Research ArticlePotential Molecular Mechanisms of Chaihu-Shugan-San inTreatment 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, China2Department of Oncology, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing 100102, China

Correspondence should be addressed to Shijie Zhu; 20180941234@bucm.edu.cn

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 isincreasingly recognized as a possible complementary and alternative therapy for breast cancer. Chaihu-Shugan-San is a traditionalChinese medicine prescription, which is extensively used in clinical practice. Its therapeutic effect on breast cancer has attractedextensive 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 8074potential drug targets were obtained in the TCMSP database according to the screening conditions. 2384 disease targets werecollected in the TTD, OMIM, DrugBank, GeneCards disease database. We applied the Bisogenet plug-in in Cytoscape 3.7.1 toobtain 451 core targets. /e biological process of gene ontology (GO) involves the mRNA catabolic process, RNA catabolicprocess, telomere organization, nucleobase-containing compound catabolic process, heterocycle catabolic process, and so on. Incellular component, cytosolic part, focal adhesion, cell-substrate adherens junction, and cell-substrate junction are highlycorrelated with breast cancer. In the molecular function category, most proteins were addressed to ubiquitin-like protein ligasebinding, protein domain specific binding, and Nop56p-associated pre-rRNA complex. Besides, the results of the KEGG pathwayanalysis 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 byChaihu-Shugan-San and has certain reference value for the development and application of new drugs.

1. Introduction

Breast cancer is one of the most common cancers en-dangering women’s health all over the world. /e GLOBO-CAN 2018 statistics show alarming results that there are 8.6million new cases of female cancer and 4.2 million femalecancer deaths worldwide./e proportion of breast cancer is24.2% and 15.0%, respectively, ranking first in femalecancer incidence and death [1]. It is predicted that, by the2050s, the global incidence of breast cancer will reachnearly 3,200,000 new cases of breast cancer each year./esedatasets reflect the high incidence of breast cancer and theurgent global need for breast cancer prevention andtreatment measures [2].

Traditional Chinese medicine (TCM) has a long historyin the etiology, pathogenesis, prevention, and treatment ofbreast cancer. According to the principle of TCM syndromedifferentiation and treatment, the clinical syndrome ofbreast cancer is mainly “Liver-Qi” stagnation. Chaihu-Shugan-San is one of the classical prescriptions for thetreatment of “Liver-Qi” stagnation. It has the effect ofsoothing “Liver-Qi.” It has a history of 485 years and iswidely used in clinical practice [3–5]. Chaihu-Shugan-Sanincludes seven kinds of traditional Chinese medicine such asBupleurum chinense DC (Chinese name: Chaihu), RadixPaeoniae Alba (Chinese name: Baishao), Citrus reticulataBlanco (Chinese name Chenpi), Cyperus rotundus L(Chinese name: Xiangfu), Glycyrrhiza uralensis Fisch

HindawiEvidence-Based Complementary and Alternative MedicineVolume 2020, Article ID 3670309, 9 pageshttps://doi.org/10.1155/2020/3670309

(Chinese name: Gancao), Citrus aurantium L (Chinesename: Zhiqiao), and Ligusticum chuanxiong Hort (Chinesename: Chuanxiong) [6]. Traditional Chinese medicinemeridian tropism is one of the core components of thetheory of TCM. According to the theory of TCM, thecharacteristics of the selective distribution of the effectivecomponents of traditional Chinese medicine in the body arebasically consistent with the relationship between the visceraand viscera of the corresponding meridian tropism. Chaihu,Chenpi, Xiangfu, Chuanxiong, and Baishao in Chaihu-Shugan-San belong to the liver meridian, which can soothethe liver and regulate Qi well. In traditional Chinesemedicine theory, the main location of breast cancer is in theliver and often related to the spleen and kidney in the processof disease. Chaihu-Shugan-San meridian attribution isconsistent with breast cancer meridian attribution and canbetter play the therapeutic effect.

As a classical prescription, Chaihu-Shugan-San has beenwidely studied in pharmacology. Chaihu-Shugan-San hasthe pharmacological effects of antidepressant [7], regulationof neuro-endocrine-immune network [8, 9], anti-inflammation, antioxidative stress [10], lipid-lowering andglucose-lowering, and antifibrosis [11]. In terms of antitu-mor, in a study of 86 patients with stage III breast cancer, thecontrol group was given a standardized CAF regimen(cyclophosphamide + doxorubicin + 5-fluorouracil), and theexperimental group was treated with Chaihu-Shugan-San onthe basis of CAF. /e short-term effective rate of the ob-servation group was significantly higher than that of thecontrol group (81.4% vs. 58.14%); the Karnofsky improve-ment rate of the observation group was significantly higherthan that of the control group (48.84% vs. 34.88%) [12]. As asafe complementary alternative therapy, Chaihu-Shugan-San combined with other chemotherapy regimens can im-prove the therapeutic effect, alleviate the myelosuppressioncaused by chemotherapeutic drugs, and improve theprognosis of breast cancer patients [13, 14]. However, themechanism of Chaihu-Shugan-San in the treatment ofbreast cancer remains to be further explored.

