Potential Molecular Mechanisms of Chaihu-Shugan-San in Treatment of Breast Cancer Based on Network Pharmacology

Hindawi Evidence-Based Complementary and Alternative Medicine Volume 2020, Article ID 3670309, 9 pages https://doi.org/10.1155/2020/3670309

Research Article Potential Molecular Mechanisms of Chaihu-Shugan-San in Treatment of Breast Cancer Based on Network Pharmacology

Kunmin Xiao,1,2 Kexin Li,1 Sidan Long,1 Chenfan Kong,1 and Shijie Zhu 1,2

1Graduate School, Beijing University of Chinese Medicine, Beijing 100029, China 2Department of Oncology, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing 100102, China

Correspondence should be addressed to Shijie Zhu; [email protected]

Received 16 May 2020; Accepted 5 August 2020; Published 25 September 2020

Guest Editor: Azis Saifudin

Copyright © 2020 Kunmin Xiao et al. /is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Breast cancer is one of the most common cancers endangering women’s health all over the world. Traditional Chinese medicine is increasingly recognized as a possible complementary and alternative therapy for breast cancer. Chaihu-Shugan-San is a traditional Chinese medicine prescription, which is extensively used in clinical practice. Its therapeutic eﬀect on breast cancer has attracted extensive attention, but its mechanism of action is still unclear. In this study, we explored the molecular mechanism of Chaihu- Shugan-San in the treatment of breast cancer by network pharmacology. /e results showed that 157 active ingredients and 8074 potential drug targets were obtained in the TCMSP database according to the screening conditions. 2384 disease targets were collected in the TTD, OMIM, DrugBank, GeneCards disease database. We applied the Bisogenet plug-in in Cytoscape 3.7.1 to obtain 451 core targets. /e biological process of gene ontology (GO) involves the mRNA catabolic process, RNA catabolic process, telomere organization, nucleobase-containing compound catabolic process, heterocycle catabolic process, and so on. In cellular component, cytosolic part, focal adhesion, cell-substrate adherens junction, and cell-substrate junction are highly correlated with breast cancer. In the molecular function category, most proteins were addressed to ubiquitin-like protein ligase binding, protein domain speciﬁc binding, and Nop56p-associated pre-rRNA complex. Besides, the results of the KEGG pathway analysis showed that the pathways mainly involved in apoptosis, cell cycle, transcriptional dysregulation, endocrine resistance, and viral infection. In conclusion, the treatment of breast cancer by Chaihu-Shugan-San is the result of multicomponent, multitarget, and multipathway interaction. /is study provides a certain theoretical basis for the treatment of breast cancer by Chaihu-Shugan-San and has certain reference value for the development and application of new drugs.

1. Introduction Traditional Chinese medicine (TCM) has a long history in the etiology, pathogenesis, prevention, and treatment of Breast cancer is one of the most common cancers en- breast cancer. According to the principle of TCM syndrome dangering women’s health all over the world. /e GLOBO- differentiation and treatment, the clinical syndrome of CAN 2018 statistics show alarming results that there are 8.6 breast cancer is mainly “Liver-Qi” stagnation. Chaihu- million new cases of female cancer and 4.2 million female Shugan-San is one of the classical prescriptions for the cancer deaths worldwide. /e proportion of breast cancer is treatment of “Liver-Qi” stagnation. It has the effect of 24.2% and 15.0%, respectively, ranking first in female soothing “Liver-Qi.” It has a history of 485 years and is cancer incidence and death [1]. It is predicted that, by the widely used in clinical practice [3–5]. Chaihu-Shugan-San 2050s, the global incidence of breast cancer will reach includes seven kinds of traditional Chinese medicine such as nearly 3,200,000 new cases of breast cancer each year. /ese Bupleurum chinense DC (Chinese name: Chaihu), Radix datasets reflect the high incidence of breast cancer and the Paeoniae Alba (Chinese name: Baishao), Citrus reticulata urgent global need for breast cancer prevention and Blanco (Chinese name Chenpi), Cyperus rotundus L treatment measures [2]. (Chinese name: Xiangfu), Glycyrrhiza uralensis Fisch 2 Evidence-Based Complementary and Alternative Medicine

