Study on the Antibreast Cancer Mechanism and Bioactive Components of Si-Wu-Tang by Cell Type-Specific Molecular Network

Si-Wu-Tang (SWT), a traditional Chinese herbal formula, has shown an effect on antibreast cancer. However, the mechanisms and bioactive components of SWT are still unclear. Fortunately, cell type-specific molecular network has provided an effective method. This study integrated the data of formula components, all types of biomolecules in the human body, and nonexpressed protein in breast cancer cells and constructed the breast cancer cell network and the biological network that SWT acted on the breast cancer-related targets by Entity Grammar System (EGS). Biological network showed 59 bioactive components acting on 15 breast cancer-related targets. The antibreast cancer mechanisms were summarized by enrichment analysis: regulation of cell death, response to hormone stimulation, response to organic substance, regulation of phosphorylation of amino acids, regulation of cell proliferation, regulation of signal transmission, and affection of gland development. In addition, we discovered that verbascoside played the role of antibreast cancer by inhibiting cell proliferation, but there was not a report on this effect. The results of CCK8 and western blot were consistent with the antibreast cancer effect of verbascoside based on biological network. Biological network modeling by EGS and network analysis provide an effective way for uncovering the mechanism and identifying the bioactive components of SWT.


Introduction
Traditional Chinese medicine (TCM) formula, a herb combination, has shown a better effect on treating complex diseases [1,2], such as diabetes, hypertension, and cancer. In the TCM formula, each herb contains numerous components that offer multitarget, multicomponent synergy, and multidimensional pharmacological actions. Besides, some diseases are related to more than one target [3]. Taken together, there is a considerable challenge for researchers to study the mechanisms and bioactive components of the formula using conventional pharmacological methods.
Fortunately, with the improvement of molecular biology and pharmaceutical chemistry, more and more public databases related to biomolecule and TCM have been constructed. Meanwhile, the large-scale molecular detection in various cells and tissues provides abundant omics data for network construction with the improvement of high throughput detection technologies. And above all the emergence of cell type-specific molecular network provided novel idea and orientation for the mechanism and bioactive components study of TCM formula [4,5].
Si-Wu-Tang (SWT), a TCM formula, which consists of four herbs including Rehmanniae Radix Praeparata (Rehmannia glutinosa Libosch.), Angelicae Sinensis Radix (Angelica sinensis (Oliv.) Diels), Paeoniae Radix Alba (Paeonia lactiflora Pall.), and Chuanxiong Rhizoma (Ligusticum chuanxiong Hort.), is used for treatment of breast cancer especially the terminal breast cancer [6][7][8]. However, the antibreast cancer mechanism and bioactive components of SWT have not yet been systematically analyzed. is study integrated the data of TCM formula components, targets of components, metabolism data, gene-transcription factors, biological reactions, and signaling pathways and constructed the breast cancer cell network and the biological network that SWT acted on the breast cancer-related targets by Entity Grammar System (EGS). e biological network could clearly show the interaction between bioactive components of SWT and biomolecules of breast cancer cells. rough network and enrichment analysis, we could identify the bioactive components and summarize the antibreast cancer mechanism of SWT. rough single pathway analysis, we found a novel antibreast cancer ingredient-verbascoside. CCK8 and western blot were applied to test the antibreast cancer effect and the specific biological pathway of bioactive components.

