Introduction
Liver cancer is one of the malignant tumors that seriously threatens human health worldwide. Approximately 865,269 new cases are reported each year, accounting for 4.3% of all malignant tumors. About 757,948 people die from liver cancer annually, which accounts for 7.8% of all cancer-related deaths.1 Studies have shown that the active ingredients of wolfberry have a positive effect on anti-liver cancer, cervical cancer, gastric cancer, etc., and have become valuable natural compounds for the treatment or adjunctive treatment of these tumors.2–4 Wolfberry extracts can regulate pathophysiological processes such as inflammatory responses, lipid metabolism, liver fibrosis, and tumor occurrence and development through various signal transduction pathways, alleviating the symptoms of various liver diseases.5,6 Among these, Lycium barbarum polysaccharides can significantly prevent alcohol-induced hepatotoxicity through the antioxidant and anti-apoptotic activities of the Nrf2 signaling pathway in a dose-dependent manner.7 The use and application of molecular docking in drug discovery have changed significantly over the past few years. It enables the discovery of potential therapeutic drugs based on computer-structured approaches, the identification of novel therapeutic compounds, the prediction of ligand-target interactions at the molecular level, and the characterization of structure-activity relationships without prior knowledge of the chemical structures of other target modulators.8 In this study, based on the preliminary network pharmacological analysis of the active ingredients of Lycium barbarum in the treatment of liver cancer, molecular docking was conducted to verify the active ingredients’ role in the treatment of liver cancer, providing a theoretical basis for clinical liver cancer treatment.
Materials and methods
Active ingredient screening and preliminary treatment
The role of the active ingredients of Lycium barbarum in the treatment of liver cancer was discussed based on a previous network pharmacology study.9 The effective active ingredients of Lycium barbarum in the treatment of liver cancer and the intersection targets of liver cancer in various databases were obtained and displayed using a Venn diagram. The selected key targets (intersection targets) were uploaded to the STRING database (https://cn.string-db.org ), and the function “Multiple proteins” was selected to predict the protein interaction network. The species were limited to Homo sapiens, and the interaction confidence threshold was set to medium (≥0.400). Finally, the generated protein-protein interaction (PPI) network data were imported into Cytoscape 3.9.1 for visual analysis. Key targets were analyzed through gene ontology (GO) function annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis (P < 0.01), and the potential mechanism of Lycium barbarum against liver cancer was illustrated using a bubble chart/bar chart from the Weshengxin platform.
Molecular docking
The role of the active ingredients of Lycium barbarum in the treatment of liver cancer was verified by molecular docking. The core targets of the core prescription, ranked in the top seven based on degree value from high to low, were interlinked with the selected key active ingredients. The 3D structure of the active ingredients was downloaded from the TCMSP database and obtained from the PDB (http://www.rcsb.org/ ) database, and the structure of the core target protein was retrieved. PyMOL software was used for the initial treatment of the core target, such as hydrogenation, deletion of water molecules, and small molecule ligands. The three-dimensional structures of key active ingredients and core target proteins were converted into appropriate formats using AutoDock Tools and Open Babel. Molecular docking was verified using AutoDock Vin. The binding activity of key active ingredients and core targets was evaluated by binding energy, and the conformation with the most stable binding was selected. Finally, the results were processed on the PLIP website, and the molecular docking results were visualized using PyMOL software.
Results
Acquisition of effective active ingredients
Forty-five active ingredients with MOLID numbers were obtained through screening conditions “drug-like ≥0.18” and “oral bioavailability ≥30%”, among which the top 10 key active ingredients in the “Chinese Medicine-active ingredient-target-disease” PPI network were δ-carotene, lano-8-enol, lano-8-ene-3beta-ol, 14b-pregnane, β-sitosterol, carotenin, 24-ethylcholester-22-enol, 24-methylene cycloanthrane-3beta, 21-diol, canolasterol, and 24-methyllophenol (see Table 1).9 Their structures are shown in Figure 1. A total of 272 liver cancer intersection targets from each database was obtained, as shown in Figure 2. The top seven core targets in the PPI network include AKT1, TNF, EGFR, ESR1, SRC, PPARG, and HSP90AA1. The core targets were visualized and screened using Cytoscape, as shown in Figure 3. The KEGG enrichment results were sorted according to count value, and the top 10 signaling pathways for Lycium barbarum therapy for liver cancer were identified. A bubble diagram was drawn. As shown in Figure 4, the color of the bubble is closely correlated with the P-value: the bluer the bubble, the smaller the P-value, indicating a stronger correlation. The larger the value, the larger the bubble. As a result of GO enrichment, the first 10 counts of each item are ranked in a bar chart (Fig. 5). The counts in the chart are shown on the horizontal axis, and the names of pathways are shown on the vertical axis. The color of the bar chart is closely related to the P-value.