(Chinese name: Gancao), Citrus aurantium L (Chinese Chinese Medicine Systems Pharmacology Database and name: Zhiqiao), and Ligusticum chuanxiong Hort (Chinese Analysis Platform (TCMSP, http://tcmspw.com/tcmsp.php, name: Chuanxiong) [6]. Traditional Chinese medicine updated on May 31, 2014) [17]. Search keywords are Chaihu, meridian tropism is one of the core components of the Baishao, Chenpi, Xiangfu, Gancao, Zhiqiao, and Chuan- theory of TCM. According to the theory of TCM, the xiong, and only oral bioavailability (OB) ≥30% and drug- characteristics of the selective distribution of the effective likeness (DL) ≥0.18 were considered in this study. /e components of traditional Chinese medicine in the body are Canonical SMILES sequence of the compound was searched basically consistent with the relationship between the viscera in the PubChem database (https://pubchem.ncbi.nlm.nih. and viscera of the corresponding meridian tropism. Chaihu, gov/) [18], and this sequence was used to predict the target of Chenpi, Xiangfu, Chuanxiong, and Baishao in Chaihu- the compound in the Swiss Target Prediction online data- Shugan-San belong to the liver meridian, which can soothe base (http://www.swisstargetprediction.ch/) [19] and collect the liver and regulate Qi well. In traditional Chinese target protein gene names and UniProt ID in the prediction medicine theory, the main location of breast cancer is in the results. liver and often related to the spleen and kidney in the process of disease. Chaihu-Shugan-San meridian attribution is consistent with breast cancer meridian attribution and can 2.2. Network Construction of Components and Targets. better play the therapeutic effect. /e chemical composition and potential targets of the above As a classical prescription, Chaihu-Shugan-San has been Chaihu-Shugan-San were uploaded to Cytoscape 3.7.1 [20] widely studied in pharmacology. Chaihu-Shugan-San has software to build up the component-target network. In the the pharmacological effects of antidepressant [7], regulation network, the degree centrality (DC) represents the number of neuro-endocrine-immune network [8, 9], anti- of nodes in the network that directly interacts with the node. inflammation, antioxidative stress [10], lipid-lowering and /e greater the degree, the more the biological functions it glucose-lowering, and antifibrosis [11]. In terms of antitu- participates in; the betweenness centrality (BC) refers to the mor, in a study of 86 patients with stage III breast cancer, the proportion of the number of nodes passing through the control group was given a standardized CAF regimen shortest path in the network, and the larger the BC is, the (cyclophosphamide + doxorubicin + 5-fluorouracil), and the more influential the node is. Closeness centrality (CC) re- experimental group was treated with Chaihu-Shugan-San on flects the degree of proximity between nodes, and the re- the basis of CAF. /e short-term effective rate of the ob- ciprocal of the shortest path distance from one node to other servation group was significantly higher than that of the nodes is CC. /e closer the nodes are, the larger the CC is; control group (81.4% vs. 58.14%); the Karnofsky improve- the average shortest path length (ASPL) is the average of the ment rate of the observation group was significantly higher shortest path length between all points in the network. /e than that of the control group (48.84% vs. 34.88%) [12]. As a smaller the average path of a node, the more crucial this safe complementary alternative therapy, Chaihu-Shugan- node is in the network. San combined with other chemotherapy regimens can improve the therapeutic effect, alleviate the myelosuppression 2.3. Prediction of Breast Cancer Targets. With “breast cancer” caused by chemotherapeutic drugs, and improve the or “malignant breast tumors” as keywords, we searched in prognosis of breast cancer patients [13, 14]. However, the Online Mendelian Inheritance in Man (OMIM, http:// mechanism of Chaihu-Shugan-San in the treatment of www.omim.org/, updated on May 4, 2018) [21], DrugBank breast cancer remains to be further explored. (https://www.drugbank.ca/, version 5.1.6, updated on Apr Classical prescriptions are currently the preferred way to treat 22, 2020) [22], /erapeutic Target Database (TTD, http://db. diseases in TCM clinic, but they lack a scientific basis to reasonably idrblab.net/ttd/, updated on Nov 11, 2019) [23], and Gen- explain the mechanism of TCM prescriptions from the whole to the eCards (https://www.genecards.org/, version 4.14.0) [24] to local level or from the cellular to the molecular level [15]. Network collect breast cancer-related targets. In the GeneCards da- pharmacology is an emerging discipline based on the integration of tabase, the higher the score value is, the closer the rela- systems biology, molecular biology, pharmacology, and a variety of tionship between the target and disease is, and the score network computing platforms in the context of the era of big data, value greater than the median is used as the screening which can more directly explain the association between TCM condition to extract the key target. /e above retrieval results prescriptions and diseases [16]. /erefore, this study constructed a were combined to remove duplicates and serve as the multidimensional network of “ingredient-target-pathway” through prediction target library of breast cancer. network pharmacology to explore the potential molecular mechanism of Chaihu-Shugan-San in the treatment of breast cancer and to provide a certain theoretical basis for Chaihu-Shugan-San in the 2.4. Protein-Protein Interaction (PPI) Network Construction treatment of breast cancer. and Selection of Core Targets. /e BisoGenet plug-in in Cytoscape 3.7.1 draws the PPI network and maps the 2. Materials and Methods Chaihu-Shugan decoction component targets and breast cancer-related disease targets into the protein interaction 2.1. Screening of Active Components and Target Prediction in relationship network, using Cytoscape 3.7.1 merge two Chaihu-Shugan-San. In this study, the chemical compo- protein interaction networks, to extract the intersection of nents of the seven herbs were searched on Traditional the network. Based on the intersection network, the Evidence-Based Complementary and Alternative Medicine 3