Classical prescriptions are currently the preferred way to treatdiseases in TCM clinic, but they lack a scientific basis to reasonablyexplain themechanism of TCMprescriptions from the whole to thelocal level or from the cellular to the molecular level [15]. Networkpharmacology is an emerging discipline based on the integration ofsystems biology, molecular biology, pharmacology, and a variety ofnetwork computing platforms in the context of the era of big data,which can more directly explain the association between TCMprescriptions and diseases [16]. /erefore, this study constructed amultidimensional network of “ingredient-target-pathway” throughnetwork pharmacology to explore the potential molecular mecha-nismof Chaihu-Shugan-San in the treatment of breast cancer and toprovide a certain theoretical basis for Chaihu-Shugan-San in thetreatment of breast cancer.

2. Materials and Methods

2.1. Screening of Active Components and Target Prediction inChaihu-Shugan-San. In this study, the chemical compo-nents of the seven herbs were searched on Traditional

Chinese Medicine Systems Pharmacology Database andAnalysis Platform (TCMSP, http://tcmspw.com/tcmsp.php,updated onMay 31, 2014) [17]. Search keywords are Chaihu,Baishao, Chenpi, Xiangfu, Gancao, Zhiqiao, and Chuan-xiong, and only oral bioavailability (OB) ≥30% and drug-likeness (DL) ≥0.18 were considered in this study. /eCanonical SMILES sequence of the compound was searchedin the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) [18], and this sequence was used to predict the target ofthe compound in the Swiss Target Prediction online data-base (http://www.swisstargetprediction.ch/) [19] and collecttarget protein gene names and UniProt ID in the predictionresults.

2.2. Network Construction of Components and Targets./e chemical composition and potential targets of the aboveChaihu-Shugan-San were uploaded to Cytoscape 3.7.1 [20]software to build up the component-target network. In thenetwork, the degree centrality (DC) represents the numberof nodes in the network that directly interacts with the node./e greater the degree, the more the biological functions itparticipates in; the betweenness centrality (BC) refers to theproportion of the number of nodes passing through theshortest path in the network, and the larger the BC is, themore influential the node is. Closeness centrality (CC) re-flects the degree of proximity between nodes, and the re-ciprocal of the shortest path distance from one node to othernodes is CC. /e closer the nodes are, the larger the CC is;the average shortest path length (ASPL) is the average of theshortest path length between all points in the network. /esmaller the average path of a node, the more crucial thisnode is in the network.

2.3. Prediction ofBreastCancerTargets. With “breast cancer”or “malignant breast tumors” as keywords, we searched inOnline Mendelian Inheritance in Man (OMIM, http://www.omim.org/, updated on May 4, 2018) [21], DrugBank(https://www.drugbank.ca/, version 5.1.6, updated on Apr22, 2020) [22],/erapeutic Target Database (TTD, http://db.idrblab.net/ttd/, updated on Nov 11, 2019) [23], and Gen-eCards (https://www.genecards.org/, version 4.14.0) [24] tocollect breast cancer-related targets. In the GeneCards da-tabase, the higher the score value is, the closer the rela-tionship between the target and disease is, and the scorevalue greater than the median is used as the screeningcondition to extract the key target./e above retrieval resultswere combined to remove duplicates and serve as theprediction target library of breast cancer.

2.4. Protein-Protein Interaction (PPI) Network Constructionand Selection of Core Targets. /e BisoGenet plug-in inCytoscape 3.7.1 draws the PPI network and maps theChaihu-Shugan decoction component targets and breastcancer-related disease targets into the protein interactionrelationship network, using Cytoscape 3.7.1 merge twoprotein interaction networks, to extract the intersection ofthe network. Based on the intersection network, the

2 Evidence-Based Complementary and Alternative Medicine

CytoNCA plug-in [25] is used to screen out the nodes whosedegree centrality (DC) is greater than 2 times the median ofall nodes. After multiple screening, the core PPI network isfinally obtained.

2.5. GO Functional Enrichment Analysis and KEGG PathwayEnrichment Analysis. /e core target of PPI network se-lected above was imported into Metascape (https://metascape.org/gp/index.html, updated on March 20,2020) [26] database for KEGG (Kyoto Encyclopedia ofGenes and Genomes) pathway analysis and GO (GeneOntology) biological process enrichment analysis. Param-eter is set to min overlap >3, p value cutoff <0.01, and minenrichment >1.5. Taking p value as parameter and sortingfrom small to large as screening condition, KEGG pathwayand GO biological process of the top 20 eligible were selectedand uploaded to OmicShare (http://www.omicshare.com/tools) platform for data visualization.

2.6. Constructing PPI Network of Ingredient-Disease-KEGGPathway. /e top 20 KEGG pathways, Chaihu-Shugan-Sanactive ingredients, and disease common targets wereuploaded to Cytoscape 3.7.1 software to obtain the multi-dimensional network diagram of component-disease-KEGG pathway.

