Zi-Hong Wu, Zi-Ming Wang, En-Feng Song, Bei Yin

1Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610072, China.2Department of Traditional Chinese Medicine, Renmin Hospital of Wuhan University, Wuhan 430060, China.3The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou 510504, China.

Abstract Objective: To explore the mechanism of anticancer prescription in treatment of gastric cancer based on network pharmacology and molecular docking.Methods: By searching TCMSP database, the active components and corresponding targets of anticancer prescriptions were screened out.GeneCards, PharmGkb, OMIM, DrugBank and TTD database were used to collect action targets of gastric cancer.And Venny 2.1 software was used to screen drug-disease co-action targets.Then, String and Cytoscape software were used to analyze and construct PPI network, and Cytonca plug-in was used to carry out topology analysis to select the core targets.ClueGO plugin was used for GO function enrichment analysis and KEGG pathway analysis.Finally, the AutoDock software was used to conduct molecular docking between the core target and the main active ingredients of the anticancer prescription.Results: Sixty-four active compounds, 159 common targets and 12 core targets of anti-cancer prescriptions were screened out, which involved 2373 GO functions and 172 KEGG pathways.Finally, the core target proteins MAPK1 TP53 and JUN were screened and molecularly docked with 8 major active components.Among them, the flavonoid quercetin and luteolin had the best binding activity with MAPK1, Quercetin baicalin also had high binding activity with FOS.Conclusion: The preliminary study showed that flavonoids were an important active ingredient in the anti-cancer prescription, which mainly treated gastric cancer through multiple targets and multiple pathways, such as the effect of MAPK1 on chemical carcinogenesis in reaction with drugs, bacterial and viral infection and cell apoptosis.

Keywords: network pharmacology; molecular docking; anticancer prescription; gastric cancer

Background

Gastric cancer is the fifth most common malignant tumor in the world.due to the low rate of early diagnosis, most patients with advanced gastric cancer have poor prognosis and high mortality, ranking the third among the causes of cancer death [1].At present, surgical resection is still the first-line treatment for gastric cancer, but even complete resection is very easy to have recurrence and metastasis.Therefore, systemic chemotherapy, radiotherapy and chemotherapy, targeted therapy, immunotherapy, intraperitoneal hyperthermic perfusion therapy and traditional Chinese medicine have formed the current multidisciplinary methods for the treatment of gastric cancer [2].In recent years, the application of traditional Chinese medicine in the prevention and treatment of tumor has achieved remarkable curative effect in clinic.The Anticancer prescription comes from the tutor's commonly used clinical experience prescription, which is composed of Hedyotis diffusa Willd (HDW), Scutellaria barbata (SB), Poria cocos (PC), Seaweed (Se), Prunella vulgaris (PV) and Coix seed (CS), it has the effects of clearing away heat and detoxification, resolving phlegm and dispersing knot, and achieving good results in clinical application, but the research on its molecular mechanism is not perfect.

Due to the complex chemical composition of traditional Chinese medicine, there is a lack of effective research methods to clarify its molecular mechanism.In recent years, the method of network pharmacology is more and more applied to the research of traditional Chinese medicine compound.The interaction between bioactive compound and target is found by constructing network, and then the key nodes are found and verified by network analysis and network verification.It is considered to be a scientific method suitable for the research of traditional Chinese medicine prescription [3].This paper intends to use the method of network pharmacology to screen the effective active components and targets of Anticancer prescription, analyze its key targets and signal pathways in the treatment of gastric cancer, and explain its mechanism of anti-gastric cancer from the molecular level.

Materials and methods

The database and website involved in this article are shown in Table 1

Table 1 Database and website

Screening the active components and targets of Anticancer Formula

According to the pharmacokinetic parameters provided by SwissADME [4], oral bioavailability (OB) ≥ 30% and drug-like drugs (DL) ≥ 0.18 were selected as screening conditions, and the active compounds in anticancer prescriptions were collected through TCMSP database.Then the target protein corresponding to the active compound is screened, and the name of the target protein is converted into the corresponding gene symbol, namely "genesymbol", by using UniprotKB database.