CytoNCA plug-in [25] is used to screen out the nodes whose disease targets were screened from Drugbank database, and degree centrality (DC) is greater than 2 times the median of 1286 disease targets with score >13.96 were obtained from all nodes. After multiple screening, the core PPI network is GeneCards database. /e duplicates were deleted after finally obtained. merging, and 2,384 breast cancer-related targets were finally obtained. 2.5. GO Functional Enrichment Analysis and KEGG Pathway Enrichment Analysis. /e core target of PPI network se- 3.4. Construction of PPI Network of Chaihu-Shugan-San and lected above was imported into Metascape (https:// Disease Targets. To further explore the pharmacological metascape.org/gp/index.html, updated on March 20, mechanism of Chaihu-Shugan-San on breast cancer, 2020) [26] database for KEGG (Kyoto Encyclopedia of Chaihu-Shugan-San and breast cancer protein were input Genes and Genomes) pathway analysis and GO (Gene into the BisoGenet plug-in of Cytoscape 7.2.1 software for Ontology) biological process enrichment analysis. Param- merging. CytoNCA plug-in performs topological analysis eter is set to min overlap >3, p value cutoff <0.01, and min and takes 2 times of the average degree value as the screening enrichment >1.5. Taking p value as parameter and sorting condition. In the first screening, a network composed of from small to large as screening condition, KEGG pathway 2,728 nodes and 109,005 edges was obtained by the median and GO biological process of the top 20 eligible were selected DC >46. Finally, a PPI network with 451 nodes and 17,140 and uploaded to OmicShare (http://www.omicshare.com/ edges was constructed by further screening with the median tools) platform for data visualization. DC >156. /e process is shown in Figure 2. Topological parameters of the top 10 targets with high DC are shown in 2.6. Constructing PPI Network of Ingredient-Disease-KEGG Table 2, and other detailed results are shown in Table S2. Pathway. /e top 20 KEGG pathways, Chaihu-Shugan-San active ingredients, and disease common targets were 3.5. GO Biological Process and Enrichment Analysis of KEGG uploaded to Cytoscape 3.7.1 software to obtain the multi- Pathway. GO biological process consists of molecular dimensional network diagram of component-disease function (MF), biological process (BP), and cellular com- -KEGG pathway. ponent (CC) to interpret antitumor biological processes at key targets. Kyoto Encyclopedia of Genes and Genomes 3. Results (KEGG) enrichment analysis studies the key target of antitumor signaling pathways. /e results of GO enrichment 3.1. Active Ingredient and Target of Chaihu-Shugan-San. analysis showed that there were 2000 biological processes, OB ≥30%, DL ≥0.18 as the screening conditions, after 274 cell components, and 564 molecular functions. KEGG searching TCMSP database, ChaiHu-ShuGan-San obtained enrichment has 154 signaling pathways. According to the a total of 157 chemical components, 13 compounds from ranking of p values, the top 20 were selected to plot the Baishao, 17 compounds from Chaihu, 5 compounds from bubble chart (Figure 3). /e left side of each chart is the top Chenpi, 7 compounds from Chuanxiong, 5 compounds enriched name. /e color of bubbles from blue to red from Zhiqiao, 92 qualified compounds from Gancao, and 18 represents the p value from large to small. /e larger the compounds from Xiangfu (as shown in Table S1 in Sup- bubbles, the larger the gene count of the pathway. /e plementary Materials). 8074 targets were obtained by in- horizontal axis represents the ratio of the pathway genes to putting 158 chemical components into Swiss Target the total input genes. /e top 20 signal pathways of KEGG Prediction online database. enrichment are shown in Table S3.

3.2. Compound-Target Network Construction. /e com- 3.6. 7e Multidimensional Network of “Component-Target pound-target network consists of 945 nodes and 8200 edges. Disease-KEGG Signaling Pathway” Was Constructed. 29 of 157 compounds were not found in the database and not Combining the component-target network and the first 20 involved in the network construction (Figure 1). In this KEGG signaling pathway targets, a multidimensional net- network, the average degree value is 15.647, and most of the work of “component-disease target-KEGG signaling path- proteins share common ligands with other proteins, which way” was obtained by Cytoscape 7.2.1 software (as shown in reflects the mechanism of the joint action between multi- Figure 4). /e results showed that the effective components components and multitargets of Chaihu-Shugan-San, and of Chaihu-Shugan-San could treat breast cancer by multi- conform to the characteristics of the traditional Chinese target and multisignal pathways. formula. Table 1 shows the detailed topological parameters of the top 10 compounds with high DC. 4. Discussion 3.3. Screening of Breast Cancer Targets. Breast cancer or Traditional Chinese medicine compound acts on diseases malignant breast tumors were used as keywords to search in through multimolecule, multitarget, and multipathway and TTD, OMIM, DrugBank, and GeneCards databases. 37 plays a certain therapeutic effect. Network pharmacology is a disease targets were obtained from TTD database, 1163 science based on the macroconnection under the back- disease targets were screened from OMIM database, 202 ground of the big data era. It systematically analyzes the 4 Evidence-Based Complementary and Alternative Medicine