3. Results

3.1. Active Ingredient and Target of Chaihu-Shugan-San.OB ≥30%, DL ≥0.18 as the screening conditions, aftersearching TCMSP database, ChaiHu-ShuGan-San obtaineda total of 157 chemical components, 13 compounds fromBaishao, 17 compounds from Chaihu, 5 compounds fromChenpi, 7 compounds from Chuanxiong, 5 compoundsfrom Zhiqiao, 92 qualified compounds from Gancao, and 18compounds from Xiangfu (as shown in Table S1 in Sup-plementary Materials). 8074 targets were obtained by in-putting 158 chemical components into Swiss TargetPrediction online database.

3.2. Compound-Target Network Construction. /e com-pound-target network consists of 945 nodes and 8200 edges.29 of 157 compounds were not found in the database and notinvolved in the network construction (Figure 1). In thisnetwork, the average degree value is 15.647, and most of theproteins share common ligands with other proteins, whichreflects the mechanism of the joint action between multi-components and multitargets of Chaihu-Shugan-San, andconform to the characteristics of the traditional Chineseformula. Table 1 shows the detailed topological parametersof the top 10 compounds with high DC.

3.3. Screening of Breast Cancer Targets. Breast cancer ormalignant breast tumors were used as keywords to search inTTD, OMIM, DrugBank, and GeneCards databases. 37disease targets were obtained from TTD database, 1163disease targets were screened from OMIM database, 202

disease targets were screened from Drugbank database, and1286 disease targets with score >13.96 were obtained fromGeneCards database. /e duplicates were deleted aftermerging, and 2,384 breast cancer-related targets were finallyobtained.

3.4. Construction of PPI Network of Chaihu-Shugan-San andDisease Targets. To further explore the pharmacologicalmechanism of Chaihu-Shugan-San on breast cancer,Chaihu-Shugan-San and breast cancer protein were inputinto the BisoGenet plug-in of Cytoscape 7.2.1 software formerging. CytoNCA plug-in performs topological analysisand takes 2 times of the average degree value as the screeningcondition. In the first screening, a network composed of2,728 nodes and 109,005 edges was obtained by the medianDC >46. Finally, a PPI network with 451 nodes and 17,140edges was constructed by further screening with the medianDC >156. /e process is shown in Figure 2. Topologicalparameters of the top 10 targets with high DC are shown inTable 2, and other detailed results are shown in Table S2.

3.5. GO Biological Process and Enrichment Analysis of KEGGPathway. GO biological process consists of molecularfunction (MF), biological process (BP), and cellular com-ponent (CC) to interpret antitumor biological processes atkey targets. Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis studies the key target of an-titumor signaling pathways. /e results of GO enrichmentanalysis showed that there were 2000 biological processes,274 cell components, and 564 molecular functions. KEGGenrichment has 154 signaling pathways. According to theranking of p values, the top 20 were selected to plot thebubble chart (Figure 3). /e left side of each chart is the topenriched name. /e color of bubbles from blue to redrepresents the p value from large to small. /e larger thebubbles, the larger the gene count of the pathway. /ehorizontal axis represents the ratio of the pathway genes tothe total input genes. /e top 20 signal pathways of KEGGenrichment are shown in Table S3.

3.6. 7e Multidimensional Network of “Component-TargetDisease-KEGG Signaling Pathway” Was Constructed.Combining the component-target network and the first 20KEGG signaling pathway targets, a multidimensional net-work of “component-disease target-KEGG signaling path-way” was obtained by Cytoscape 7.2.1 software (as shown inFigure 4). /e results showed that the effective componentsof Chaihu-Shugan-San could treat breast cancer by multi-target and multisignal pathways.

4. Discussion

Traditional Chinese medicine compound acts on diseasesthrough multimolecule, multitarget, and multipathway andplays a certain therapeutic effect. Network pharmacology is ascience based on the macroconnection under the back-ground of the big data era. It systematically analyzes the

Evidence-Based Complementary and Alternative Medicine 3

molecular mechanism of action from all levels, which isconsistent with the holistic view of TCM and the thought ofsyndrome differentiation and treatment. Chaihu-Shugan-San prescription, in which Chaihu is the monarch drug, isgood at soothing the “Liver-Qi.” Xiangfu and Chuanxiongare the minister drugs, which can relieve “Liver-Qi.” Chenpiand Zhiqiao, regulating “Qi” stagnation and Baishao,nourishing “Blood” and softening the “Liver,” are adjuvants.Gancao is used as a guide medicine to reconcile variousdrugs./e combination of various drugs can regulate “Liver-Qi” and smooth “Qi.” Chaihu-Shugan-San has a history ofmore than 480 years. /e basic compatibility of clinicalmedication is Chenpi 6 g, Chaihu 6 g, Chuanxiong 4.5 g,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 add250ml of water to the herbs and boil them for 30min. Su[27] and his team identified 33 chemical constituents inChaihu-Shugan-San. Among them, gallic acid (source:Shaoyao), oxidized paeoniflorin (source: Shaoyao), paeo-niflorin (source: Shaoyao), paeoniflorin (source: Shaoyao),glycyrrhizin (source: Gancao), naringin (source: Chenpi,Zhiqiao), hesperidin (source: Chenpi, Zhiqiao), and ferulicacid (source: Chuanxiong) had higher contents, all above1000mg/g [28]. Although some studies have comprehen-sively elucidated the treatment of depression [6, 29], non-alcoholic fatty liver disease [30, 31] and functional dyspepsia[5] by Chaihu-Shugan-San, no studies have comprehen-sively elucidated the mechanism of Chaihu-Shugan-San inthe treatment of breast cancer. /erefore, with the aid ofnetwork pharmacology, this study analyzed the specificmolecular mechanism of Chaihu-Shugan-San in the treat-ment of breast cancer from a microscopic perspective.

