Molecular mechanism prediction analysis of Xiaozheng decoction in the treatment of bladder cancer based on network pharmacology

Wan-Ying Zhang, Jia-Yi Shi, Ying Chen, Miao-Miao Zhang, Guo-Wei Zhang*

ARTICLE

Molecular mechanism prediction analysis of Xiaozheng decoction in the treatment of bladder cancer based on network pharmacology

Wan-Ying Zhang1, Jia-Yi Shi1, Ying Chen1, Miao-Miao Zhang1, Guo-Wei Zhang1*

1College of Chinese Medicine, Hebei University, Baoding 071000, China.

To investigate the mechanism of Xiaozheng decoction in treatment of bladder cancer based on network pharmacology.Based on the Tradictional Chinese Medicine Systems Pharmacology Database(TCMSP), the active compositions of Xiaozheng decoction were screened. The targets of active components were obtained from TCMSP, Swiss Target Prediction, and STITCH database. By mapping the disease targets of bladder cancer obtained from the DisGeNET and Genecards databases, the potential targets of Xiaozheng decoction for bladder cancer were obtained. The active components of Xiaozheng decoction-targets network was constructed using the Cytoscape software. The target protein interaction (PPI) network was constructed by using the String online platform, which was visualized by Cytoscape software and analyzed by network topology to obtain the key targets of Xiaozheng decoction. GO bioprocess enrichment analysis and KEGG pathway enrichment analysis were performed on key targets of the Xiaozheng decoction by DAVID database.A total of 68 active componentsand 255 potential targets of Xiaozheng decoction in the treatment of bladder cancer were retrieved and screened out. The key targets were enriched and analyzed by the GO biological pathway, and 135 pathways were obtained, which involved transcriptional process, platelet activation, cell proliferation, and apoptosis. KEGG pathway enrichment analysis revealed 95 pathways, mainly involving cancer pathway, bladder cancer pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, and VEGF signaling pathway.The result of this study showed that Xiaozheng decoction may play its therapeutic role through multiple components, multiple targets and multiple pathways, and preliminarily explored the mechanism of Xiaozheng decoction in the treatment of bladder cancer, laying a foundation for subsequent experimental studies.

Xiaozheng decoction, Bladder cancer, Network pharmacology, Targets, Signaling pathway

This study analyzed the active components of Xiaozheng decoction from Professor Liu Xianfang’s empirical formula, predicted potential targets, action pathways and biological processes through a network pharmacology method, and initially explored the molecular role of Xiaozheng decoction in treating bladder cancer from a holistic perspective mechanism.

Background

Bladder cancer is a common malignant tumor of urinary system. According to statistics, bladder cancer is the 10th most common cancer in the world. The incidence of male bladder cancer (9.6/10 million) and mortality (3.2/10 million) are about 4 times that of women (2.4/10 million and 0.87/10million) [1]. Bladder cancer can be divided into two types of muscular invasive bladder cancer and non-muscular invasive bladder cancer, of which non-muscular invasive bladder cancer accounts for 70% of initial bladder tumors [2]. At present, the treatment of bladder cancer is mainly transurethral resection of bladder tumor (TUBRT), combined with bladder influsion chemotherapy. Bladder infusion chemotherapy drugs are the most commonly used method to prevent recurrence after TURBT. The main infusion drugs are epirubicin, hydroxycamptothecin, mitomycin and others. However, its recurrence rate is relatively high, accompanied by various adverse complications [3]. According to reports, patients with non-muscle invasive bladder cancer have a recurrence rate of 50% within one year after surgery and as high as 90% within five years [4]. Traditional Chinese medicine has an obvious synergistic and detoxifying effect on patients with postoperative bladder cancer perfusion [5], which can improve the quality of life and survival of patients.

In traditional Chinese medicine, bladder cancer belongs to the category of micturition, dampness, blood drenching, etc. It is caused by the deficiency of the body’s vital qi, the yin and yang disorders of the visceral qi and blood, and the external exposure to damp heat. Deficiency of the spleen and kidney is the origin of bladder cancer, and sputum, dampness, stasis, and poison condense on the lower coke are the pathogenic factors [6]. The Xiaozheng decoction, composed of,, Coicis Semen,,,and, is an empirical prescription composed of Professor Xian-Fang Liu according to the characteristics of bladder cancer in clinical treatment. At present, it has been widely used in clinical. In the prescription, Coicis Semen which is sweet, light, and slightly cold, returning to the spleen, stomach, lung, is a monarch medicine, and it has the functions of invigorating the spleen and promoting dampness, clearing away heat and detoxification.andare the official medicines. They are used together to play the functions of invigorating the kidney, strengthening the spleen, benefiting Qi and nourishing Yin.was used to clear heat and detoxify.was used to detoxify and disperse.was used to break blood and move Qi andwas used to promote water and dampness. These two are adjuvant medicines. The whole prescription plays the functions of detoxification, promoting dampness, activating blood, supplementing qi and nourishing the kidney [3].