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Figure 1: Compound-target network: green diamond nodes represent compounds, red diamond nodes represent common compounds, yellow v nodes represent herb names, and purple circular nodes represent corresponding potential targets of compounds. molecular mechanism of action from all levels, which is consistent with the holistic view of TCM and the thought of Table 1: Topological parameter of top 10 compounds. syndrome differentiation and treatment. Chaihu-Shugan- San prescription, in which Chaihu is the monarch drug, is ID Molecule name DC BC CC ASPL good at soothing the “Liver-Qi.” Xiangfu and Chuanxiong MOL000422 Kaempferol 317 0.011 0.377 2.651 are the minister drugs, which can relieve “Liver-Qi.” Chenpi MOL000354 Isorhamnetin 303 0.008 0.373 2.681 and Zhiqiao, regulating “Qi” stagnation and Baishao, MOL000359 Sitosterol 233 0.005 0.360 2.774 nourishing “Blood” and softening the “Liver,” are adjuvants. MOL004328 Naringenin 199 0.058 0.397 2.520 Gancao is used as a guide medicine to reconcile various MOL000358 Beta-sitosterol 139 0.004 0.357 2.802 drugs. /e combination of various drugs can regulate “Liver- MOL000098 Quercetin 116 0.009 0.377 2.654 Qi” and smooth “Qi.” Chaihu-Shugan-San has a history of MOL004609 Areapillin 101 0.008 0.372 2.690 MOL003044 Chrysoeriol 101 0.008 0.371 2.690 more than 480 years. /e basic compatibility of clinical MOL000006 Luteolin 101 0.008 0.371 2.690 medication is Chenpi 6 g, Chaihu 6 g, Chuanxiong 4.5 g, MOL004071 Hyndarin 101 0.063 0.374 2.677 Xiangfu 4.5 g, Zhiqiao 4.5 g, Shaoyao 4.5 g, and Gancao 1.5 g, which is added or subtracted according to the actual situ- ation of patients. /e official preparation method is to add breast cancer cells by activating the Fas signaling pathway 250 ml of water to the herbs and boil them for 30 min. Su and caspase-8 activity [32] and is expected to be an orphan [27] and his team identified 33 chemical constituents in nutrition drug against cancer [33]. Kaempferol has shown a Chaihu-Shugan-San. Among them, gallic acid (source: good affinity for PAK4 in molecular docking and is con- Shaoyao), oxidized paeoniflorin (source: Shaoyao), paeo- sidered to be a potential inhibitor in triple-negative breast niflorin (source: Shaoyao), paeoniflorin (source: Shaoyao), cancer [34], and kaempferol can prevent G2/M phase of the glycyrrhizin (source: Gancao), naringin (source: Chenpi, cell cycle by downregulating CDK1 in human breast cancer Zhiqiao), hesperidin (source: Chenpi, Zhiqiao), and ferulic MDA-MB-453 cells [35], and blocking RhoA and Rac1 acid (source: Chuanxiong) had higher contents, all above signaling pathways to inhibit breast cancer cell migration 1000 mg/g [28]. Although some studies have comprehen- and invasion [36] is a powerful antioxidant inducer and can sively elucidated the treatment of depression [6, 29], non- inhibit oncogene transformation and induce cancer cell alcoholic fatty liver disease [30, 31] and functional dyspepsia apoptosis and DNA damage. Quercetin, naringenin, and [5] by Chaihu-Shugan-San, no studies have comprehen- isorhamnetin, such as flavonoids, can prevent breast cancer sively elucidated the mechanism of Chaihu-Shugan-San in cell migration through inflammatory and apoptotic cell the treatment of breast cancer. /erefore, with the aid of signaling [37, 38], and quercetin can induce autophagy by network pharmacology, this study analyzed the specific inhibiting the Akt-mTOR pathway [39]. molecular mechanism of Chaihu-Shugan-San in the treat- /e PPI network showed that the active components in ment of breast cancer from a microscopic perspective. Chaihu-Shugan-San may function through the core targets /e results of network analysis showed that the active such as histone deacetylase 1 (HDAC1), huntingtin (HTT), ingredients in Chaihu-Shugan-San mainly included beta- RAC-alpha serine/threonine-protein kinase (AKT1), hepa- sitosterol, kaempferol, quercetin, naringenin, isorhamnetin, toma-derived growth factor (HDGF), roquin-1 (RC3H1), and nobiletin. Beta-sitosterol can promote the apoptosis of chromobox protein homolog 8 (CBX8), histone deacetylase Evidence-Based Complementary and Alternative Medicine 5 DC > 46 DC > 156