Table 1The top 10 active ingredients of wolfberry screened in this study
| Mol ID | Molecule name | OB (%) | DL |
|---|
| MOL010234 | delta-Carotene | 31.8 | 0.55 |
| MOL009678 | lanost-8-enol | 34.23 | 0.74 |
| MOL009677 | lanost-8-en-3beta-ol | 34.23 | 0.74 |
| MOL009604 | 14b-pregnane | 34.78 | 0.34 |
| MOL000358 | beta-sitosterol | 36.91 | 0.75 |
| MOL008173 | daucosterol | 36.91 | 0.75 |
| MOL009617 | 24-ethylcholest-22-enol | 37.09 | 0.75 |
| MOL009615 | 24-Methylenecycloartan-3beta,21-diol | 37.32 | 0.8 |
| MOL005438 | Campesterol | 37.58 | 0.71 |
| MOL009635 | 24-methyllophenol | 37.83 | 0.75 |
Molecular docking verification
The top 10 key active ingredients in the PPI network were docked with the top seven core targets, and the results are shown in Figure 6. The binding energies of the key active ingredients and core targets were all less than −5.0 kcal/mol (1 kcal = 4.184 J), and the binding energies of most results were less than −7.0 kcal/mol, which indicates that the key active ingredients and core targets have good binding ability, with most showing strong binding affinity, as shown in Table 2. These results suggest that most of the active ingredients in Lycium barbarum can spontaneously bind to the core target protein, thereby playing a therapeutic role in liver cancer. The results of the minimum binding energy were visualized, as shown in Figure 7, using PyMOL software for molecular docking. In the figure, the blue represents the small molecular ligand, and the gray represents the amino acid residues of the protein. The two are combined by hydrophobic forces, and the marked number indicates the distance between the carbon atoms of the force.
Table 2Results of the docking between key active ingredients and core target proteins
| Key active ingredient | AKT1
| EGFR
| ESR1
| HSP90AA1
| PPARG
| SRC
| TNF
|
|---|
| 1uNR | 3poz | 1xpc | 1byq | 3u9q | 1mfk | 4tsv |
|---|
| 14b-pregnane | −5.9 | −7.1 | −6.5 | −7.2 | −7.6 | −7.8 | −5.7 |
| 24-methylene cycloanthrane-3beta, 21-diol | −6.5 | −8.3 | −6.6 | −6.5 | −7.9 | −8.8 | −6.6 |
| 24-ethylcholester-22-enol | −6.9 | −8.5 | −7.2 | −7.5 | −7.5 | −8.8 | −6.3 |
| 24-methyllophenol | −6.8 | −9.3 | −6.4 | −7 | −7.6 | −8.8 | −6.5 |
| Beta-sitosterol | −6.6 | −8.5 | −7.1 | −8.4 | −8.5 | −8.7 | −6.6 |
| delta-carotene | −6.5 | −9.3 | −9 | −6.6 | −7.4 | −9.2 | −6.8 |
| Canola sterol | −7.1 | −8 | −6.5 | −8.6 | −6.9 | −8.3 | −5.7 |
| carotenin | −6.5 | −8 | −7.1 | −7.4 | −7.6 | −7.8 | −6.3 |
| lano-8-ene-3beta-alcohol | −6.3 | −8.6 | −7 | −8.3 | −6.9 | −8.8 | −6.6 |
| Lano-8-enol | −6.6 | −8.4 | −7 | −7.7 | −6.5 | −8.1 | −6.7 |
Discussion
The role of wolfberry active ingredients in the treatment of liver cancer was predicted based on previous network pharmacology studies.9 Key active components of Lycium berry were identified, including delta-carotene, lanost-8-enol, lanost-8-en-3beta-ol, 14b-pregnane, and beta-sitosterol, among others. It has been shown that some of these active ingredients have anticancer potential,10 with β-sitosterol alleviating oxidative stress and chronic liver injury induced by carbon tetrachloride in rats,11 and carotenoids demonstrating protective effects on hepatocytes and the liver.12
Core targets include AKT1, TNF, EGFR, ESR1, SRC, PPARG, and HSP90AA1, with AKT1 gene polymorphism potentially being closely related to the occurrence of primary liver cancer.13 In the early stages of liver cancer, TNF can promote tumor development by stimulating the proliferation of oval cells (liver stem cells), and TNF may also facilitate tumor metastasis by inducing epithelial-mesenchymal transition.14 EGFR is highly expressed in many tumors, and its positive expression is correlated with the pathological grade of serum alpha-fetoprotein (AFP)-negative hepatocellular carcinoma, affecting the occurrence, development, and prognosis of serum AFP-negative hepatocellular carcinoma to some extent.15 Furthermore, cell experiments have confirmed that the proliferation and invasion ability of liver cancer cells significantly increase, and apoptosis is weakened, following ESR1 knockdown, suggesting that ESR1 may be an effective potential target for liver cancer treatment.16 These intersection targets are likely to be key targets of Lycium barbarum in the treatment of liver cancer.
Limitations
Although network pharmacology and molecular docking research methods reveal the association between drug targets and diseases to a certain extent, they cannot accurately reflect the precise process by which drugs exert drugs in the body. In addition, network pharmacology and molecular docking studies rely on a large number of publicly available bioinformatics databases of various websites for analysis, some of which may have maintenance problems such as missing information or lagging updates, which may lead to bias in research results
Conclusions
This study confirms that Lycium barbarum contains multiple components that play a role in the treatment of liver cancer. It also interacts with multiple targets and pathways through molecular docking and a basic analysis of previous studies, suggesting that the Chinese medicine Lycium barbarum acts in the treatment of liver cancer through various components and pathways, providing new insights for the clinical treatment of liver cancer.
Declarations
Acknowledgement
We sincerely appreciate the data support provided by the TCMSP database, PDB database, and other databases during the research process, which facilitated the smooth progress of the study. These extensive data resources have laid a solid foundation for the writing of this paper.
Ethical statement
Not applicable.
Data sharing statement
After publication, all major data sheets will be available upon request.
Funding
This study is supported by the project of the Natural Science Foundation of Hebei Province (No: H2022209048).
Conflict of interest
The authors have no conflicts of interest to declare.
Authors’ contributions
Study conception and design (SL, ML), main data analysis, and manuscript draft (ML, JL, KY, QY, SL). All authors contributed to the article and approved the final version.