Data Sources.
e components of four herbs in SWT were collected from TCM Database@Taiwan [9] and TCMSP [10]; then the components were supplemented and perfected by the literature in CNKI and PubMed (1979∼2019). e name, structure, and SMILES string of components should be recorded. Repetitive components were deleted by name. For components which possess synonym, repetitive components were deleted by structural similarity. e collected components in each herb of SWT can be seen in Supplemental Information 1.
e proteins which could interact with components were derived from STITCH 5.0 [11] and DrugBank version 5.1.4 [12]. In STITCH, the proteins which could interact with components were discovered by name or SMILES string. e required confidence was higher than 0.400 and the active prediction methods included gene fusion, cooccurrence, coexpression, experiments, databases, neighborhood, predictions, and text mining. Because some components could not find their targets in STITCH, we found them by molecular similarity in DrugBank. e similarity threshold was 0.7 and the other parameters values were "default." e proteins which could interact with the components were recorded in Supplemental Information 2. e data of biological reactions and signaling pathways were derived from the Reactome [13]. Metabolism data of small molecules was from KEGG [14] and HumanCyc [15]. e data of gene-transcription factors was derived from TRRD [16] and TRED [17]. e data of specific expression proteins was derived from the Gene Expression Omnibus (GSE23610) [18]. Breast cancer-related targets (Supplemental Information 3) were collected from TTD [19] and DrugBank version 4.3.

e Construction of Breast Cancer Cell Network and the Biological Network at SWT Acted on the Breast Cancer-Related Targets by EGS.
How to integrate the fragmentary information and further much useful knowledge is a crucial work. So a method which could model the interactions of molecules in biological systems and simulate the evolution of systems is required. In this study, we used EGS to construct the breast cancer cell network and the biological network that SWT acted on the breast cancer-related targets.
EGS is a quadri-tuple, G � (V, F, P, S), whereas V is the character set representing a basic element, F is a finite set of relations for V, V and F were viewed as entity, P is a set of rules to deduce relationships between entities, and S is the starting entity [20]. According to different objective, we can write different reasoning engine. Providing a starting condition (starting entity, such as components in herb or TCM formula), EGS can obtain the result of the relationship among entities automatically. With these relationships among entities, we can construct formulabiomolecule interaction networks in a specific cell. Here, we used EGS as a framework to find the relationships between components and other entities. e results, after reasoning and rearranging, were visualized with Cytoscape.
us, we could construct a cell type-specific molecular network. e application of EGS is more flexible compared to traditional network modeling methods. According to different objectives, we can define different V, F, P, and S. e V, F, P, and S of the breast cancer cell network and the biological network that SWT acted on the breast cancerrelated targets were described by the following: is the set of components of the corresponding herb, V 2 is the set of proteins which can interact with components, V 3 is the set of breast cancerrelated proteins, V 4 is the set of rest proteins in the whole biological network of the human body, and V 5 is the set of rest complexes or small molecule metabolites in the whole biological network of the human body. V 6 is the set of none-express proteins in breast cancer cells, V 7 is the set of the number of reactions or metabolisms, and V 8 is the set of chemical constituents.
neg, known, enzyme}, Y ∈ Z * . at is, A acts on B with an effect described in C through Y reactions and the distance number is Y. If a process takes place in one reaction, Y equals 1. e start (A, B), A ∈ V 1 , B ∈ V 2 . In other words, A represents the components of the corresponding herbs; B represents the targets of compounds. In dis (A, B), A represents the disease-breast cancer, while B represents the breast cancer-related proteins. In pict (A, represents that A (targets of components) affects B (breast cancer-related protein) with an effect described in C by the reactions of Y.
One thing to note here is that the same letters may represent different meaning in various entities; different letters represent the same meaning because they are located in the same entity. 8 is a set of rules which were utilized to construct the breast cancer cell network. P 9 ∪ P 10 ∪ P 11 was utilized to compute all the numbers of disease-related proteins that the components could affect and the distance between them. e distances were limited within 15 steps. P 9 labels the starting point and P 11 labels the ending point. P 12 ∪ P 13 ∪ P 14 ∪ P 15 ∪ P 16 were used to describe the detailed pathway from components target to disease-related proteins, with the specific steps of Y. Forward (C, D, E, M) was the final result used to construct the network. After connecting each of the forward (C, D, E, M), we could get the detailed pathway from components to disease-related proteins. In this study, the starting points were defined as noneexpress proteins in breast cancer cells and components in SWT, respectively. e ending point was defined as breast cancer-related proteins when constructing the biological network that SWT acted on the breast cancer-related targets.

e Bioactive Components of SWT.
Starting with a disease target, this study could identify the bioactive components of SWT reverse biological pathway. In other words, the letter C in forward (C, D, E, M) represents bioactive components.