According to research by Liu B and others[7–9], it was found that Xiaozheng decoction combined with bladder infusion of hydroxycamptothecin can significantly reduce the adverse reactions after bladder infusion, reduce the postoperative recurrence rate, and improve the quality of life of patients with bladder cancer. The mechanism of reducing postoperative recurrence may be related to regulating the expression of Slit2, Bim, and Bad genes, thereby increasing the apoptosis of bladder tumor cells. Xiaozheng decoction has a good therapeutic effect in clinical application. However, there are few studies on the mechanism of molecular action [10]. As a new research method, network pharmacology can effectively discover the main active components of traditional Chinese medicine through systematic pharmacology research on components, targets, diseases and pathways, and clarify the mechanism of its pharmacological action from the level of molecular network regulation [11]. In this study, through the research methods of network pharmacology, we analyzed the active components, predicted potential targets, action pathway and biological process of Xiaozheng decoction. Finally, the molecular mechanism of Xiaozheng decoction in the treatment of bladder cancer was initially explored from an overall perspective.

Materials and methods

Screening of active ingredients

According to the keywords “Coicis Semen” ,“”, “” , “”, “”, “” and “”, all chemical components of 7 individual herbs in Xiaozheng decoction were retrieved from the TCMSP (http://lsp.nwu.edu.cn/tcmsp.php). The activecomponents of Xiaozheng decoction were screened by applying the absorption, distribution, metabolism, and excretion (ADME) parameters in pharmacokinetics, including oral bioavailability (OB) ≥30% and drug-likeness (DL) ≥0.18.

Screening of active component targets

TCMSP database, Switch Target Prediction (http://www.swisstargrtprecision.ch/) database and STITCH (http://stick.embl.de/) database were used to retrieve the target of the active components. In the TCMSP database, the targets of the corresponding component are retrieved, and the targets that have been “validated” in the result are selected. We converted the active components into the standard Canonical SMILES format using the PubChem (https://pubchem.ncbi.nlm.nih.gov/) database, and then we imported the SMILES format file into Swiss Target Prediction database. We predicted the targets based on the 2D/3D structural similarity of the chemical component. In the Swiss Target Prediction database, we set the species to “Homo sapiens” and screen out the targets with Probability ≥ 0.7 [12] in the prediction results. In the STITCH database, we set the species to “Homo sapiens” and selected the top ten targets of each active ingredient.

Screening of disease-associated targets

We collected disease targets related to the keywords “bladder cancer” and “Malignant neoplasm of urinary bladder” from GeneCards database (https://www.genecards.org/) and DisGeNET database (http://www.disgenet.org/) .

Construction of “component-target” networks

The targets of Xiaozheng decoction components and disease are intersected using Venny (http://bioinfogp.cnb.csic.es/tools/venny/index.html) to obtain the potential targets of Xiaozheng decoction in the treatment of bladder cancer. Cytoscape software was used to construct the network diagram of “active component-bladder cancer-target” of Xiaozheng decoction to explore the pharmacological mechanism of Xiaozheng decoction.

Construction of protein-protein interaction (PPI) network

A large number of known or predicted protein-protein interactions are collected in the String database (https://string-db.org/) [13]. The potential targets of Xiaozheng decoction in bladder cancer were imported into the String database for studying target protein interactions, and all the targets were only limited the species. What’s more, in order to construct a target PPI network, the lowest target protein interaction threshold is set to “highest confidence” (>0.9) and hide disconnected nodes in the network. Eventually, the protein interaction information is exported in a TSV format file. TSV format file was imported into Cytoscape software, and “Network Analyzer” tool was used to conduct topological analysis of PPI Network. According to the topology results of PPI network, the size and color of nodes are adjusted to represent the size of Degree value.