Collect the related targets of gastric cancer and predict the common target of anticancer prescription-gastric cancer

With "gastric cancer" as the key word, gastric cancer-related targets were searched through five databases of GeneCards, PharmGkb, OMIM, DrugBank and TTD, and all gastric cancer-related targets were obtained after removing repeated targets.Using R language and Venny2.1 software to intersect the gastric cancer related target with the anticancer drug target to obtain the anticancer prescription-gastric cancer common target.

Construction of “anticancer prescription-active ingredient-target-gastric cancer” network

The common targets of anticancer prescription and gastric cancer are sorted out, and the attribute identification of node data is mainly using Perl program.The network diagram of "anticancer prescription-active ingredient-target-gastric cancer" was drawn by Cytoscape3.8.2 software to predict the main targets and compound components of anticancer prescription in the treatment of gastric cancer.

Construction and analysis of protein-protein interaction (PPI) network

Selecting “Homo sapiens” and set the minimum interaction requirement score (minimum required interaction score) ＞ 0.9.input the above common targets into the STRING database in the form of "gene symbol" for PPI analysis, obtain the PPI network diagram and save it in tsv format.Import the obtained tsv file into Cytoscape3.8.2 software to analyze the topological properties of the common target.The central specificity of nodes in the network is mainly evaluated from four quantitative values: DC (degree centrality), CC (closeness centrality), BC (betweenness centrality) and EC (eigenvector centrality).The importance of nodes in the network is positively correlated with their quantitative values [5].

Functional analysis of GO and enrichment analysis of KEGG signal pathway

The core target of anti-cancer prescription of ClueGO plug-in was used for gene ontology (Gene Ontology, GO) function enrichment analysis and Kyoto gene and genome encyclopedia (Kyoto Encyclopedia of Genes and Genomes, KEGG) pathway analysis.The function of GO is divided into three parts: BP (biological process), CC (cellular component) and MF (Molecular Function) [6].After screening withP＜ 0.05, the biological process and related signal pathways involved in the treatment of gastric cancer were analyzed.Then the relevant signal pathways are selected according to the results of KEGG enrichment analysis, and the Pathview plug-in is used to draw the road map.

Molecular docking

The 2D structure of the s mall molecular ligand of the core target is searched and downloaded from the PubChem database.The 2D structure is converted into 3D structure by ChemOffice software, optimized and saved as a mol2 file, then import the mol2 file into AutoDockTools (1.5.6) software and save it as a ligand file in PDBQT format.In addition, the PDB file of the 3D structure of the core target protein was retrieved and downloaded from the PDB database.The water molecules and small molecular ligands were removed by Pymol (3.2.2) software, and then hydrogen was added and charged by AutoDockTools software, which was saved as a receptor file in PDBQT format.Then, the active pocket of the docking receptor was determined by AutoGrid tool, in which "num_modes = 20 and energy_range = 5" were used as parameters, molecular docking was carried out with the help of AutoDock Vina (1.1.2) software, and finally, Pymol (3.2.2) software was used for analysis and mapping.At the same time, Xeloda, a commonly used anti-gastric cancer chemotherapy drug, was selected as a positive control for analysis and verification.

Results

Screening results of active components and targets of Anticancer Formula

All the compounds in anticancer prescriptions were collected from TCMSP database.According to OB and DL values, the compounds of HDW, SB, PC, Se, PV and CS were 7, 29, 15, 4, 11 and 9, respectively.After the integration of repetitive genes, a total of 64 compounds were collected (Table 2).