Enrichment Analysis.
Gene-GO term enrichment analysis was utilized to highlight the most relevant biopathways associated with a given gene list. It was carried by DAVID 6.7 Functional Annotation Clustering [21]. Values of p < 0.05 and FDR < 0.05 were considered as significantly enriched biopathways.  Evidence-Based Complementary and Alternative Medicine e plate was preincubated at 37°C in a humidified atmosphere comprised of 95% air and 5% CO 2 . Verbascoside was diluted in DMSO and then added to each well at a final concentration of 0, 5, 10, 20, 40, 80, and 160 μM, which were incubated for 24 h, 48 h, and 72 h. Paclitaxel (3.83 μg/mL) was used as positive control group. e above groups were all set 5 repeats. CCK-8 solution (10 μL) was added to each well of the plates. e plates was incubated for 1∼4 h. e absorbance was measured at 450 nm using a microplate reader ( ermo).

CCK8 Was
Cell survival rate � A 1 (medication group)/A 2 (blank control group) * 100%; the results were shown as the mean ± standard error of the mean (SEM). e differences between the two groups of independent samples were analyzed by Student's t-test. p < 0.05 was considered statistically significant. e IC50 in different timings were calculated by GraphPad Prism 6.

Western Blot Was Used to Detect the Effluences of Verbascoside on the Expression of Cdk6, AP-1, and MYC in MCF-7.
e MCF-7 cells of logarithmic growth were inoculated in 96-well plates (5000/well). Holes without cells were used as blank controls. e plate was preincubated at 37°C in a humidified atmosphere comprised of 95% air and 5% CO 2 . Verbascoside was diluted in DMSO and then added to each well at a final concentration of 0, 1, 2, 4, 8, 16, and 32 μM, which were incubated for 48 h. Paclitaxel (3.83 μg/mL) was used as positive control group. e above groups were all set 5 repeats. Cell lysis buffer was prepared by PMSF, cocktail, Triton-100, NaVO3, and base lysis buffer. Protein lysis buffer (150 μL) was added to the cells of black control group, paclitaxel group, and verbascoside group, vortex 8∼10 times, pyrolysis 35∼40 min on ice; e lysates were then centrifuged at 12000 rpm for 15 min at 4°C; the concentration of the total proteins was determined using BCA kit. e proteins were separated by SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and then were transferred into a PVDF membrane. e membranes were blocked with 5% skimmed milk in TBST for 2 h at room temperature and incubated with specific primary antibodies overnight at 4°C. e membranes were washed three times and incubated with appropriate secondary antibodies within 1 h at room temperature. Protein bands were detected using the chemiluminescence detection system (Applygen, China). All of the western blots were performed at least three times. e results were shown as the mean ± standard error of the mean (SEM). e differences between the two groups of independent samples were analyzed by Student's t-test. p < 0.05 was considered statistically significant.

3.1.
e Breast Cancer Cell Network. With none-express proteins in the breast cancer cell as the starting point, all the biological reactions, that is, link (I, K, M), in the breast cancer cell, were visualized with Cytoscape. We could obtain  Evidence-Based Complementary and Alternative Medicine the breast cancer cell network ( Figure 1). is network can be used as the background network to qualitatively deduce the antibreast cancer mechanism of SWT.