Gene ontology (GO) functional enrichment analysis and KEGG pathway enrichment analysis

DAVID is an online analysis tool for large-scale genetic biological processes and pathway enrichment. By importing target genes into DAVID, it is easy to analyze and obtain information about biological processes and pathways corresponding to genes [14]. The key target of Xiaozheng decoction for bladder cancer was imported into DAVID database, the Select Identifier was set as uniprot-accession, the List Type was set as Gene List, and the species was set as Homo sapiens. The GOTERM_BP function in Gene_Ontology was used for GO enrichment analysis of the key targets. The KEGG_PATHWAY function was used for KEGG pathway analysis of the key targets.,, Coicis Semen,,,and

Results

The active components of Xiaozheng decoction

A total of 358 chemical components of Xiaozheng decoction were screened from the TCMSP database by retrieval (Coicis Semen contains 38,contains 87,contains 37,contains 31,contains 38,contains 46,contains 81). A total of 75 chemical components were obtained, with OB ≥ 30% and DL ≥ 0.18 as the screening conditions. Among them, Coicis Semen contains 9 active components,contains 20 components,contains 7 components,contains 11 components,contains 12 components,contains 13 components,contains 3 components. After the repeated components were removed, 68 active components of Xiaozheng decoction were finally obtained (Table 1).

Target of active components of Xiaozheng decoction

In the TCMSP database, 159 targets corresponding to each active component were obtained and “validated” was used as the screening condition. In the SwissTargetPrediction database, with Probability ≥ 0.7 as the screening condition, a total of 85 targets were obtained; Selecting the top ten targets in the STITCH database, a total of 98 targets were obtained. After sorting out the repeated targets, a total of 296 active target targets were obtained.

Targets of bladder cancer

A total of 1143 targets were retrieved in the DisGeNET database, and 115 targets were obtained after screening. A total of 7981 targets were obtained in the Genencards database. After sorting out duplicate targets, a total of 7996 targets related to bladder cancer were obtained.

Construction of component-target network

Map the obtained 296 Xiaozheng decoction targets with 7,996 bladder cancer-related targets to obtain 255 intersection targets, which is the potential targets of Xiaozheng decoction in the treatment of bladder cancer, and draw Venny diagram of disease and component targets (Figure 1). The potential action targets of Xiaozheng decoction in the treatment of bladder cancer and their corresponding active component are organized into an Excel table, and imported into Cytoscape 3.6.0 software to construct the Xiaozheng decoction active component-bladder cancer target network diagram (Figure 2). The network contains 276 nodes and 373 edges, of which 21 nodes represent active ingredients, 255 nodes represent the potential targets of Xiaozheng decoction in the treatment of bladder cancer, and edges represent the correlation between active components and potential bladder cancer targets. The Network Analyser tool in Cytoscape (3.6.0) was used for topological analysis of the network graph, and the results showed that 52.4% of the active components had targets ≥ 10, of which 5 compounds had targets ≥ 20. The top 5 of Degree are quercetin, baicalein, kaempferol, diosgenin, and formononetin, which can be interacted with 170, 31, 27, 22, and 20 target proteins respectively, suggesting that these components may be an important component of Xiaozheng decoction in the treatment of bladder cancer.

Table 1 Basic information for active compositions of Xiaozheng decoction

Table 1 Basic information for active compositions of Xiaozheng decoction (continued)

Figure 1 Venny diagram of disease and component targets

Figure 2 Network of active component-disease target. (M1: (4aS,6aR,6aS,6bR,8aR,10R,12aR,14bS)-10-hydroxy -2,2,6a,6b,9,9,12a-heptamethyl-1,3,4,5,6,6a,7,8,8a,10,11,12,13,14b-tetradecahydropicene-4a-carboxylic acid)

Construction of PPI network and topology analysis

Enter the intersection target obtained above into the String database to obtain the PPI network and save it as a TSV file. Import it into Cytoscape software to visualize it and get the PPI network map of Xiaozheng decoction target (Figure 3). Using the Network Analyzer tool to perform a topology analysis on the PPI network, three important network topology parameters of Degree, Betweenness Centrality and Closeness Centrality were obtained. They are important indicators to measure the importance of a target [15]. In this study, the targets with values of Degree, Betweenness Centrality and Closeness Centrality above the mean values were selected as the key targets for the treatment of bladder cancer by Xiaozheng decoction.