Table 2 Effective compounds in anticancer prescription (continued)

Table 2 Effective compounds in anticancer prescription

Then the corresponding targets of drug active components were screened from TCMSP database, and the corresponding targets of HDW, SB, PC, Se, PV and CS were 556, 121, 121, 262, 803, 246 respectively.A total of 1445 human target proteins were obtained after removing repeated targets by software.The active ingredient-target network is drawn by using Cytoscape3.8.2 software, and the "active ingredient-target" network diagram of anticancer prescription is obtained (Figure 1).The network consists of 248 nodes (representing compounds and corresponding targets) and 616 edges (representing the interaction between compounds and target proteins).

Figure 1 Active ingredient-target network diagram of anticancer prescription

Prediction of drug-disease common targets and construction of network

A total of 12705 disease targets related to gastric cancer were obtained by searching the databases of GeneCards, PharmGkb, OMIM, DrugBank and TTD (Figure 2).Then the targets of anticancer prescription and gastric cancer were intersected by R language program and Venny2.1 software.finally, 159 drug-disease common targets were selected (Figure 3).Finally, the common targets selected by Cytoscape3.8.2 software are used to construct the network diagram of "Anticancer prescription-active ingredient-target-gastric cancer" (Figure 4).

Construction of core target PPI network

The common targets were imported into the STRING database, and the unrelated targets were removed to obtain the PPI network of anticancer targets (Figure 5).The obtained PPI network diagram is saved as a tsv file and imported into Cytoscape 3.8.2 software.The median values of BC (61.63), DC (6), CC (0.14) and EC (0.03) of network nodes are calculated by CytoNCA tool.Finally, 12 core targets are obtained, and the PPI network diagram of core targets is constructed (Figure 6).The average degree value of the target in the network is 7.83.the central nodes are MAPK1 (degree = 10), TP53 (degree = 10), JUN (degree = 10), CCND1 (degree = 9), ESR1 (degree = 9) and FOS (degree = 9).The results of PPI network analysis show that these targets play a very important role in the molecular mechanism of anticancer prescription in the treatment of gastric cancer.

Functional enrichment analysis of GO

The ClueGO plug-in was used to analyze the GO functional enrichment of the 12 core targets from three aspects of BP, CC and MF.The top 10 enriched gene entries are shown in Figure 7.The ordinate is the name of GO, and the Abscissa is the number of genes enriched on each GO.The color represents the significance of enrichment.The redder the color is, the more significant the enrichment of genes in the network is on the GO.A total of 2103 items were obtained by BP analysis, mainly related to drug reaction, lipopolysaccharide, bacterial molecule response, cell response to chemical stress and response to metal ions.A total of 72 items were obtained by CC analysis, mainly involving membrane raft, membrane microdomain, fossa, plasma membrane raft, outer membrane of organelle and so on.A total of 198 items were obtained by MF analysis, mainly related to DNA binding transcription factor binding, G protein coupled amine receptor activity, nuclear receptor activity, ligand activated transcription factor activity and RNA polymerase II specific DNA binding transcription factor binding.

Enrichment analysis of KEGG signal pathway

ClueGO insertion was used to analyze the enrichment of KEGG pathways.172 signal pathways were selected according to the criteria ofP＜ 0.05.the top 30 pathways were shown in Figure 8.The ordinate is the name of the pathway, the Abscissa is the proportion of genes, the size of the circle represents the number of genes enriched in each pathway, the color represents the significance of enrichment, the redder the color, the more significant the enrichment of genes in the network.Pathways related to gastric cancer include lipid and atherosclerosis, chemical carcinogenesis-receptor activation pathway, fluid shear stress and atherosclerosis, Kaposi's sarcoma-associated herpesvirus infection, human cytomegalovirus infection, and advanced glycation end product-receptor for advanced glycation end product (AGE-RAGE) signal pathways in diabetic complications, and tumor necrosis factor (TNF) signal pathway, etc.According to the results of KEGG enrichment analysis, the "Gastriccancer" signal pathway is selected, and the Pathview plug-in is used to draw the path map (Figure 9).The red node indicates that the gene exists in the regulatory network.

