Research Article Systematic Investigation of

Hindawi Evidence-Based Complementary and Alternative Medicine Volume 2018, Article ID 4365739, 12 pages https://doi.org/10.1155/2018/4365739

Research Article Systematic Investigation of Scutellariae Barbatae Herba for Treating Hepatocellular Carcinoma Based on Network Pharmacology Benjiao Gong ,1 Yanlei Kao ,2 Chenglin Zhang,1 Fudong Sun,1 and Huishan Zhao 1 2

1

Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China Yantai Hospital of Traditional Chinese Medicine, Yantai, China

Correspondence should be addressed to Benjiao Gong; [email protected] and Huishan Zhao; [email protected] Benjiao Gong and Yanlei Kao contributed equally to this work. Received 13 June 2018; Revised 30 October 2018; Accepted 8 November 2018; Published 21 November 2018 Academic Editor: Kuzhuvelil B. Harikumar Copyright © 2018 Benjiao Gong et al. This 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. As the fifth most common type of malignant cancers globally, hepatocellular carcinoma (HCC) is the second leading cause of cancerrelated mortality worldwide. As a long-time medicinal herb in Traditional Chinese Medicine (TCM), Scutellariae Barbatae Herba (SBH) has also been used for treating various cancers including HCC, but its underlying mechanisms have not been completely clarified. Presently, an innovative network-pharmacology platform was introduced to systematically elucidate the pharmacological mechanisms of SBH against HCC, adopting active ingredients prescreening, target fishing, and network analysis. The results revealed that SBH appeared to work on HCC probably through regulating 4 molecular functions, 20 biological processes, and hitting on 21 candidate targets involved in 40 pathways. By in-depth analysis of the first-ranked signaling pathway and hit genes, only TTR was highly and specially expressed in the liver tissue. TTR might play a crucial role in neutrophil degranulation pathway during SBH against HCC. Hence, TTR might become a therapeutic target of HCC. The study investigated the anti-hepatoma mechanisms of SBH from a holistic perspective, which provided a theoretical foundation for further experimental research and rational clinical application of SBH.

1. Introduction Hepatocellular carcinoma (HCC) is the fifth most common type of malignant cancer globally and the second leading cause of cancer-related mortality worldwide [1]. The morbidity of HCC is increasing and more than 50% of new cases are diagnosed in China each year [2, 3], while the median survival of patients with advanced HCC is less than 5 months [4, 5]. The high morbidity and mortality were attributed to diagnosis executed at an advanced stage [6]. The five-year survival rate is still less than 30% among patients subjected to hepatectomy [7]. Surgical resection, transarterial chemoembolization (TACE), tumor ablation, and liver transplantation are current treatment modalities and Sorafenib is the only drug approved by FDA [8, 9]. Unfortunately, only TACE and the drug Sorafenib have been

shown to provide a survival benefit for patients with advanced HCC (stage II-III) [10, 11]. Scutellariae Barbatae Herba (SBH) is originated from the dried entire plant of Scutellaria Barbata D. Don in the Labiatae family [12], which is natively distributed throughout Korea and southern China [13]. The herb is well renowned in Traditional Chinese Medicine (TCM) as Ban-Zhi-Lian and has been used for hundreds of years in Asian countries. Conducted by TCM theory, SBH possesses effects of heatclearing, detoxifying, removing blood stasis, and diuretic swelling and has been utilized for treating boils, swollen poison, sore throat, and venomous snake bites for thousands of years in China [14]. Moreover, SBH has also been used for treating primary liver cancer, lung cancer, and carcinoma of uterine cervix, and it is also indicated for more types of cancers in combination with other Chinese herbal