In the PPI network diagram, 220 protein nodes and 1036 lines are involved. The size and color of the nodes are used to indicate the size of the Degree value. The larger the node and the darker the color, the higher the degree value. According to the results of topological analysis, the average of Degree is 9.42, the average of Betweenness Centrality is 0.0144, and the average of Closeness Centrality is 0.340. There are 82 targets above the average of Degree, 41 targets above the average of Betweenness Centrality, and 91 targets above the average of Closeness Centrality. In this study, 33 targets that are above the mean value of Degree, Betweenness, and Closeness were selected as the key targets for Xiaozheng decoction in the treatment of bladder cancer, and the key targets information was sorted according to the Degree value (Table 2). The results suggest that the mechanism of Xiaozheng decoction in the treatment of bladder cancer is closely related to mitogen-activated protein kinase 1(MAPK1), Transcription factor AP-1 (JUN), Tumor protein P53 (TP53), Transcription factor p65(RELA), mitogen-activated protein kinase 3 (MAPK3), RAC-alpha serine/threonine-protein kinase (AKT1), mitogen-activated protein kinase 8 (MAPK8), Tumor necrosis factor (TNF), vascular endothelial growth factor A (VEGFA), and matrix metalloproteinases-9(MMP9).

Table 2 Key targets and their network topological parameters

Enrichment analysis results of gene ontology

The DAVID database was used to analyze the enrichment of GO biological processes for 33 key targets, with-vaule < 0.01 as the cut-off point, and 135 items related to biological processes. Results sorted by-value, the top 20 biological processes were selected (Table 3) and displayed by drawing a bar chart with the name of GO biological pathway as the vertical coordinate and -log (10)value as the horizontal coordinate (Figure 4). The results showed that the mechanism of Xiaozheng decoction in the treatment of bladder cancer may involve the regulation of transcriptional process, platelet activation, cell proliferation, apoptosis, NO biosynthesis and other processes.

Enrichment analysis results of KEGG pathway

The KEGG pathway of 33 key targets was analyzed by using DAVID database, and 95 biological pathways with significant differences were obtained by using< 0.01 as the screening condition. The pathway analysis results were sorted by the count value, and the top 20 pathway were selected for display (Table 4). Meanwhile, the pathway-target network was constructed (Figure 5). The results showed that the therapy of Xiaozheng decoction mainly involves Pathways in cancer, Bladder cancer, TNF signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, VEGF signaling pathway, and HIF-1 signaling pathway.

Discussion

Traditional Chinese medicine formula is usually composed of two or more herbs, which can treat diseases through multi-components, multi-targets and multi-pathways interaction. However, traditional research ideas of single component and single target are difficult to fully explain the molecular mechanism of action. Network pharmacology can analyze the mechanism of drugs more comprehensively through the prediction of components and targets, the construction of active component-disease target network maps and target interaction network maps, and the enrichment analysis of biological pathways.

In this study, 68 active components of Xiaozheng decoction were screened using the TCMSP database, and oral bioavailability (OB) and drug-likeness (DL) were used as screening conditions. Two hundred and ninety-one active component targets were predicted and mapped to 7891 disease targets retrieved. Eventually, 255 potential targets for bladder cancer treatment by Xiaozheng decoction were obtained. The active component-disease network map was constructed and topological analysis was performed. According to the analysis results of the active components-disease network, quercetin, baicalein, kaempferol, diosgenin and formononetin had more bladder cancer targets. Related studies have found that quercetin can inhibit the proliferation and invasion of bladder cancer cells and promote their apoptosis by down-regulating the expression of hIAP-2 and MMP-2 proteins [16]. Baicalein can block cell cycle, induce bladder cancer apoptosis and inhibit proliferation by inhibiting the PI3K/AKT/mTOR signaling pathway [17]. Kaemphenol can block the cell cycle in the S phase, induce cell apoptosis, and inhibit the proliferation of bladder cancer cells by inhibiting the expression of phosphorylated AKT (p-Akt), CyclinD1, CDK4 and other genes, and at the sametimepromote the expression of p-BRCA1, p-ATM, p53, p38, and Bax [18]. At the same time, some experiments have shown that both diosgenin and formononetin can significantly inhibit the proliferation and apoptosis of bladder cancer cells [19–20]. Therefore, the key active ingredients of Xiaozheng decoction can induce apoptosis and inhibit the proliferation of bladder cancer cells, suggesting that these key active ingredients have an important role in the treatment of bladder cancer.