9787 nodes and 215324 edges 2728 nodes and 109005 edges 451 nodes and 17140 edges Figure2: Network topology analysis of PPI.

treatment of breast cancer by Chaihu-Shugan-San is the Table 2: Topological parameter of top 10 core targets. result of multimolecular, multitarget, and multipathway interaction. Target DC BC CC ASPL /e biological process of Gene Ontology (GO) involves HDAC1 1976 0.075 0.534 1.874 the mRNA catabolic process, RNA catabolic process, telo- HTT 1695 0.094 0.526 1.900 mere organization, nucleobase-containing compound cat- AKT1 1136 0.031 0.495 2.019 abolic process, heterocycle catabolic process, and so on. In HDGF 1095 0.021 0.499 2.002 cellular component, cytosolic part, focal adhesion, cell- RC3H1 930 0.026 0.501 1.996 substrate adherens junction, and cell-substrate junction are CBX8 836 0.013 0.488 2.050 highly correlated with breast cancer. In the molecular HDAC2 813 0.015 0.493 2.029 SUMO1 810 0.021 0.500 1.999 function category, most proteins were addressed to ubiq- RPSA 761 0.013 0.489 2.047 uitin-like protein ligase binding, protein domain specific RPLP0 736 0.011 0.481 2.081 binding, and Nop56p-associated pre-rRNA complex. In addition, the results of KEGG pathway analysis showed that the pathways mainly involved in apoptosis, cell cycle, 2 (HDAC2), small ubiquitin-related modifier 1 (SUMO1), transcriptional dysregulation, endocrine resistance, and viral 40 S ribosomal protein SA (RPSA), and 60 S acidic ribosomal infection. Estrogen receptor (ER) signal transduction protein P0(RPLP0). HDAC1 plays an important role in pathway plays a central role in the development of breast transcriptional regulation and cell cycle progression [40]. cancer. ER can not only regulate the expression of certain HDAC1 can promote the proliferation and migration of genes through its mediated signal transduction pathway but breast cancer cells by activating the Snail/IL-8 signaling also has extensive connections with many other signal pathway [41]. Downregulation of HTT transcription and transduction pathways, forming a signal transduction reg- protein levels is a key factor in poor prognosis and metastasis ulatory network [45]. ER binds to receptor proteins in the development of breast cancer [42]. AKT1 is involved in the nucleus, and the receptor is activated. Activated ER-α and regulation of many tumor processes, including tumor ER-β form homodimers or heterodimers. Some coregulators proliferation, cell survival, metabolism, growth, and an- form complexes with dimers. /e complexes bind to ER giogenesis. /e mutation frequency of AKT1 in Chinese response elements to initiate transcription, thereby regu- breast cancer patients is 3.2%, and it is considered to be a lating the function of target genes, leading to abnormal cell sensitive target for the treatment of breast cancer. A study proliferation and differentiation, and ultimately leading to involving 313 Chinese breast cancer patients found that the tumorigenesis [46]. /e increased mutation rate of ER-α in mutation frequency of AKT1 in Chinese breast cancer pa- precancerous lesions of breast cancer affects the junction tients was 3.2%, and it is considered a sensitive target for the between ER-α zinc finger region and ligand binding domain, treatment of breast cancer [43]. HDAC2 is a poor prognostic resulting in high sensitivity of the body to estrogen. Under factor in patients receiving anthracycline therapy and is the action of low levels of hormones, ER-α is highly bound to positively correlated with breast cancer metastasis, pro- TNF-2 co-activator, which leads to the occurrence of tu- gression, increased Ki-67, multidrug resistance protein, and mors. For ER receptor-positive breast cancer patients, negatively correlated with overall survival of patients [44]. quantitative expression of ER receptor is an independent /e occurrence and development of breast cancer are closely imaging factor to evaluate their prognosis, recurrence, and related to the core proteins, which fully prove that the metastasis [47]. Abnormal activation of MAPK signal 6 Evidence-Based Complementary and Alternative Medicine

Top 20 of GO enrichment Top 20 of GO enrichment

GO:0071824:protein−DNA complex subunit organization GO:0015935:small ribosomal subunit

GO:0019221:cytokine−mediated signaling pathway GO:0044815:DNA packaging complex GO:1903320:regulation of protein modiﬁcation GO:0000786:nucleosome by small protein conjugation or removal GO:0019941:modiﬁcation−dependent protein catabolic process GO:0022627:cytosolic small ribosomal subunit