/e results of network analysis showed that the activeingredients in Chaihu-Shugan-San mainly included beta-sitosterol, kaempferol, quercetin, naringenin, isorhamnetin,and nobiletin. Beta-sitosterol can promote the apoptosis of

breast cancer cells by activating the Fas signaling pathwayand caspase-8 activity [32] and is expected to be an orphannutrition drug against cancer [33]. Kaempferol has shown agood affinity for PAK4 in molecular docking and is con-sidered to be a potential inhibitor in triple-negative breastcancer [34], and kaempferol can prevent G2/M phase of thecell cycle by downregulating CDK1 in human breast cancerMDA-MB-453 cells [35], and blocking RhoA and Rac1signaling pathways to inhibit breast cancer cell migrationand invasion [36] is a powerful antioxidant inducer and caninhibit oncogene transformation and induce cancer cellapoptosis and DNA damage. Quercetin, naringenin, andisorhamnetin, such as flavonoids, can prevent breast cancercell migration through inflammatory and apoptotic cellsignaling [37, 38], and quercetin can induce autophagy byinhibiting the Akt-mTOR pathway [39].

/e PPI network showed that the active components inChaihu-Shugan-San may function through the core targetssuch as histone deacetylase 1 (HDAC1), huntingtin (HTT),RAC-alpha serine/threonine-protein kinase (AKT1), hepa-toma-derived growth factor (HDGF), roquin-1 (RC3H1),chromobox protein homolog 8 (CBX8), histone deacetylase