Figure 3 PPI network of Xiaozheng decoction

Table 3 GO biological process enrichment analysis results

Figure 4 Diagram of enrichment analysis of biological process

Figure 5 Target-pathway network

Table 4 Results of KEGG pathway enrichment analysis

PPI network topology analysis obtained 33 key targets of Xiaozheng decoction in the treatment of bladder cancer, suggesting that the related mechanism may be closely related to MAPK1, JUN, TP53, AKT1, TNF, VEGFA, and MMP9 targets. MAPK1, a member of the MAP kinase family, is involved in cell proliferation, differentiation, transcriptional regulation and other processes, and interference with the expression of MAPK1 can inhibit the growth and migration of bladder cancer cells [21]. TP53 is one of the most common mutated genes in bladder cancer, with a mutation rate of 12.50% [22–23]. The mutation of TP53 gene can cause disorder of intracellular signaling pathways, uncontrolled cell growth and apoptosis, which leads to canceration of cells [24]. AKT is a serine/threonine protein kinase that can be phosphorylated by a variety of transcription factors, playing an important role in cell signaling and participating in a variety of life activities [25]. AKT1, a subtype of AKT, inhibits the expression of AKT1 in bladder cancer cells and significantly inhibits the migration of bladder cancer cells [26]. VEGFA is a major regulator of angiogenesis, which is related to invasion, migration and angiogenesis of tumor cells. Its expression in bladder tumor tissues is significantly higher than that in normal tissues, suggesting that VEGFA plays an important role in the occurrence and development of bladder cancer [27–28]. As an important member of matrix metalloproteinases (MMPs), MMP9 is overexpressed in a variety of malignancies and plays an important role in the process of malignant tumor invasion and metastasis [29]. Its expression in bladder cancer tissues is significantly higher than that in normal tissues, and the expression level is negatively correlated with the differentiation degree of the tumor [30]. The above analysis indicates that the predicted key targets are important in the development and progression of bladder cancer.

Enrichment analysis of GO biological pathways of key targets resulted in 135 biological pathways, mainly involving the regulation of transcription process, platelet activation, cell proliferation, apoptosis process, NO biosynthesis and other processes. KEGG pathway enrichment analysis resulted in 95 pathways with significant differences, mainly involving Pathways in cancer, Bladder cancer, TNF signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, VEGF signaling pathway, HIF-1 signaling pathway and so on. In the results, the TNF signaling pathway, PI3K-AKT signaling pathway, and MAPK signaling pathway play important roles in inhibiting the migration and invasion of bladder cancer [31–32]. VEGF signaling pathway and HIF-1 signaling pathway are involved in the process of tumor angiogenesis, and inhibition of HIF-1α/VEGF signaling pathway can inhibit the formation of blood vessels in bladder cancer tissues, thereby affecting their growth and metastasis [33–34]. The above analysis indicated that Xiaozheng decoction could inhibit the proliferation, invasion, and multiple pathways of angiogenesis of bladder cancer and their interactions.

Conclusion

In this study, a variety of active components of Xiaozheng decoction, key targets for the treatment of bladder cancer and related biological pathways were obtained by using the platform and database of network pharmacology. The molecular mechanism of Xiaozheng decoction in the treatment of bladder cancer was preliminarily explored from three aspects of components, targets and pathways, which reflected the characteristics of Xiaozheng decoction in the treatment of bladder cancer with multiple components, targets and pathways. However, this study is only based on the database for screening and prediction, and there may be incomplete database information, collection of active ingredients, targets and other incomplete problems. Therefore, the results are limited to some extent and lack the support of experimental data, so further experiments are needed to verify the reliability of network pharmacology prediction results.

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This study was supported by Natural Science Foundation of Hebei Province (H2018201179), Youth Fund of Education Department of Hebei Province (QN2019146), and Scientific Research Fund of Health Department of Hebei Province (NO: 20190948).

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TCMSP, Tradictional Chinese Medicine Systems Pharmacology Database; TUBRT, transurethral resection of bladder tumor; ADME, applying the absorption, distribution, metabolism, and excretion; PPI, protein-protein interaction; GO, gene ontology; MAPK1, mitogen-activated protein kinase 1; TP53, Tumor protein P53; MAPK3, mitogen-activated protein kinase 3; MAPK8, mitogen-activated protein kinase 8, TNF, tumor necrosis factor; VEGFA, vascular endothelial growth factor A; MMP9, matrix metalloproteinases-9; OB, oral bioavailability; DL, drug-likeness; p-Akt, phosphorylated AKT.

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The authors declare that they have no conflict of interest.

:

Wan-Ying Zhang, Jia-Yi Shi, Ying Chen, et al. Molecular mechanism prediction analysis of Xiaozheng decoction in the treatment of bladder cancer based on network pharmacology. Drug Combination Therapy 2020, 2 (4): 185–197.

: Xiao-Hong Sheng.

:25 March 2020,

7 April 2020,

:22 April 2020

Guo-Wei Zhang. College of Chinese Medicine, Hebei University, No.342 East Yuhua Road, Baoding 071000,China. E-mail: xxzgw@126.com.

10.12032/DCT2020A017