2 medicines [15, 16]. Modern pharmacological studies have illuminated that SBH possesses antioxidant [13], anticancer [17], antiangiogenesis, etc. activities [18]. More importantly, studies demonstrated that SBH presents effect-enhancing and toxicity-reducing actions for chemotherapy in Hepatoma H22 tumor-bearing mice and a protective effect against liver damage induced by various hepatotoxins [16, 19]. Therefore, it is worthwhile to elucidate the potential mechanisms of antihepatoma effect. TCM has the characteristics of multiple components, multiple targets, and synergistic effects [20], which lead to some problems such as unclear mechanisms of action and unclear substance bases. Thereby, it is comparatively difficult to detect the accurate mechanisms of TCM through conventional experimental methods systematically and comprehensively [21]. It is necessary to illuminate scientific bases and potential mechanisms of TCM exploring new approaches. Network pharmacology, first proposed by Andrew L Hopkins, is a systematic analytical way based on the interaction network of diseases, genes, protein targets, and drugs [22], which has substantially promoted the mechanistic studies of herbal medicines [23, 24]. Network pharmacology (sometimes also called systems pharmacology) integrates systems biology, omics, and computational biology to reveal the mechanism of drug action from the overall perspective, which possesses integrity, synergy, and dynamic characteristics [25]. The characteristics coincide with those of the holistic theory of TCM. Thus, we employed network pharmacology to dissect the anti-hepatoma of SBH. In this study, we recruited active ingredients of SBH based on Oral Bioavailability (OB) and Drug-Likeness (DL) and predicted respective targets using pharmacophore mapping approach. HCC significant targets were retrieved from two public databases and mapped to predicted targets of active ingredients for SBH to get common targets. The common targets were executed GO and pathway analysis, which revealed the major biological processes and molecular functions and involved pathways during SBH against HCC. Then, the interaction networks were visualized via Cytoscape software, containing the network between common targets and associated interacting proteins and networks among active ingredients, common targets, and pathways. The workflow of the study on SBH against HCC based on network pharmacology was shown in Figure 1.

2. Materials and Methods 2.1. Active Ingredients in SBH. The chemical ingredients were obtained from the Traditional Chinese Medicine Systems Pharmacology Database [26] (TCMSP, http://ibts.hkbu.edu .hk/LSP/tcmsp.php), which provides an analysis platform for studying TCM comprehensively. The active ingredients of OB ≥ 30% and DL ≥ 0.18 were selected for subsequent research referring to the most common criteria by TCMSP database. Eventually, 29 active herbal ingredients were selected for SBH (Table S1).

Evidence-Based Complementary and Alternative Medicine 2.2. Drug Targets for SBH. The PubChem database [27] (https://pubchem.ncbi.nlm.nih.gov/) is a key chemical information resource, which contains the largest collection of publicly available chemical information. The active ingredients were imported into PubChem database and the 3D molecular structures were exported in the form of SDF files with the exception of 6 compounds without relevant 3D molecular structure information in SBH. The targets cannot be successfully predicted in compounds lacking precise structural information, which were removed. Finally, 23 herbal ingredients with 3D molecular structure information were recruited for further research. PharmMapper Server [28] (http://lilab.ecust.edu.cn/pharmmapper/) is a freely accessed database designed to identify potential target candidates for the given probe small molecules using pharmacophore mapping approach. The predicted drug targets of 23 herbal ingredients were obtained from PharmMapper database. The targets with normalized fit score > 0.9 were harvested as potential targets for SBH after discarding duplicate data (Table S2). 2.3. HCC Significant Targets. HCC significant targets were retrieved from OncoDB.HCC [29] (http://oncodb.hcc.ibms .sinica.edu.tw/) and Liverome [30] (http://liverome.kobic.re .kr/index.php). OncoDB.HCC effectively integrates three datasets from public references to provide multidimension view of current HCC studies, as the first comprehensive oncogenomic database for HCC. Significant genes experimentally validated were selected [23]. Liverome is a curated database of liver cancer-related gene signatures, in which the gene signatures were obtained mostly from published microarray, proteomic studies and thoroughly curated by experts. Genes of occurrence frequency > 7 among various gene signatures were elected in Liverome [23]. The duplicated genes were removed from two databases and the corresponding targets were retained (Table S3). The predicted targets of main active ingredients of SBH were mapped to these targets to get common targets, which were regarded as candidate targets of SBH (Table S4). 2.4. Protein-Protein Interaction Data. The associated proteins of candidate targets of SBH were acquired from String [31] (https://string-db.org, ver 10.5), with the organism limited to “Homo sapiens”. String integrates known and forecasted protein-protein interactions and evaluates PPI with confidence score ranges (low confidence: score < 0.4; medium: 0.40.7; high: > 0.7). PPIs with high confidence scores were kept for further study. 2.5. Gene Ontology and Pathway Analysis. The GO biological process and molecular function were dissected via BINGO plug-in of Cytoscape. During this procedure, the significance level was set to 0.01, and organism was selected as Homo sapiens. The pathway analysis was executed by means of Reactome FI plug-in of Cytoscape. 2.6. Network Construction. The interaction networks were established as follows: (1) network between active ingredients and candidate targets of SBH; (2) network between