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MMP10

GUSB

AVPR1A

ITGAL

MAPK3

SRC

PKN1

SCN5A

ADAMTS5PLAU

HDAC6

CA9

PTP4A3P2RX3

BMP4

AKR1C2

DPYD

MAP3K5

HDAC9

ADRA1D

F2RLIPE

CASK

CYP19A1

CASP7

RPS6KA1

LTA4H

GPR35

CSNK1D

CA7 KDM4E MGMTSHBGSCARB1

TAAR1

EGFR

PTGES

FGFR3

TBXAS1

MPEG1

RPS6KA3

CCNT1

TDP2

PLK4

CYP1A1

PSEN1

MTNR1A

CNR2

CDC42BPA

TACR2

MAPKAPK5

ADCY5

PDE10A

GCK

PDGFRA

IRAK4

COMT

MMP7

FGFR1

CTSB

MARS

SRD5A1

DRD5

CCNA2

RBP4CLK2

PRKCD

AKR1C3

RELA

PLAA

OXTR

GRM5

TGM2

OPRD1

OGA

HSP90B1

GRM1

FFAR1

CYP11B2

KDM5C

OPRM1

SLC19A1

CCR2

CES2

PPIA

ZQ

MOL005100

MMP3

PFKFB3

KAT2B

KDM1A

CDK4

PDE3A

TNF

CSK

SHH

SLC18A2

AKR1C4

ADAM9 CAPN2

CDK1

CTSD

SSTR5

ODC1

DBF4

ABCB1

SPHK2

PDGFRB

TNNI3K

BACE1

GRIN2A

GSTA1

NPY5R

ESR2

EPAS1

BMP1

DHCR7

PDE6C

FOLR2

PGF

LRRK2

FGF1

KDM4C

PRKCB

SIRT1

ERCC5

ABHD6

MOL004058

MOL004068 MOL004053

XFMOL004071

MOL004074

MOL000006

MOL000449

GLI1 ADORA2ATGFBR1

EPHA3ESRRB EPHB1

MOL003044

MAP3K8FLT3

SPHK1

DYRK1A

PTPN11 NAE1

CSNK1G1NR3C1

LGALS7

MAP2K3

PDE1A

IGFBP6

MGAT2

SERPINA6

LYPLA1

ALOX12

PDF

G6PDCALM1

SLC28A2

NEK6

VEGFA

ERBB2

ATR

CAPN1

TLR9

CA5B

HSD11B2

ESR1

GLO1

CCR1

ADORA3

HIF1A

DNMT1

DYRK2

MT-CO2

STK17B

CDK7

HSD11B1

P2RX7

PON1

AKR1A1

TAOK1

NOS1

MOL002135

ADAMTS4

MAP2K1

GABRA2

CD81

METAP2

PRKD2

CASP3

UTS2R

NEK2

ST6GAL1

CHRM3

AKR1B1

JAK2

ALOX15B

DSTYK

HSPA5

PROKR1

PRKD1

PABPC1

CLK3

KDM5A

AGTR1

DNM1

PTGER4

LDHA

RPS6KA5

CTSL

S1PR3

MBD2

CYP1A2

CSNK1A1

ILK

AKR1B10

KCNH2

CHRM2

LYN

PLA2G1B

TTR

CDK5R1

FOLH1

FLT1

FABP3

TAOK3

STAT3

MMP9

PNP

VDR PTGS1

F3 IGF1R

EIF4H

CHRM1

ABL1CCR4

EIF2AK3

COQ8B

ADCY10

DMPK

PLEC

ROCK2

EPHB4

HDAC10

SLC6A9

REN

KCNA5

CA5A

NCOR1

PTPN2

SNCA

DUT

ACPP

IGFBP5

SLC46A1

POLB

PLG

PTAFR

EDNRB

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.

Table 1: Topological parameter of top 10 compounds.

ID Molecule name DC BC CC ASPLMOL000422 Kaempferol 317 0.011 0.377 2.651MOL000354 Isorhamnetin 303 0.008 0.373 2.681MOL000359 Sitosterol 233 0.005 0.360 2.774MOL004328 Naringenin 199 0.058 0.397 2.520MOL000358 Beta-sitosterol 139 0.004 0.357 2.802MOL000098 Quercetin 116 0.009 0.377 2.654MOL004609 Areapillin 101 0.008 0.372 2.690MOL003044 Chrysoeriol 101 0.008 0.371 2.690MOL000006 Luteolin 101 0.008 0.371 2.690MOL004071 Hyndarin 101 0.063 0.374 2.677

4 Evidence-Based Complementary and Alternative Medicine

2 (HDAC2), small ubiquitin-related modifier 1 (SUMO1),40 S ribosomal protein SA (RPSA), and 60 S acidic ribosomalprotein P0(RPLP0). HDAC1 plays an important role intranscriptional regulation and cell cycle progression [40].HDAC1 can promote the proliferation and migration ofbreast cancer cells by activating the Snail/IL-8 signalingpathway [41]. Downregulation of HTT transcription andprotein levels is a key factor in poor prognosis andmetastasisdevelopment of breast cancer [42]. AKT1 is involved in theregulation of many tumor processes, including tumorproliferation, cell survival, metabolism, growth, and an-giogenesis. /e mutation frequency of AKT1 in Chinesebreast cancer patients is 3.2%, and it is considered to be asensitive target for the treatment of breast cancer. A studyinvolving 313 Chinese breast cancer patients found that themutation frequency of AKT1 in Chinese breast cancer pa-tients was 3.2%, and it is considered a sensitive target for thetreatment of breast cancer [43]. HDAC2 is a poor prognosticfactor in patients receiving anthracycline therapy and ispositively correlated with breast cancer metastasis, pro-gression, increased Ki-67, multidrug resistance protein, andnegatively correlated with overall survival of patients [44]./e occurrence and development of breast cancer are closelyrelated to the core proteins, which fully prove that the

treatment of breast cancer by Chaihu-Shugan-San is theresult of multimolecular, multitarget, and multipathwayinteraction.

/e biological process of Gene Ontology (GO) involvesthe mRNA catabolic process, RNA catabolic process, telo-mere organization, nucleobase-containing compound cat-abolic process, heterocycle catabolic process, and so on. Incellular component, cytosolic part, focal adhesion, cell-substrate adherens junction, and cell-substrate junction arehighly correlated with breast cancer. In the molecularfunction category, most proteins were addressed to ubiq-uitin-like protein ligase binding, protein domain specificbinding, and Nop56p-associated pre-rRNA complex. Inaddition, the results of KEGG pathway analysis showed thatthe pathways mainly involved in apoptosis, cell cycle,transcriptional dysregulation, endocrine resistance, and viralinfection. Estrogen receptor (ER) signal transductionpathway plays a central role in the development of breastcancer. ER can not only regulate the expression of certaingenes through its mediated signal transduction pathway butalso has extensive connections with many other signaltransduction pathways, forming a signal transduction reg-ulatory network [45]. ER binds to receptor proteins in thenucleus, and the receptor is activated. Activated ER-α andER-β form homodimers or heterodimers. Some coregulatorsform complexes with dimers. /e complexes bind to ERresponse elements to initiate transcription, thereby regu-lating the function of target genes, leading to abnormal cellproliferation and differentiation, and ultimately leading totumorigenesis [46]. /e increased mutation rate of ER-α inprecancerous lesions of breast cancer affects the junctionbetween ER-α zinc finger region and ligand binding domain,resulting in high sensitivity of the body to estrogen. Underthe action of low levels of hormones, ER-α is highly bound toTNF-2 co-activator, which leads to the occurrence of tu-mors. For ER receptor-positive breast cancer patients,quantitative expression of ER receptor is an independentimaging factor to evaluate their prognosis, recurrence, andmetastasis [47]. Abnormal activation of MAPK signal

451 nodes and 17140 edges2728 nodes and 109005 edges9787 nodes and 215324 edges

DC

> 46

DC

> 15

6

Figure2: Network topology analysis of PPI.