e Biological Network at SWT Acted on Breast Cancer-Related Targets by EGS.
With all the components as the starting point and breast cancer-related targets as the ending point, the biological networks of SWT that acted on the breast cancer-related targets were constructed by EGS and visualized by Cytoscape (Figure 2). Figure 2 could overall exhibit the bioactive components and the antibreast cancer mechanism on a molecular level. One component could act on multiple targets and multiple components could act on only one target. SWT played a regulatory role through regulating multiple components, multiple targets, and multiple pathways. ese illustrated that the targets and pathways of SWT were extensive.
at is to say, its targets and pathways were not specific but comprehensive. Besides, some components could act on one target, but the active directions were opposite. For example, the direction of quercetin acted on NFκB1 and p50 was positive, while palmitoleic acid was negative. SWT may keep the cell state in balance through this opposite regulation.

e Bioactive Components of SWT.
e biological networks clearly showed the 15 breast cancer-related targets affected by 59 bioactive components. ese 59 components (Table 1) were considered as bioactive components of SWT.
ese indicated that multiple components of SWT play an antibreast cancer role through multiple biological pathways and targets.
According to the results computed by EGS, the biopathway of each bioactive component could be extracted and described. For example, kaempferol could inhibit the proliferation of MCF-7 by increasing the expression of ESR2, improving the activity of ESR2 and PPARG, and finally inhibiting EGFR activity.  Figure 2: e biological networks of SWT that acted on breast cancer-related targets. is network displayed all the pathways that bioactive components acted on the breast cancer-related targets. Different shapes gave the nodes various biological roles. Red node represented breast cancer-related targets, green node represented bioactive components, purple node represented the proteins which can interact with bioactive components, yellow node represented genes and transcript factors, blue node represented complex, and bright blue node represented biological reaction. e green edge represented positive regulation, red edge represented negative regulation, the gray edge represented binding, and the blue edge represented the direction which is uncertain.

Enriched Pathways of Breast Cancer-Related Targets Affected by SWT.
Single biopathway affected by bioactive components could only illustrate the antibreast cancer mechanism of SWT in one aspect. Enrichment analysis of breast cancer-related targets affected by SWT could uncover the mechanism comprehensively. ese targets were highlighted in 17 biopathways (Table 2). We find that 17 biopathways could be divided into 7 groups. In other words, the antibreast cancer mechanism of SWT could be summarized as follows: response to organic substances, regulation of amino acid phosphorylation, regulation of cell death, response to hormone stimulation, regulation of cell proliferation, regulation of signal transmission, and affection of gland development. Apparently, they are all closely associated with the occurrence and development of breast cancer [22][23][24][25][26][27][28].
Some reversed regulations existed in the enrichment analysis of SWT, such as negative regulation of programmed cell death and positive regulation of cell proliferation. ese pathways appeared to be opposite to antibreast cancer, but this phenomenon was also reasonable. ey may be derived from the opposite active direction of some components. Some components acted on one target with the same or opposite active direction, such as quercetin and palmitoleic acid. ese various directions may represent the synergism or antagonism between components. Just because of the synergism and antagonism of these bioactive components, SWT could maintain the homeostasis of the cell. is may be the reason why the drug combination had the potential to enhance efficacy and reduce toxicity [29,30]. All the results fully demonstrated the curative properties of the TCM formula.