Molecular docking verification of active components and core targets of Anticancer Formula

The top 6 targets in PPI network were docked with 10 active components of Xeloda and anticancer prescription respectively (Table 3).The molecular docking was not performed because the chemical formula and fractionator structure were not found in M12250 (7-methoxymurine-8-methoxymurine-8-methoxymurine-2-Methoxyan thraquinone) and M1670 (2-methoxyanthraquinone-3-methylanthraq- uinone).The affinity is used to judge the interaction strength between small molecular ligands and target proteins.Binding energy ＜ 0 indicates that ligands and receptor molecules can bind freely, while binding energy ＜ –5.0kcal/moL indicates better binding activity, and the higher the absolute value of Affinity, the stronger the docking activity [7, 8].The results showed that among the first five pairs of ligands and receptors with the best binding activity, CID 5280343 had the best binding activity with FOS (affinity = –10.2 kcal/mol), followed by CID 5281605 and FOS (affinity = –9.6 kcal/mol), CID5280445 and MAPK1 (affinity = –9.1 kcal/mol), CID5280445 and JUN (affinity = –9.0 kcal/mol), CID 5280343 and MAPK1 (affinity = –8.9 kcal/mol).The molecular docking map with the best docking result for each target protein is shown in Figure 10.

Figure 10 Molecular docking simulation diagram with the best docking result of each target protein

Table 3 The Affinity of active components of antineoplastic prescription docking with target protein molecules

Figure 2 Wayne diagram of gastric cancer related genes

Figure 3 Wayne diagram of anticancer

Figure 4 Network diagram of “Anticancer prescription-active ingredient-Target-gastric cancer”

Figure 5 PPI network diagram of targets of anticancer prescription

Figure 6 Core target of anticancer prescription

Figure 7 Histogram of functional enrichment analysis of GO, the core target of anticancer prescription in the treatment of gastric cancer

Figure 8 Bubble map of KEGG pathway enrichment analysis, the core target of anticancer prescription in the treatment of gastric cancer

Figure 9 Gastric cancer signal path diagram

Discussion

Epidemiological studies have shown that with the change of human life style, the incidence of gastric cancer in young people is increasing year by year [9].Although the comprehensive treatment of gastric cancer has made some progress in recent years, most of the patients with gastric cancer are in the middle and advanced stage, and the treatment is limited, so the 5-year overall survival rate of patients with gastric cancer in China is less than 30% [10].In recent years, more and more scholars are interested in studying the molecular mechanism of anti-tumor of traditional Chinese medicine.According to traditional Chinese medicine, the core pathogenesis of gastric cancer is positive deficiency and evil excess, that is, positive deficiency, phlegm coagulation and toxin accumulation, which coincides with the theory of tumor local tissue microenvironment of hypoxia, acidity and inflammation put forward by western medicine [11].Professor Tang Xudong [12] confirmed that traditional Chinese medicine can improve gastric microenvironment and prevent and treat gastric precancerous lesions by regulating gastric flora, reducing gastric mucosal inflammation and abnormal mucosal repair, regulating immunity and other multiple effects.The anti-cancer prescription in this study comes from the tutor's clinical experience prescription for many years.Modern pharmacological studies have shown that the main ingredients in the prescription, such as Hedyotis diffusa, Scutellaria barbata, seaweed and other traditional Chinese medicines, have a variety of biological activities, such as anti-tumor, immune regulation, anti-inflammation, anti-oxidation and so on [13–15].

In this study, 64 active compounds and 159 targets of Anticancer prescription were screened by the method of network pharmacology.The active ingredient-target network map of the anticancer prescription showed that compounds such as quercetin, luteolin , kaempferol, sitosterol, stigmasterol and ivy sapogenin played a major anticancer role in the anticancer prescription.Quercetin, an important compound, is the main representative of flavonols, which can promote cell viability loss, apoptosis and autophagy by regulating PI3K/Akt/mTOR, Wnt/-catenin and MAPK/ERK1/2 signal pathways [16].The second is luteolin, which belongs to flavonoids, which can induce apoptosis by inhibiting tumor cell proliferation, avoiding carcinogenic stimulation and activating cell cycle block, thus blocking the development of cancer [17].