GO:0044257:cellular protein catabolic process GO:1990234:transferase complex

GO:0019083:viral transcription GO:0044391:ribosomal subunit GO:0000184:nuclear−transcribed mRNA catabolic GO:0016604:nuclear body process, nonsense−mediated decay GO:0006281:DNA repair GO:0005840:ribosome

GO:0065004:protein−DNA complex assembly GO:0000788:nuclear nucleosome

GO:0097190:apoptotic signaling pathway GO:0000781:chromosome, telomeric region

GO:0010564:regulation of cell cycle process GO:0000784:nuclear chromosome, telomeric region GO term GO term

GO:1901361:organic cyclic compound catabolic process p value GO:0098687:chromosomal region 7.947150e − 43 p value 5.12632e − 21 GO:0019439:aromatic compound catabolic process 5.960362e − 43 GO:0032993:protein−DNA complex 3.84474e − 21 3.973575e − 43 GO:0044270:cellular nitrogen compound catabolic process GO:0022626:cytosolic ribosome 2.56316e − 21 1.986787e − 43 1.28158e − 21 GO:0046700:heterocycle catabolic process 3.457230e − 63 GO:0070161:anchoring junction 4.98804e − 50 GO:0034655:nucleobase−containing compound catabolic process GO:0005912:adherens junction

GO:0032200:telomere organization GO:0030055:cell−substrate junction

GO:0006401:RNA catabolic process GO:0005924:cell−substrate adherens junction

GO:0006402:mRNA catabolic process GO:0005925:focal adhesion GO:0072594:establishment of protein GO:0044445:cytosolic part localization to organelle

0.1 0.2 0.3 0.4 0.2 0.4 0.6 Rich factor Rich factor

Gene number Gene number 50 80 30 60 60 90 40 70 70 50 (a) (b)

Top 20 of GO enrichment Top 20 of KEGG Enrichment

GO:0001085:RNA polymerase II transcription factor binding hsa01522:Endocrine resistance

GO:0005198:structural molecule activity hsa05202:transcriptional misregulation in cancer

CORUM:5266:TNF−alpha/NF−kappa B signaling complex 6 hsa04068:FoxO signaling pathway

GO:0051427:hormone receptor binding hsa04010:MAPK signaling pathway

GO:0042826:histone deacetylase binding hsa04919:thyroid hormone signaling pathway

GO:0003682:chromatin binding hsa04210:apoptosis

CORUM:305:40S ribosomal subunit, cytoplasmic hsa05215:prostate cancer

CORUM:338:40S ribosomal subunit, cytoplasmic hsa05166:HTLV−I infection

GO:0046982:protein heterodimerization activity hsa05212:pancreatic cancer

GO:0003735:structural constituent of ribosome p value hsa04120:ubiquitin mediated proteolysis 4.266670e − 19 p value 5.849230e − 22

GO:0050839:cell adhesion molecule binding e Pathway hsa05168:herpes simplex infection GO term 3.200003 − 19 4.386922e − 22 2.133335e − 19 GO:0045296:cadherin binding hsa05220:chronic myeloid leukemia 2.924615e − 22 1.066667e − 19 1.462308e − 22 CORUM:306:ribosome, cytoplasmic 2.554400e − 78 hsa03010:ribosome 4.782950e − 67 GO:0008134:transcription factor binding hsa04110:cell cycle

GO:0019901:protein kinase binding hsa05205:proteoglycans in cancer

GO:0019900:kinase binding hsa05034:alcoholism

CORUM:3055:Nop56p−associated pre−rRNA complex hsa05200:pathways in cancer

GO:0019904:protein domain speciﬁc binding hsa05161:hepatitis B

GO:0031625:ubiquitin protein ligase binding hsa05169:Epstein−Barr virus infection

GO:0044389:ubiquitin−like protein ligase binding hsa05203:viral carcinogenesis

0.00 0.25 0.50 0.75 0.1 0.2 0.3 0.4 Rich factor Rich factor

Gene number Gene number 25 75 30 50 50 100 40 60 (c) (d)

Figure 3: GO function enrichment analysis and enrichment analysis of KEGG signaling pathway (top 20). (a) BP. (b) CC. (c) MF. (d) KEGG. transduction pathway can lead to cell loss of apoptosis and migration, and apoptosis of breast cancer cells through diﬀerentiation ability, promote malignant transformation, MAPK and are expected to become new targets for the abnormal proliferation, produce tumors, and further pro- treatment of breast cancer. Studies found that the activation mote the proliferation of tumor cells. /erefore, inhibitors of or loss of FOXO function can inhibit the growth and me- some key kinases in the MAPK signaling pathway have tastasis of breast tumors [50], and the dysregulation of become a hotspot in the treatment of breast cancer in recent FOXO transcription factors has also become a key molecule years. Studies have found that Kruppel-like factor 4 [48] and in the endocrine resistance mechanism [51]. More and pre-mRNA processing factor 4 [49] aﬀect the growth, more attention has been paid to the relationship between Evidence-Based Complementary and Alternative Medicine 7