Table 2: Topological parameter of top 10 core targets.

Target DC BC CC ASPLHDAC1 1976 0.075 0.534 1.874HTT 1695 0.094 0.526 1.900AKT1 1136 0.031 0.495 2.019HDGF 1095 0.021 0.499 2.002RC3H1 930 0.026 0.501 1.996CBX8 836 0.013 0.488 2.050HDAC2 813 0.015 0.493 2.029SUMO1 810 0.021 0.500 1.999RPSA 761 0.013 0.489 2.047RPLP0 736 0.011 0.481 2.081

Evidence-Based Complementary and Alternative Medicine 5

transduction pathway can lead to cell loss of apoptosis anddifferentiation ability, promote malignant transformation,abnormal proliferation, produce tumors, and further pro-mote the proliferation of tumor cells./erefore, inhibitors ofsome key kinases in the MAPK signaling pathway havebecome a hotspot in the treatment of breast cancer in recentyears. Studies have found that Kruppel-like factor 4 [48] andpre-mRNA processing factor 4 [49] affect the growth,

migration, and apoptosis of breast cancer cells throughMAPK and are expected to become new targets for thetreatment of breast cancer. Studies found that the activationor loss of FOXO function can inhibit the growth and me-tastasis of breast tumors [50], and the dysregulation ofFOXO transcription factors has also become a key moleculein the endocrine resistance mechanism [51]. More andmore attention has been paid to the relationship between

GO:0072594:establishment of proteinlocalization to organelle

GO:0006402:mRNA catabolic process

GO:0006401:RNA catabolic process

GO:0032200:telomere organization

GO:0034655:nucleobase−containing compound catabolic process

GO:0046700:heterocycle catabolic process

GO:0044270:cellular nitrogen compound catabolic process

GO:0019439:aromatic compound catabolic process

GO:1901361:organic cyclic compound catabolic process

GO:0010564:regulation of cell cycle process

GO:0097190:apoptotic signaling pathway

GO:0065004:protein−DNA complex assembly

GO:0006281:DNA repair

GO:0000184:nuclear−transcribed mRNA catabolicprocess, nonsense−mediated decay

GO:0019083:viral transcription

GO:0044257:cellular protein catabolic process

GO:0019941:modification−dependent protein catabolic process

GO:1903320:regulation of protein modificationby small protein conjugation or removal

GO:0019221:cytokine−mediated signaling pathway

GO:0071824:protein−DNA complex subunit organization

0.1 0.2 0.3 0.4

Rich factor

GO

term

Top 20 of GO enrichment

Gene number

3.457230e − 631.986787e − 433.973575e − 435.960362e − 437.947150e − 43

p value

50

60

70

80

90

(a)

GO:0044445:cytosolic part

GO:0005925:focal adhesion

GO:0005924:cell−substrate adherens junction

GO:0030055:cell−substrate junction

GO:0005912:adherens junction

GO:0070161:anchoring junction

GO:0022626:cytosolic ribosome

GO:0032993:protein−DNA complex

GO:0098687:chromosomal region

GO:0000784:nuclear chromosome, telomeric region

GO:0000781:chromosome, telomeric region

GO:0000788:nuclear nucleosome

GO:0005840:ribosome

GO:0016604:nuclear body

GO:0044391:ribosomal subunit

GO:1990234:transferase complex

GO:0022627:cytosolic small ribosomal subunit

GO:0000786:nucleosome

GO:0044815:DNA packaging complex

GO:0015935:small ribosomal subunit

0.2 0.4 0.6

Rich factor

Top 20 of GO enrichment

Gene number

4.98804e − 501.28158e − 212.56316e − 213.84474e − 215.12632e − 21

p value

30

40

50

60

70G

O te

rm

(b)

GO:0044389:ubiquitin−like protein ligase binding

GO:0031625:ubiquitin protein ligase binding

GO:0019904:protein domain specific binding

CORUM:3055:Nop56p−associated pre−rRNA complex

GO:0019900:kinase binding

GO:0019901:protein kinase binding

GO:0008134:transcription factor binding

CORUM:306:ribosome, cytoplasmic

GO:0045296:cadherin binding

GO:0050839:cell adhesion molecule binding

GO:0003735:structural constituent of ribosome

GO:0046982:protein heterodimerization activity

CORUM:338:40S ribosomal subunit, cytoplasmic

CORUM:305:40S ribosomal subunit, cytoplasmic

GO:0003682:chromatin binding

GO:0042826:histone deacetylase binding

GO:0051427:hormone receptor binding

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

GO:0005198:structural molecule activity

GO:0001085:RNA polymerase II transcription factor binding

0.00 0.25 0.50 0.75

Rich factor

GO

term

Top 20 of GO enrichment

Gene number

2.554400e − 781.066667e − 192.133335e − 193.200003e − 194.266670e − 19

p value

25

50

75

100

(c)