Evidence-Based Complementary and Alternative Medicine

3

Scutellariae Barbatae Herba

Active Ingredients in SBH

Database of HCC

Drug Targets for SBH

HCC Significant Targets CYP3A4 CDA LRAT

MTAP

UGT1A1 UGT1A6

PLAU

CES2 CES1 CES5A

AURKA

GUSB

SRC

HRSP12

HSPA8

CES3 RBP4 EGFR

CCNA2

HNF4A

RBP1 STRA6

HSPG2

TTR APP

Baicalin

TTR

CEBPB

5490127

Rhamnazin

GSTP1

APCS

GC quercetin

FOXA3

CLR HSP90AA1

TTC12 AHSA1

luteolin

Common Targets

5484202

CA2

ESR1

PPIA

5354503

baicalein

AKR1C2

26213330

AR ALB 188308

PTGES3

FOXA1 NKX3-1

SUGT1P3

BAG5

HSPA8

KRT83 MIF

AR

HSP90AB1

KLK3

DNAJC6

DNAJC3

NCOA3

ESR1 CDKN1B

ADA

rivularin PKMYT1CDK2CCNA2 CDT1

Moslosooflavone

AKT1

JUN SNX12

MRI1

MTAP SRM

ATF2 DUSP1

SNX3

PPIL3

MAPK1

PTPRR PEA15 SRPRB SCO2

PLAU

MYB PTPN7

SUGP1 MBOAT4

VTN VLDLR

GYPB

CYP2E1 GSTT1

PRDX6 PPP3CA

SLC4A1 EPB42

SLC4A2

SERPINE2 SERPINB2

MKNK1

CA1

AHSP HBD

BSG PIN1

ALAS2

HRSP12 PQBP1 RBMS1 MRPL53 NDUFA7

14825644

FOS

DUSP6

FAM49B RBM12

CES1

GSTP1 GSTT2B

ST6GALNAC1 HP

GSTO1

PPIA

PTH

CYP1A1 CYP1B1

NFATC3

GSR

LGMN CYP2R1

Ingredient - Target Network

GC

BUD31

PPP3R1

VDR

SLC25A18

EPHX1 CYP2B6 CYP1A2

NFATC1 NFATC2 PPP3CB

PCMT1 AKR1C4

AKR1C3 DHDH

CTTNCDC42 PXN PTPN1 PIK3CA SHC1

HRAS APOA1 GRB2 KRAS AHSG EGF STAT3 CBL VEGFA TP53 CALM2 APOH PTPN11 CALM3 SLC9A1 PLG KNG1 EGFR ALB PRDM10 HGF TGFA CALM1 IGF1 SERPINE1 CA2 SLC4A4 DNAH8 MMP9 PLAUR

GMPS

APRT

CDC6 CDK1 AURKA CCNB1 BIRC5 MAD2L1 CCNB2 CDC20 BUB1 UBE2C TPX2 DLGAP5 CENPA PTTG1

wogonin 14353376

NCOR1 NRIP1

AKR1C1

CRK BCAR1

SRC

Stigmasterol

AKR1C2

DHRS9

IGF1R NCOA1 CCND1

GAK MAPK1

SRD5A1

NCOA2

DNAJB1

667495 Salvigenin

eriodictyol

C11orf54

TMPRSS2

DNAJB6 STIP1 HSP90AA1

beta-sitosterol

sitosterol CA1 Dinatin

CDC37

HSPA4 TTC31

TTC28 SUGT1

DNAJC5 BAG2

CUBN

LRP2

PPI Network response to estradiol stimulus

response to estrogen stimulus

response to steroid hormone stimulus nitric-oxide synthase regulator activity

response to hormone stimulus response to organic substance

enzyme regulator activity response to endogenous stimulus response to chemical stimulus

regulation of bone resorption regulation of homeostatic process

interspecies interaction between organisms

response to stimulus

regulation of bone remodeling

molecular_function

regulation of biological process regulation of multicellular organismal regulation of tissue remodeling process

carbonate dehydratase activity binding

multi-organism process reproduction

biological regulation

catalytic activity growth

female pregnancy biological_process multicellular organism reproduction reproductive process

hydro-lyase activity

lyase activity lipid binding

reproductive process in a multicellular organism maternal process involved in female pregnancy

developmental growth carbon-oxygen lyase activity

multicellular organismal process reproductive developmental process prostate gland growth developmental process

mammary gland branching involved in pregnancy

steroid binding reproductive structure development ment multicellular organismal development morphogenesis of a branching structure prostate gland development

urogenital system development

branching morphogenesis of a tube tube development system development anatomical structure development branching involved in mammary gland duct morphogenesis anatomical structure morphogenesis morphogenesis of a branching epithelium tube morphogenesis organ development

