis B-related hepatocellular carcinoma with drugs based on bioinformatics and key target reverse network pharmacology and experimental verification

BACKGROUND

Chronic hepatitis B virus (HBV) infection is the major etiology of hepatocellular carcinoma (HCC). However, the mechanism of hepatitis B-related hepatocellular carcinoma (HBV-related HCC) is still unclear. Therefore, understanding the pathogenesis and searching for drugs to treat HBV-related HCC was an effective strategy to treat this disease.

PURPOSE

Bioinformatics was used to predict the potential targets of HBV-related HCC. The reverse network pharmacology of key targets was used to analyze the clinical drugs, traditional Chinese medicine (TCM) and small molecules of TCM in the treatment of HBV-related HCC.

METHODS

In this study, three microarray datasets totally containing 330 tumoral samples and 297 normal samples were selected from the GEO database. These microarray datasets were used to screen DEGs. And the expression profile and survival of 6 key genes were analyzed. In addition, Comparative Toxicogenomics Database and Coremine Medical database were used to enrich clinical drugs and TCM of HBV-related HCC by the 6 key targets. Then the obtained TCM were classified based on the Chinese Pharmacopoeia. Among these top 6 key genes, CDK1 and CCNB1 had the most connection nodes and the highest degree and were the most significantly expressed. In general, CDK1 and CCNB1 tend to form a complex, which is conducive to cell mitosis. Hence, this study mainly studied CDK1 and CCNB1. HERB database was used to predict small molecules TCM. The inhibition effect of quercetin, celastrol and cantharidin on HepG2.2.15 cells and Hep3B cells was verified by CCK8 experiment. The effects of quercetin, celastrol and cantharidin on CDK1 and CCNB1 of HepG2.2.15 cells and Hep3B cells were determined by Western Blot.

RESULTS

In short, 272 DEGs (53 upregulated and 219 downregulated) were identified. Among these DEGs, 6 key genes with high degree were identified, which were AURKA, BIRC5, CCNB1, CDK1, CDKN3 and TYMS. Kaplan-Meier plotter analysis showed that higher expression levels of AURKA, BIRC5, CCNB1, CDK1, CDKN3 and TYMS were associated with poor OS. According to the first 6 key targets, a variety of drugs and TCM were identified. These results showed that clinical drugs included targeted drugs, such as sorafenib, palbociclib and Dasatinib. and chemotherapy drugs, such as cisplatin and doxorubicin. TCM, such as the TCM flavor was mainly warm and bitter, and the main meridians were liver and lung. Small molecules of TCM included flavonoids, terpenoids, alkaloids and glycosides, such as quercetin, celastrol, cantharidin, hesperidin, silymarin, casticin, berberine and ursolic acid, which have great potential in anti-HBV-related HCC. For molecular docking of chemical components, the molecules with higher scores were flavonoids, alkaloids, etc. Three representative types of TCM small molecules were verified respectively, and it was found that quercetin, celastrol and cantharidin inhibited the proliferation of HepG2.2.15 cells and Hep3B cells along concentration gradient. Quercetin, celastrol and cantharidin decreased CDK1 expression in HepG2.2.15 and Hep3B cells, but for CCNB1, only cantharidin decreased CCNB1 expression in the two strains of cells.

CONCLUSION

In conclusion, AURKA, BIRC5, CCNB1, CDK1, CDKN3 and TYMS could be potential targets for the diagnosis and prognosis of HBV-related HCC. Clinical drugs include chemotherapeutic and targeted drug, traditional Chinese medicine is mainly bitter and warm TCM. Small molecular of TCM including flavonoids, terpenoids and glycosides and alkaloids, which have great potential in anti-HBV-related HCC. This study provides potential therapeutic targets and novel strategies for the treatment of HBV-related HCC.

Molecular docking and in vitro experiments verified that kaempferol induced apoptosis and inhibited human HepG2 cell proliferation by targeting BAX, CDK1, and JUN

Hepatocellular carcinoma, as a common liver cirrhosis complication, has become the sixth most common cancer worldwide, and its increasing incidence has resulted in considerable medical and economic burdens. As a natural polyphenolic compound, kaempferol has exhibits a wide range of antitumor activities against multiple cancer targets. In this study, the Autodock software was used for molecular docking to simulate the interaction process between kaempferol and HCC targets and the PyMOL software was used for visualization. Proliferation of kaempferol HepG2 cells under the effect of kaempferol was detected using Cell Counting Kit-8 (CCK-8) assay, and the apoptosis rate of HepG2 cells was detected using flow cytometry. The expressions of proteins BAX, CDK1, and JUN protein expressions were detected by Western blot. Molecular docking found that the kaempferol ligand has 3 rotatable bonds, 6 nonpolar hydrogen atoms, and 12 aromatic carbon atoms, and can form complexes with the kaempferol targets P53, BAX, AR, CDK1, and JUN through electrostatic energy. GO (Gene Ontology) enrichment analysis suggests that kaempferol regulates the biological function of hepatocellular carcinoma cells and is related to apoptosis. Cell Counting Kit-8 assay suggested that Kaempferol can significantly inhibited HepG2 cell proliferation, and the inhibition rate increased with the increase in drug concentration and incubation time. Moreover, kaempferol can promoted HepG2 cell apoptosis in a dose-dependent manner. This compound upregulated BAX and JUN expression and downregulated CDK1 expression. Thus, Kaempferol can promote HepG2 cell apoptosis, and the regulatory mechanism may be related to the regulation of the expression levels of the apoptosis-related proteins BAX, CDK1, and JUN.

Molecular and genetic markers in hepatocellular carcinoma: In silico analysis to clinical validation (current limitations and future promises)

Hepatocellular carcinoma (HCC) is the second cause of cancer-related mortality. The diagnosis of HCC depends mainly on α-fetoprotein, which is limited in its diagnostic and screening capabilities. There is an urgent need for a biomarker that detects early HCC to give the patients a chance for curative treatment. New targets of therapy could enhance survival and create future alternative curative methods. In silico analysis provides both; discovery of biomarkers, and understanding of the molecular pathways, to pave the way for treatment development. This review discusses the role of in silico analysis in the discovery of biomarkers, molecular pathways, and the role the author has contributed to this area of research. It also discusses future aspirations and current limitations. A literature review was conducted on the topic using various databases (PubMed, Science Direct, and Wiley Online Library), searching in various reviews, and editorials on the topic, with overviewing the author’s own published and unpublished work. This review discussed the steps of the validation process from in silico analysis to in vivo validation, to incorporation into clinical practice guidelines. In addition, reviewing the recent lines of research of bioinformatic studies related to HCC. In conclusion, the genetic, molecular and epigenetic markers discoveries are hot areas for HCC research. Bioinformatics will enhance our ability to accomplish this understanding in the near future. We face certain limitations that we need to overcome.