hsa05203:viral carcinogenesis

hsa05169:Epstein−Barr virus infection

hsa05161:hepatitis B

hsa05200:pathways in cancer

hsa05034:alcoholism

hsa05205:proteoglycans in cancer

hsa04110:cell cycle

hsa03010:ribosome

hsa05220:chronic myeloid leukemia

hsa05168:herpes simplex infection

hsa04120:ubiquitin mediated proteolysis

hsa05212:pancreatic cancer

hsa05166:HTLV−I infection

hsa05215:prostate cancer

hsa04210:apoptosis

hsa04919:thyroid hormone signaling pathway

hsa04010:MAPK signaling pathway

hsa04068:FoxO signaling pathway

hsa05202:transcriptional misregulation in cancer

hsa01522:Endocrine resistance

0.1 0.2 0.3 0.4

Rich factor

Top 20 of KEGG Enrichment

Gene number

4.782950e − 671.462308e − 222.924615e − 224.386922e − 225.849230e − 22

p value

30

40

50

60

Path

way

(d)

Figure 3: GO function enrichment analysis and enrichment analysis of KEGG signaling pathway (top 20). (a) BP. (b) CC. (c) MF. (d)KEGG.

6 Evidence-Based Complementary and Alternative Medicine

viral infection and breast cancer. In particular, humanpapilloma virus (HPV) has a strong cause-and-effect re-lationship with breast cancer. Many studies have found thatdifferent HPV genotypes are associated with the prevalenceof breast cancer and the nuclear prognosis./e relationshipbetween viral infection and breast cancer has been paidmore and more attention [52–54]. /e relationship be-tween Epstein–Barr Virus (EBV) and breast cancer has alsobeen extensively studied, but the current evidence is lessandmore controversial [55]. It is proved again that Chaihu-Shugan-San treatment of breast cancer is through acombination of multiple biological pathways and multiplesignaling pathways, but this multifeature is not only foundin a single disease. Chaihu-Shugan-San is mainly involvedin the regulation of neurotransmitters, regulation of in-flammatory mediators of TRP channels, calcium signalingpathways, cyclic adenosine monophosphate signalingpathways, and neuroactive ligand-receptor interactions toplay an antidepressant role [56]. Chaihu-Shugan-San canimprove cognitive impairment in Alzheimer’s diseasethrough multitarget action, and its effect is verified bybiological experiments [57]. /ese all embody the principleof “treating different diseases with the same treatment” inTCM.

Data Availability

All data generated or analyzed during this study are includedin this paper.

Disclosure

Kunmin Xiao and Kexin Li are the first authors.

Conflicts of Interest

All authors state that they have no conflicts of interest re-garding the publication of this paper.

Authors’ Contributions

Shijie Zhu conceived and designed the experiments. KunminXiao and Kexin Li performed the experiments and wrote themanuscript. Sidan Long and Chenfan Kong contributed toanalysis tools.

Acknowledgments

/is work was supported by grants from the China NationalNatural Science Foundation (Grant no. 81973640).

Supplementary Materials

Table S1: active ingredients of Chaihu-Shugan-San. Table S2:topological parameters of Chaihu-Shugan-San targets. TableS3: the top 20 signal pathways of KEGG enrichment.(Supplementary Materials)