tissue development gland development tissue morphogenesis mammary mmar gland alveolus development gland morphogenesis organ morphogenesis morphogenesis of an epithelium mammary gland duct morphogenesis epithelial tube morphogenesis epithelium development mammary gland development mammary gland morphogenesis

morphogenesis of an epithelial fold

mammary gland epithelium development

Molecular Function

Pathway Analysis

Biological Process

R-HSA-383280 R-HSA-5654736 R-HSA-69275 R-HSA-2029480 R-HSA-453274 R-HSA-6806667

R-HSA-1475029

R-HSA-1236394

R-HSA-1237112

R-HSA-445144 R-HSA-456926 R-HSA-3371571 R-HSA-437239 R-HSA-3371556

Baicalin 5490127 CLR

R-HSA-5654743 AR

R-HSA-170145

AURKA

14825644

R-HSA-76002

CES1

PPIA AR

HRSP12

R-HSA-1295596

beta-sitosterol

CCNA2 R-HSA-5654741

CA1 R-HSA-8863795

14353376

R-HSA-191650

GC

R-HSA-168249 TTR

rivularin

R-HSA-5654732

R-HSA-8866910

ESR1

CA2 R-HSA-194138

R-HSA-191647

26213330 R-HSA-180292

ALB

CCNA2

R-HSA-3371568

5354503 R-HSA-422475

R-HSA-3371453

CA2

MTAP

667495

R-HSA-5654733

R-HSA-1227986 MTAP

ESR1

R-HSA-4420097

R-HSA-1251932

Stigmasterol R-HSA-8864260

R-HSA-373760 MAPK1

quercetin

HSPA8 R-HSA-3928663

R-HSA-6798695

R-HSA-5654726

wogonin

R-HSA-6798695

GSTP1

EGFR

AKR1C2 Moslosooflavone

R-HSA-5654727

R-HSA-373760

luteolin

R-HSA-1227986

R-HSA-3928663

HSP90AA1

R-HSA-3371568

PPIA

GC

R-HSA-3371453

5484202

GSTP1 R-HSA-8864260

R-HSA-1251932

Dinatin

SRC

R-HSA-5654726

R-HSA-5654727

EGFR

HSP90AA1

eriodictyol

R-HSA-4420097

ALB

R-HSA-191647 PLAU

baicalein

TTR MAPK1

R-HSA-5654733

R-HSA-8866910

PLAU

HSPA8 sitosterol 188308 Salvigenin

R-HSA-180292

R-HSA-168249

R-HSA-5654732

R-HSA-1295596

R-HSA-8863795

SRC R-HSA-422475

R-HSA-191650

Rhamnazin R-HSA-194138

R-HSA-170145

R-HSA-5654741

R-HSA-1237112

R-HSA-3371556


R-HSA-5654743

R-HSA-1475029

R-HSA-1236394

R-HSA-453274


Target - Pathway Network

Ingredient - Target - Pathway Network

Figure 1: Workflow for SBH against HCC.

AURKA

4


MTAP

AURKA

PLAU

HRSP12

HSPA8

SRC

EGFR

CCNA2 TTR Baicalin

5490127

Rhamnazin

GSTP1

GC

quercetin CLR HSP90AA1

luteolin PPIA

CA2

ESR1

5354503

baicalein

5484202 AKR1C2

26213330

AR 188308

sitosterol

CA1 Dinatin

ALB

beta-sitosterol

667495 MAPK1

Salvigenin

Stigmasterol

eriodictyol rivularin Moslosooflavone wogonin 14353376

CES1

14825644

Figure 2: Ingredient-target network. Green arrows represent active ingredients in SBH. Red circles represent common targets between ingredient targets from SBH and HCC significant targets. Edges represent interaction between ingredients and targets.

common targets and associated human proteins that directly or indirectly interacted with candidate targets of SBH; (3) network among active ingredients, common targets, and pathways. The network construction was performed via utilizing visualization software Cytoscape [32] (version 3.2.1).