Biopathway Affected by Verbascoside.
In order to clearly show and analyze the antibreast cancer mechanism of SWT, we extracted all the biopathways. e following was the biopathway that verbascoside acted on Cdk6 (Figure 3).
Verbascoside is the index component of Rehmanniae Radix Praeparata. It has some effects such as being neuroprotective [31], antipneumonia [32], antifatigue [33], being cardiotonic [34], etc. Research showed that verbascoside could have competitive binding estrogen receptors in Hela with estradiol, antagonizing the upregulation of ERE luciferase activity by estradiol. e effect of verbascoside is similar to known phytoestrogens such as resveratrol [35], indicating that verbascoside has plant estrogenic activity. In addition, verbascoside is associated with the proliferation and differentiation of colorectal cancer and gastric cancer cells [36,37], but there are no reports on its effect on antibreast cancer and mechanism. We further verified the antibreast cancer effect of verbascoside in Sections 3.6 and 3.7.
From Figure 3, we could see that verbascoside enhanced the phosphorylation of SMAD3, promoted the expression of AP-1, and inhibited the expression of MYC and Cdk6. SMAD3 could control gene activity and inhibit cell proliferation; Ap-1 could control many cell processes, including differentiation, proliferation, and apoptosis. AP-1 inhibited the expression of cellular adhesion proteins by the phosphorylation of SMAD3 and inhibited nuclear transcription factor AP-1 in the biological pathway SMAD3/AP-1. Enhancing the activity of AP-1 could reduce the expression of MYC, and MYC was an oncogene that was closely related to the cell cycle. MYC was involved in the malignant transformation of human and animal cells and overexpression in many malignant tumors, which could lead to apoptosis of  Evidence-Based Complementary and Alternative Medicine cells such as HL60 [38,39]. e degradation of MYC could reduce the expression of Cdk6 [40]. Cdk6 was a cyclindependent kinase that controlled cell cycle; Cdk6 could make positive regulation of cell proliferation during the G1/ S phase transition. In conclusion, verbascoside may play the role of antibreast cancer by inhibiting cell proliferation and promoting apoptosis. is study also tested the effect of antibreast cancer on CCK8 and western blot. Figure 4 showed that the positive drug has the highest inhibition rate, and the inhibition rate was 56%∼ 62%. e inhibition rate of verbascoside was lower than paclitaxel. e highest inhibition rate of verbascoside was 30%∼38% when its concentration was 20 μM. When the concentrations of verbascoside were 5∼20 μM and 40∼160 μM, its inhibition rate increased; when the   Figure 5, we could see that the expression of AP-1 increased, and the expression of Cdk6 and MYC decreased.

e Influence of Verbascoside on the Proliferation Activity of MCF-7.
at is to say, verbascoside facilitated the expression of AP-1, inhibited the expression of MYC and Cdk6, and achieved the optimum state at 32 μM. Verbascoside and paclitaxel had the same influence tendency on the expression of the three proteins. e results of Figure 5 were consistent with the analysis in Figure 3.
In conclusion, verbascoside's antibreast cancer action mechanism enhanced the phosphorylation of SMAD3, promoted expression of AP-1, inhibited expression of MYC and Cdk6, and played the role of antibreast cancer by inhibiting the proliferation of MCF-7. e experimental results were consistent with the antibreast cancer effect of verbascoside based on biological network. All these verified the reliability of the bioactive components identification method based on cell type-specific molecular network. e antibreast cancer mechanisms of SWT were summarized by enrichment analysis as follows: regulation of cell death, response to hormone stimulation, response to organic substance, regulation of phosphorylation of amino acids, regulation of cell proliferation, regulation of signal transmission, and affection of gland development.

Conclusion
Single pathway could elucidate the antibreast cancer mechanism of each component. In this study, we identified and validated a novel antibreast cancer ingredient-verbascoside. e results of CCK8 and western blot verified the reliability of the bioactive components identification method based on cell type-specific molecular network. is study will provide the basis for the quality control of SWT and the design of combination drugs based on SWT.
Comparing to protein-protein interaction network (PPI), cell type-specific molecular network constructed by EGS is belonging to a multimolecular type network. EGS could integrate the information of gene regulation, metabolic network, cell signal, etc. Effectively, realizing batch processing is an efficient and flexible information fusion method. When combined with experimental data, especially all kinds of omics data, the space scale characteristics of complex systems are fully considered. At present, no studies have been reported on the mechanism of action of TCM using cell type-specific molecular networks. is study was based on the data included in the existing database or literature, and we did not take into account the quantity of components, so this method still has some limitations. With further study of the bioactivity of SWT components and breast cancer-related biomolecules, the data we used will be more complete and the results will be more precise and integrated.
Data Availability e datasets used during the current study are available from the corresponding author on reasonable request.