References

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LYNRAC1

YWHAHH4C9

RB1

YWHABTRAF2

YWHAEYWHAG

YWHAZUSP7H2BC8H2BC10H4C4H4C12H4C11H4C8H4C2

IKBKGH4C1

H4C3 H4C6HDAC3KAT2BSNW1

H4-16CSNK2B

H4C5H4C13

H4C14YWHAQ

HSPB1HSPA1B

CSNK2A2CHUK

H4C15

HSPA8

PSMC5

HSPA1AICAM1

MYC

NEDD4

POLR2A

PML

PTEN

CUL2

SHC1

CANX

MAP3K3

PSMC3

PSMD2

PSMD4

TRAF6

VIM

XPO1

FOS

SMAD3

CRKCTNNB1

FN1HSP90AB1

SMAD2

SMAD4

MAP3K1

NFATC2

PCNAAPC

CBL

VCAM1

KAT5

PRMT1

PRKAB1

BRCA1

Pathways in cancer

PPP2CB

CDC37RPS6PPP2R1A

FoxOsignalingpathway

ChronicmyeloidleukemiaSignalingpathwaysregulating

pluripotencyof stem

cells Proteoglycansin cancer

Adherensjunction

Prostatecancer

Colorectalcancer

HTLV-Iinfection

Bacterialinvasion of

epithelialcells

PPP1CBPPP1CC

H3-4H3C1

H3C11H3C3

ITGA4PPP2CARPS6KB2POU5F1BNANOGCOMMD3-BMI1PPP1CA

SOX2POU5F1SMAD1BMI1

H3C4H3C6

H3C8

H3C12

H3C2

H3C7

VHL

FBXO25RBX1 TNFRSF1A

ERBB3

CAV1

ACTG1

ACTBH3C10

FYN

HDAC5

STAT3

CASP8AKT1

NFKB1CREBBP

SRC

ABL1

PLK1

PRKACA

PKM

JUN

CDK4

IKBKB

AR

ESR1

EZR

ERBB2

GSK3B

RELA

MDM2RAF1

NTRK1

CDK2

TGFBR1

ILK

EP300

EGFR

CASP3

HIF1A ATM

PLCG1

HDAC4

SIRT1CDKN1A

CDKN2A

ATF2

DDB1

CDC42

MYH9

AGE-RAGEsignaling

pathway in diabetic

complications

PI3K-Aktsignalingpathway

Hepatitis B

Systemiclupus

erythematosus

Viralcarcinogenesis

Epstein-Barrvirus

infection

Alcoholism Pancreaticcancer

Focaladhesion

Regulationof actin

cytoskeleton

GRB2

H2BC5

HLA-B

NFKBIA

PXN

REL

TRAF1

HNRNPK

DDX3X

IQGAP1MAPK3

HDAC6

MAPK1MAPK14

PIK3R1HDAC2

PRKCA

HSP90AA1

STAT1

PTPN11

CDK1

IKBKE

DDX5 MAPK8HDAC1 PRKCD

FLNA

VCL

SSX2IP

CLTC

SLC19A1

DPYD

HSD17B1

CCND1

LGALS3

PPARG

PRKCQ

PIK3CA

CCND3

RARA

CHRNA3

BTK

CYP19A1

LDHA

JAK1

SPHK2

FGF2

SLC29A1

SMARCA2

EDNRB

NOS2

PRKD1

AURKASLC6A2

FGFR1TKT

MOL000211

MOL004841

MOL005000

MOL004966

MOL000239

MOL004882

MOL004833MOL004974

MOL004058MOL000098 MOL004978MOL004848

MOL004898MOL004904

MOL004071

MOL004838

MOL000359

MOL004864

MOL004935

MOL004959MOL004913

MOL004828

MOL001484

MOL000500

MOL004849

MOL001923

MOL004908

MOL000497

MOL005012

MOL005003

MOL004053

MOL000422

RPS6KB1

STAT6

MMP9

PSEN2

TNF

MAP2K1

MYLK

IGFBP4

CYP17A1F2R HSD17B3GRM1

GLI2FGFR2

CYP1A1ROCK1PDGFRACSK

PLGNR3C2MET MME

EPHA4RARG

ABCG2SPHK1

PKN1

KDM1A

SYK

CYP3A4

SLC2A1

XIAP

GNRHR

ALK

APEX1MIF

PIK3CGPRKCB

NQO2

PON1CCNE1EPHB4CYP1B1CA9ADAM17MGMT

PTGS1

ATR

FLT4

WNT3APIK3CB

MMP14

SRD5A1

AKT2ESRRG

ERCC5

IL2

FOLH1SSTR2

CXCL8BMP4

KCNH2

JAK2

HPGD

AXL

CXCR2

DRD2MAP2K4SHH

BCL2L1

PDGFRB

KDRNCOR1

S1PR3PTPRFMAP3K8

ABCB1

EZH2

PLA2G2ABRS3

CXCR4CSNK1D

TERT

ANPEPIGFBP3

MMP7GLI1MCL1ADA CDC25BTEK FLT3GPER1

PLA2G10MDM4

GSTP1

IGFBP1

AKR1C3

CHEK2

PTK2

IGF1R

CDC25AKISS1RDAPK1CTSL

ROS1PTPN1HMGCRPLK3

BMP1NOS3

TGFB1WEE1

SLC6A4

PIK3CD

CCKBRG6PD

SLC6A3IDH1

CA2

HSPA5

KIT

RPS6KA3

VCP

CBFBF2

SREBF2EPHA2

BRAFHPSE

CTSDCALCAMPOTYMSFABP4SERPINE1CDK7CDC25C

LIMK1FGFR3

NCOR2NR1H2

COMT

TLR9ABCC1

CASP7ESR2

TYMPVEGFATOP1EPHB2AHRALDH2DUSP3

HDAC9TTR

JAK3DNMT1

IGFBP5

CTSBMMP2

VDR

BAD AURKB

MAP2K2DHFR

TLR4KDM4C

IGFBP2CYP2D6

PGR

PRSS1

MAPK9CYP11B1

HSD11B2

MTOR

PLAU

CCNE2

TOP2A

F3

EDNRA

CDK6

SHBG

CYP1A2

CCNA2

PTK2B

CFTR

CHEK1

PARP1

ALOX5

PTGS2

NR3C1

ODC1

FGF1

BCL2

EPHA3

TYR

RET

SRD5A2

FLT1

MMP1MOL004911

MOL004879

MOL001494

MOL004991

MOL004924

HRAS

FABP3

MMP3

CXCR3

GAPDH

PTK6

TGM2

MAPK10

RARB

ESRRAROCK2

FASN

MOL000392

MOL004996

MOL004814

MOL000433

MOL004885

MOL002311

MOL003896

MOL004985

MOL004905

MOL004910

MOL002565

MOL001792

MOL004856

MOL004857

MOL003656

MOL001919

MOL002341

MOL013187 INSR

STS

PDPK1

EPAS1

MMP13

CSF1R

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.

Evidence-Based Complementary and Alternative Medicine 7

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