Through PPI network analysis, there are 12 core targets, and the top 6 targets according to degree value are MAPK1, TP53, JUN, CCND1, ESR1 and FOS, among which MAPK1, TP53 and JUN are important targets for anti-cancer prescription in the treatment of gastric cancer.Min [18] and others have confirmed that mitogen-activated protein kinase 1 (mitogen-activatedproteinkinase, MAPK1) can inhibit the viability, migration and invasion of gastric cancer cells, and promote the apoptosis of gastric cancer cells.Zhi [19] studies have shown that miR-378a-3p enhances the sensitivity of ovarian cancer cells to cisplatin by using MAPK1 and GRB2 as target gene pathways.TP53 protein is the main component that regulates the response of cells to a variety of stress.in invasive breast cancer (BRCA) and lung adenocarcinoma (LUAD), TP53 mutated tumors have significantly higher levels of anti-tumor immune signals [20].Transcription factor JUN is an important component of complex activating protein.Studies have shown that c-JUN transcription factor promoter protein plays an important role in the transcription of dihydropyrimidine dehydrogenase (dihydropyrimidinedehydrogenase, DPD), thus affecting the metabolism and drug resistance of 5-FU [21].

The results of GO functional enrichment analysis showed that the active components of Anticancer prescription mainly affected the biological processes such as drug response, lipopolysaccharide, bacterial molecule, cell response to chemical stress and response to metal ions, participated in cellular components such as membrane raft, membrane microdomain, fossa and outer membrane of organelles, and regulated molecular functions such as DNA binding transcription factor binding, G protein coupling amine receptor activity and nuclear receptor activity.KEGG signal pathway enrichment analysis showed that the pathways related to gastric cancer included lipid and atherosclerosis, chemical carcinogenesis-receptor activation pathway, viral infection (Kaposi's sarcoma-associated herpes and human cytomegalovirus), AGE-RAGE and TNF signaling pathway and so on.These functions and signal pathways are significantly related to the occurrence and development of gastric cancer [22, 23], suggesting the molecular mechanism of the effect of anticancer prescription on gastric cancer.

The results of molecular docking showed that eight active components in the anti-cancer prescription had good binding to their corresponding target proteins, among which CID 5280343 (quercetin) had the best binding activity with FOS, followed by CID 5281605 (baicalein) and FOS, CID5280445 (luteolin) and MAPK1, CID5280445 and JUN, CID 5280343 and MAPK1.Therefore, it can be considered that MAPK1 is the target protein with the best binding activity to the main active components in anti-cancer prescription.Quercetin, luteolin and baicalein all belong to flavonoids [16, 17, 24], and both of them have high binding activity to MAPK1.It can be seen that flavonoids are important active components of anticancer prescription in the treatment of gastric cancer.The results of molecular docking showed that the main active components of anticancer prescription had similar binding activity to Xeloda.

To sum up, based on the methods of network pharmacology and molecular docking technology, this study predicted the active components, target proteins and possible molecular mechanism of Anticancer Formula in the treatment of gastric cancer, and considered that flavonoids are important active components of Anticancer Formula in the treatment of gastric cancer.These active components may inhibit gastric cancer by affecting multiple pathways related to drug reaction, chemical carcinogenesis, bacterial and viral infection, apoptosis, etc., by acting on MAPK1.

However, this study is a theoretical study of data collection in multiple databases based on the method of network pharmacology, ignoring the fact that the drug components may react and change each other in the process of traditional Chinese medicine compound compatibility preparation, and there are some limitations.Therefore, the predicted results of this study need to be verified by further experiments.

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