3. Results and Discussion 3.1. Ingredient - Target Network Analysis. As shown in Figure 2, the ingredient-target network was composed of 44 nodes (23 active ingredient nodes and 21 candidate target nodes) and 78 edges. In the network, a total of 23 active ingredients from Scutellariae Barbatae Herba were derived

from TCMSP database, which was correspondent with the characteristic of multiple components for TCM. Most ingredient nodes were connected with multiple target nodes such as Baicalin, Dinatin, Sitosteryl acetate, etc., which coincided with the characteristic of multiple targets for TCM. We also found that many targets were hit by multiple ingredients. For example, ESR1 was modulated by ten ingredients containing Chrysin-5-methylether, Baicalin, Sitosteryl acetate, Dinatin, baicalein, Salvigenin, Rhamnazin, and so on. CES1 was regulated by multiple components covering 5-hydroxy-7,8dimethoxy-2-(4-methoxyphenyl)chromone, 7-hydroxy-5,8dimethoxy-2-phenyl-chromone, rivularin, and wogonin. The phenomenon implied that active ingredients of SBH might


5 CYP3A4 LRAT

CDA UGT1A1UGT1A6

CES2 CES1

GUSB

CES5A

CES3 RBP4

HNF4A

RBP1 STRA6

HSPG2 APP TTR

CEBPB

APCS FOXA3

TTC12

AHSA1

NKX3-1

PTGES3 BAG2

FOXA1

TTC31

HSPA4

TTC28 SUGT1

DNAJC5

CDC37

SUGT1P3

C11orf54

TMPRSS2

KRT83

DNAJB6 STIP1

MIF

HSP90AA1 BAG5

HSPA8

AR

HSP90AB1

KLK3

DNAJC6

AKR1C2

SRD5A1

PCMT1 NCOA2

DNAJB1 GAK

DHRS9

IGF1R

AKR1C4

NCOA1

DNAJC3

NCOA3

AKR1C3 SRC

CTTN CDC42

ESR1

PKMYT1

ADA

CDC6

STAT3 TP53

AURKA BIRC5

MRI1

CBL

PPIL3

EGFR ATF2

SNX3

DUSP1

FOS

TGFA

DUSP6

MMP9

FAM49B RBM12 MAPK1 PTPRR

PTPN7 SUGP1 ALAS2

MRPL53 NDUFA7

RBMS1

ALB

CALM3

MBOAT4

HGF

CALM1

BSG

SLC4A4

VLDLR

SLC4A2

MKNK1 PIN1

CYP2E1

SLC4A1

ST6GALNAC1 HP

GSTT1

PRDX6 PPP3CA

HBD

CA2

PLAUR

SERPINE2 SERPINB2

CA1

AHSP

SLC9A1

PRDM10

DNAH8

VTN

MYB

SRPRB

PQBP1

IGF1

PLAU

PEA15

HRSP12

CALM2

APOH

KNG1

PLG

SERPINE1

CENPA PTTG1

SCO2

VEGFA PTPN11

SRM UBE2C

AHSG

EGF

MTAP

MAD2L1 CCNB2 CDC20 BUB1 TPX2 DLGAP5

APOA1

GRB2

KRAS

JUN SNX12

CDK1

PIK3CA SHC1

HRAS

APRT

CDT1 CCNB1

PTPN1

AKT1 GMPS

CDK2 CCNA2

DHDH

PXN

NCOR1 NRIP1 CDKN1B

AKR1C1

BCAR1

CRK

CCND1

GSTP1 GSTT2B

GSTO1

VDR

SLC25A18

PPIA GYPB

NFATC1

EPB42

EPHX1 NFATC2 NFATC3

PPP3CB PPP3R1

CYP2B6 CYP1A2

CYP1A1 CYP1B1

PTH GC GSR LGMN

BUD31 CYP2R1 CUBN

LRP2

Figure 3: HCC targets’ PPI network. Purple Diamonds represent associated human proteins that directly or indirectly interacted with common targets. Red circles represent common targets between ingredient targets from SBH and HCC significant targets.

act on these targets synergistically. The network target nodes represented common targets from intersections between ingredient targets from SBH and HCC significant targets, and so Figure 2 not only displayed the relationship between active ingredients and ingredient targets but also reflected the connection for SBH resisting HCC. PharmMapper has been widely applied for computational target identification and can provide the top 300

candidate targets for the query compound by default [33]. The targets with normalized fit score > 0.9 were employed as potential targets in this study. Several potential targets of active ingredients from SBH have been recognized in other studies. For example, baicalein was an indirect CAR activator and interfered with epidermal growth factor receptor (EGFR) signaling [34]. Wedelolactone, apigenin, and luteolin from Wedelia chinensis synergistically disrupted the

6

Evidence-Based Complementary and Alternative Medicine nitric-oxide synthase regulator activity

enzyme regulator activity

molecular_function

binding

carbonate dehydratase activity catalytic activity

lipid binding

lyase activity

hydro-lyase activity

carbon-oxygen lyase activity

steroid binding

Figure 4: Gene Ontology (GO) Molecular Function Analysis for candidate targets of SBH. The yellow nodes indicate significant enrichment of molecular function terms. The larger the yellow node, the more the term enrichment. The darker the color, the smaller the P value (p < 0.01).