AHR EPHB2TOP1 VEGFA ALDH2 TYMPBRAF DUSP3 HPSE MAP2K2 EPHA2 DHFR SREBF2 TLR4 COMT KDM4C NR1H2 IGFBP2 NCOR2 CYP2D6 FGFR3 PGR LIMK1 HSD11B2 FABP4TYMSMPO CDK7SERPINE1 CALCACTSD F2 CYP11B1 CDC25C CASP7 HDAC9 ESR2 CBFB H4C11H4C12H4C4H2BC10 MAPK9 TTR ABCC1 H4C2H4C8 H2BC8 H4C5 USP7 JAK3 TLR9 VCP H4C13 YWHAZ PRSS1 YWHAG DNMT1 IDH1 RPS6KA3 H4C14 YWHAE YWHAQ MTOR CTSB SLC6A3 YWHAB H4C15 MMP2 KISS1RDAPK1ROS1PTPN1HMGCR G6PD KIT TRAF2 PLAU CDC25A CTSL CHUK HDAC3IKBKGH4C3 PLK3 WEE1 KAT2B H4C6 RB1 VDR IGFBP5 TGFB1 CCKBR HSPA5 SNW1 H4C1 CSNK2A2 CCNE2 AURKB NOS3 H4-16 H4C9 RAC1 BAD PIK3CD BCL2 HSPA1B TOP2A AURKA BMP1 CSNK2B YWHAH LYN CCND1 SLC6A4 HSPB1 SLC6A2 CA2 EPHA3 HLA-B HSPA1A TRAF1 F3 HSD17B1 FGFR1 ROCK2 CHEK1 ICAM1 TYR H2BC5 MYC HSPA8 REL PSEN2 SLC19A1 TKT ESRRA PARP1 RET GRB2 CYP19A1 RARB NEDD4 PSMC5 PXN RPS6KB1 DPYD ALOX5 DDB1 SRD5A2 SMARCA2 MOL004898MOL004904MOL004974MOL004833MOL001792MOL004905MOL003896FASN POLR2A Chronic Regulation STAT6 LGALS3 PTGS2 PTEN Prostate Focal NFKBIA ATF2 myeloid of actin HIF1A ATM STAT1 HDAC6 PRKCA ESR1 PRKACA SLC29A1 MAPK10 FLT1 PSMC3 cancer adhesion MOL000098MOL004058MOL004978MOL004848MOL002341MOL004910MOL004985 leukemiaSignaling cytoskeleton MMP9 PPARG NR3C1 PML pathways HNRNPK CDKN2A FGF2 TGM2 PSMD2 SIRT1 PLCG1 PTPN11 MAPK14MAPK1 IKBKB PKM MMP1 regulating Colorectal Pancreatic PRKCQ MOL000422MOL000211MOL004071MOL001923MOL001919MOL000392MOL004911 ODC1 Alcoholism CDKN1A TNF SPHK2 PTK6 CUL2 pluripotency cancer cancer DDX3X EGFR HDAC4 CDK1 HDAC2 PIK3R1 EZR JUN PSMD4 of stem CFTR CDC42 PIK3CA MOL004053MOL004841MOL004838MOL004849MOL003656MOL004996MOL004924 FGF1 cells Epstein-Barr EP300 CASP3 IKBKE MAPK3 HSP90AA1AR CDK4 MAP2K1 JAK1 GAPDH TRAF6 Proteoglycans HTLV-I Viral PTK2B SHC1 virus IQGAP1 MOL005003MOL005000MOL000359MOL000500MOL004857MOL004814MOL004991 in cancer infection carcinogenesis MYH9 NTRK1 RELA ILK ERBB2 SRC PLK1 HDAC5 MYLK CCND3 LDHA CXCR3 CSF1R VIM infection Bacterial CCNA2 PI3K-Akt Systemic CLTC RARA MOL005012MOL004966MOL004864MOL001484MOL004856MOL000433MOL001494 STS CANX invasion of Adherens FLNA CDK2 RAF1 MDM2 GSK3B CREBBP NFKB1 STAT3 IGFBP4 EDNRB MMP3 XPO1 signaling lupus epithelial junction EDNRA pathway erythematosus SSX2IP HDAC1 PRKCD MAPK8 TGFBR1ABL1 AKT1 CASP8 CHRNA3 NOS2 MOL000497MOL000239MOL004935MOL004828MOL013187MOL002311MOL004879FABP3 INSR FOS MAP3K3 cells AGE-RAGE DDX5 CHEK2 FoxO signaling Pathways in VCL BTK PRKD1 HRAS PDPK1 CDK6 signaling pathway in Hepatitis B MOL004908MOL004882MOL004959MOL004913MOL002565MOL004885 SMAD3 cancer PTK2 VCAM1 pathway diabetic H3C7 FYN JAK2 EZH2 IL2 MMP13 SHBG SMAD4 complications AKR1C3 KAT5 H3C2 PLA2G2A WNT3A ERBB3 HPGD EPAS1 CYP1A2 MAP3K1 IGFBP1 BRS3 PIK3CB PRMT1 H3C12 CAV1 AXL ALK NFATC2 CXCR4 MMP14 PKN1 ACTG1 IGF1R PCNA PRKAB1 H3C8 CXCR2 CSNK1D GSTP1 XIAP KDM1A BRCA1 H3C6 ACTB PDGFRB DRD2 TERT MDM4 AKT2 APC IGFBP3 PLA2G10 SYK PPP2CB H3C4 H3C10 SHH MAP2K4 ANPEP GPER1 ESRRG CBL MMP7 FLT3 CYP3A4 PPP2R1A SOX2 H3C11 GLI1MCL1 CDC25BTEK CRK RPS6 POU5F1 BCL2L1 FOLH1 ADA ERCC5 CDC37BMI1 SMAD1 H3C3 SLC2A1 CTNNB1 SSTR2 SRD5A1 H3C1 PTGS1 FN1 CXCL8 KDR GNRHR H3-4 FLT4 HSP90AB1 BMP4 NCOR1 PPP1CC KCNH2 S1PR3 ABCG2 SMAD2 MGMT ADAM17 MAP3K8 VHL PPP1CB CA9 PTPRF SPHK1 RBX1 PPP1CA