AR, HER2/3, and AKT signaling networks and enhanced the therapeutic efficacy of androgen ablation in prostate cancer [35]. Luteolin was observed to decrease sorbitol accumulation in the rat lens under high-sorbitol conditions ex vivo via high inhibitory activity against AR and may be used as natural drugs for treating diabetic complications [36]. Luteolin was also identified as the small-molecule drug during identifying the differentially expressed genes including ESR1 in kidneys undergoing laparoscopic donor nephrectomy [37]. The protein expression of GSTP1 was mainly dominated by AhR pathway and luteolin inhibited the expression of drug-metabolizing enzymes by modulating Nrf2 and AhR pathways [38]. Quercetin downregulated the expression of EGFR and modulated this signal pathway on the liver-induced preneoplastic lesions in rats [39]. On the other hand, quercetin was considered an effective anti-cancer agent against breast cancer, human head and neck squamous carcinoma, prostate cancer, oral cancer, and pancreatic tumor [40–44]. The above literature data indicated the accuracy of target prediction with PharmMapper. 3.2. HCC Targets’ PPI Network Analysis. The PPI network was composed of candidate targets of SBH and associated human proteins that directly or indirectly interacted with those in Figure 3. The network was composed of 200 nodes (21 candidate target nodes and 179 associated target nodes) and 622 edges. The network systematically and thoroughly summarized internal net of SBH response to HCC. The main section of the network covered 13 (61.9%) candidate target nodes and 107 (59.8%) associated target nodes, which might

play the leading role in the process of pharmacological effects for SBH. 3.3. GO and Reactome Analysis. To further excavate the significance of common targets, the GO molecular function and biological process were analysed via BINGO plug-in of Cytoscape. As shown in Figure 4 and Table S5, SBH effected HCC by regulating four principal molecular functions, namely, steroid binding, nitric-oxide synthase regulator activity, carbonate dehydratase activity, and lipid binding. As shown in Figure 5 and Table S6, SBH mainly participated in 20 biological processes containing response to organic substance, response to chemical stimulus, response to estrogen stimulus, multi-organism process, response to steroid hormone stimulus, and so on. The yellow nodes indicated significant enrichment of GO terms. The larger the yellow node, the more the term enrichment. The darker the color, the smaller the P value. The pathway analysis was executed by means of Reactome FI plug-in of Cytoscape. As shown in Table S7, the 21 common targets were involved in 40 Reactome pathways ( p < 0.01). It was found that SBH fought against HCC mainly depending on neutrophil degranulation, L1CAM interactions, signaling by ERBB2, attenuation phase, TFAP2 (AP-2) family regulating transcription of growth factors and their receptors, innate immune system, etc. Then the relevant target-pathway network and ingredient-target-pathway network were constructed with nodes consistent with ingredients, targets, pathways, and edges indicating interactions, respectively, in Figures 6 and 7. The network also indicated


7

response to estradiol stimulus

response to estrogen stimulus

response to steroid hormone stimulus

response to hormone stimulus

response to organic substance

response to endogenous stimulus response to chemical stimulus

regulation of bone resorption regulation of homeostatic process

interspecies interaction between

response to stimulus

organisms regulation of bone remodeling

regulation of biological process regulation of multicellular organismal regulation of tissue remodeling process

multi-organism process reproduction

biological regulation

growth

female pregnancy multicellular organism reproduction

biological_process

reproductive process

reproductive process in a multicellular organism maternal process involved in female

developmental growth

pregnancy multicellular organismal process reproductive developmental process

prostate gland growth developmental process

mammary gland branching involved in pregnancy

reproductive structure development multicellular organismal development morphogenesis of a branching structure

prostate gland development

branching morphogenesis of a tube

tube development system development anatomical structure development urogenital system development

branching involved in mammary gland

anatomical structure morphogenesis

duct morphogenesis morphogenesis of a branching epithelium tube morphogenesis

organ development tissue development gland development mammary mmary gland alveolus development

tissue morphogenesis

gland morphogenesis organ morphogenesis morphogenesis of an epithelium mammary gland duct morphogenesis epithelial tube morphogenesis epithelium development mammary gland development mammary gland morphogenesis

morphogenesis of an epithelial fold

mammary gland epithelium development

Figure 5: Gene Ontology (GO) Biological Process Analysis for candidate targets of SBH. The yellow nodes indicate significant enrichment of biological process terms. The larger the yellow node, the more the term enrichment. The darker the color, the smaller the P value (p < 0.01).

8

Evidence-Based Complementary and Alternative Medicine R-HSA-383280 R-HSA-5654736 R-HSA-69275 R-HSA-2029480 R-HSA-453274 R-HSA-6806667

R-HSA-1475029

R-HSA-1236394

R-HSA-1237112

R-HSA-5654743 AR

R-HSA-170145

AURKA CCNA2

R-HSA-5654741

R-HSA-191650

GC CA2

R-HSA-194138

R-HSA-191647

ALB R-HSA-422475

R-HSA-3371453 MTAP

R-HSA-4420097

R-HSA-1251932

ESR1 HSPA8

R-HSA-3928663

R-HSA-6798695 EGFR

R-HSA-5654727

R-HSA-373760 HSP90AA1 SRC

R-HSA-5654726

R-HSA-1227986 GSTP1

R-HSA-8864260

R-HSA-3371568

PPIA TTR

R-HSA-5654733

MAPK1

R-HSA-8866910

PLAU

R-HSA-180292

R-HSA-168249

R-HSA-5654732

R-HSA-1295596

R-HSA-8863795

R-HSA-3371556


Figure 6: Target-pathway network. Blue hexagons represent enriched Reactome pathways. Red circles represent common targets between ingredient targets from SBH and HCC significant targets.

that SBH possessed multiple components, multiple targets, and multiple pathways against HCC. Previous studies have reported that L1CAM interactions, signaling by ERBB2, attenuation phase, TFAP2 (AP-2) family regulating transcription of growth factors and their receptors, and innate immune system played important roles in HCC [45–49], which was fully in support of the reliability of network analysis prediction. Performing in-depth analysis of the first-ranked signaling pathway, we found that neutrophil degranulation was involved in seven genes in this study, namely, HSPA8, HSP90AA1, GSTP1, TTR, PLAU, MAPK1, PPIA. The roles of these genes in HCC have also been reported in previous literature. High GSTP1 could inhibit cell proliferation by reducing AKT phosphorylation and provide

a better prognosis in hepatocellular carcinoma [50]. The highranking gene MAPK1 was confirmed as an important target involved in hepatocarcinogenesis [51]. The VEGF/VEGFR2 pathway might be associated with HCC recurrence in patients expressing high levels of HSP90AA1/HSPA8 [52]. PPIA regulated cell growth and could serve as a novel marker and therapeutic molecular target for HCC patients [53]. Serum TTR might be useful for predicting the prognosis of HCC patients [54]. The String database was employed to construct an interaction network of all hit genes. Interestingly, HSPA8, HSP90AA1, GSTP1, PLAU, MAPK1, and PPIA formed a network of interactions and TTR was independent of the network in Figure 8. All hit genes were further analysed through Expression Atlas, which provided RNA-seq of coding RNA

Evidence-Based Complementary and Alternative Medicine Baicalin


5490127

CLR

9

14825644

R-HSA-76002

CES1

PPIA AR

HRSP12

R-HSA-1295596

beta-sitosterol R-HSA-8863795

14353376

CA1

R-HSA-168249 TTR

rivularin

R-HSA-5654732

R-HSA-8866910

ESR1

26213330 R-HSA-180292

R-HSA-3371568

CCNA2

5354503 CA2 R-HSA-5654733

667495

R-HSA-1227986 MTAP

Stigmasterol R-HSA-8864260

R-HSA-373760 MAPK1

quercetin R-HSA-5654726

wogonin

R-HSA-6798695

GSTP1

AKR1C2

Moslosooflavone R-HSA-5654727

R-HSA-1251932 EGFR

Dinatin luteolin

R-HSA-3928663

GC

R-HSA-3371453 HSP90AA1

5484202 R-HSA-4420097

eriodictyol

ALB

R-HSA-191647 PLAU

baicalein

HSPA8 sitosterol 188308 Salvigenin

R-HSA-422475

SRC

R-HSA-191650

AURKA

Rhamnazin R-HSA-194138

R-HSA-170145

R-HSA-5654741

R-HSA-1237112

R-HSA-5654743

R-HSA-1475029

R-HSA-1236394

R-HSA-453274


Figure 7: Ingredient-target-pathway network. Green arrows represent active ingredients in SBH. Red circles represent common targets between ingredient targets from SBH and HCC significant targets. Blue hexagons represent enriched Reactome pathways.