ATR CYP1B1EPHB4CCNE1PON1ABCB1 TNFRSF1A NANOGCOMMD3-BMI1 RARG FBXO25ITGA4PPP2CARPS6KB2POU5F1B APEX1 EPHA4 MIF FGFR2 PIK3CG GLI2 PRKCB GRM1 NQO2 HSD17B3 PLG F2R ROCK1CYP1A1 CYP17A1 PDGFRACSK NR3C2MET MME Figure 4: Component-disease target-KEGG signaling pathway. Green represents active ingredients of drugs; purple represents targets; yellow represents common targets; and red represents signaling pathways. viral infection and breast cancer. In particular, human Conflicts of Interest papilloma virus (HPV) has a strong cause-and-effect relationship with breast cancer. Many studies have found that All authors state that they have no conflicts of interest re- different HPV genotypes are associated with the prevalence garding the publication of this paper. of breast cancer and the nuclear prognosis. /e relationship between viral infection and breast cancer has been paid Authors’ Contributions more and more attention [52–54]. /e relationship between Epstein–Barr Virus (EBV) and breast cancer has also Shijie Zhu conceived and designed the experiments. Kunmin been extensively studied, but the current evidence is less Xiao and Kexin Li performed the experiments and wrote the and more controversial [55]. It is proved again that Chaihu- manuscript. Sidan Long and Chenfan Kong contributed to Shugan-San treatment of breast cancer is through a analysis tools. combination of multiple biological pathways and multiple signaling pathways, but this multifeature is not only found Acknowledgments in a single disease. Chaihu-Shugan-San is mainly involved in the regulation of neurotransmitters, regulation of in- /is work was supported by grants from the China National flammatory mediators of TRP channels, calcium signaling Natural Science Foundation (Grant no. 81973640). pathways, cyclic adenosine monophosphate signaling pathways, and neuroactive ligand-receptor interactions to Supplementary Materials play an antidepressant role [56]. Chaihu-Shugan-San can improve cognitive impairment in Alzheimer’s disease Table S1: active ingredients of Chaihu-Shugan-San. Table S2: through multitarget action, and its effect is verified by topological parameters of Chaihu-Shugan-San targets. Table biological experiments [57]. /ese all embody the principle S3: the top 20 signal pathways of KEGG enrichment. of “treating different diseases with the same treatment” in (Supplementary Materials) TCM. References Data Availability [1] F. Bray, J. Ferlay, I. Soerjomataram et al., “Global cancer statistics 2018: GLOBOCAN estimates of incidence and All data generated or analyzed during this study are included mortality worldwide for 36 cancers in 185 countries,” CA: A in this paper. Cancer Journal for Clinicians, vol. 68, no. 6, pp. 394–424, 2018. [2] J. R. Benson and I. Jatoi, “/e global breast cancer burden,” Future Oncology, vol. 8, no. 6, pp. 697–702, 2012. Disclosure [3] N. Yang, X. Jiang, X. Qiu, Z. Hu, L. Wang, and M. Song, “Modified Chaihu shugan powder for functional dyspepsia: Kunmin Xiao and Kexin Li are the first authors. meta-analysis for randomized controlled trial,” Evidence- 8 Evidence-Based Complementary and Alternative Medicine

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