TTR

GSTP1

HSP90AA1 HSPA8

MAPK1 PPIA

PLAU

Figure 8: Interaction network of all hit genes.

from tissue samples of 122 human individuals representing 32 different tissues. As shown in Figure 9 and Table S8, GSTP1, PLAU, and MAPK1 rendered lower expression, and HSPA8, HSP90AA1, and PPIA were expressed in various organizations without obvious differences. It was surprising that TTR was highly and specially expressed in the liver tissue. TTR might play a crucial role in SBH against HCC.

analysed through network related tools. We found that TTR was highly and specially expressed in the liver tissue. TTR might play a crucial role in neutrophil degranulation pathway during SBH against HCC. TTR might become a therapeutic target of HCC and further experiments are needed to provide support for our findings. This study provided a systematic view of anti-hepatoma mechanisms of Scutellariae Barbatae Herba from a network-based perspective.

4. Conclusion 23 of 29 active herbal ingredients were determined by OB and DL from TCMSP database; their 3D molecular structures were obtained from PubChem database and respective targets were predicted via PharmMapper Database. HCC significant targets were retrieved from OncoDB.HCC and Liverome, which were mapped to predicted targets of active ingredients of SBH to get 21 common targets regarded as candidate targets of SBH. The 21 common targets were analysed by Cytoscape plug-ins. The results revealed that SBH effected HCC by regulating four principal molecular functions and 20 biological processes. The pathway analysis suggested the 21 common targets were involved in 40 Reactome pathways. The first-ranked signaling pathway and hit genes were further

Data Availability The data used to support the findings of this study are available from Supplementary Materials.

Conflicts of Interest The authors declare that they have no conflicts of interest.

Authors’ Contributions Benjiao Gong and Yanlei Kao contributed equally to this work and are jointly first authors.

10

Evidence-Based Complementary and Alternative Medicine Expression level in TPM 0

vermiform appendix

zone of skin

urinary bladder

tonsil

thyroid gland

testis

stomach

spleen

smooth muscle tissue

small intestine

skeletal muscle tissue

saliva-secreting gland

rectum

prostate gland

placenta

pancreas

ovary

lymph node

lung

liver

kidney

heart

gall bladder

fallopian tube

esophagus

endometrium

duodenum

colon

cerebral cortex

bone marrow

adrenal gland

adipose tissue

2802

TTR HSPA8 HSP90AA1 PPIA GSTP1 MAPK1 PLAU

Figure 9: Hit genes analysed by Expression Atlas. X-axis represents 32 different tissues. Y-axis represents hit genes.

Acknowledgments This work is supported by Shandong Provincial Natural Science Foundation, China (ZR2017PH047).

Supplementary Materials Table S1: 29 active ingredients from TCMSP. Table S2: ingredients in SBH and corresponding targets. Table S3: HCC targets. Table S4: candidate targets of SBH. Table S5: Gene Ontology (GO) Molecular Function Analysis for candidate targets of SBH. Table S6: Gene Ontology (GO) Biological Process Analysis for candidate targets of SBH. Table S7: Reactome analysis for candidate targets of SBH. Table S8: hit genes analysed by Expression Atlas. (Supplementary Materials)

References [1] J. Ferlay, I. Soerjomataram, R. Dikshit et al., “Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012,” International Journal of Cancer, 2014. [2] A. G. Singal and H. B. El-Serag, “Hepatocellular Carcinoma From Epidemiology to Prevention: Translating Knowledge into Practice,” Clinical Gastroenterology and Hepatology, vol. 13, no. 12, pp. 2140–2151, 2015. [3] S. Ashtari, M. A. Pourhoseingholi, A. Sharifian, and M. R. Zali, “Hepatocellular carcinoma in Asia: Prevention strategy and planning,” World Journal of Hepatology, vol. 7, no. 12, pp. 1708– 1717, 2015. [4] J. O. Lee, K. W. Lee, D. Y. Oh et al., “Combination chemotherapy with capecitabine and cisplatin for patients with metastatic hepatocellular carcinoma,” Annals of Oncology, vol. 20, no. 8, pp. 1402–1407, 2009. [5] M. Nakamura, H. Nagano, S. Marubashi et al., “Pilot study of combination chemotherapy of S-1, a novel oral DPD inhibitor,

and interferon-𝛼 for advanced hepatocellular carcinoma with extrahepatic metastasis,” Cancer, vol. 112, no. 8, pp. 1765–1771, 2008. [6] J. D. Yang and L. R. Roberts, “Hepatocellular carcinoma: a global view,” Nature Reviews Gastroenterology & Hepatology, vol. 7, no. 8, pp. 448–458, 2010. [7] E. P. Simard, E. M. Ward, R. Siegel, and A. Jemal, “Cancers with increasing incidence trends in the United States: 1999 through 2008,” CA: A Cancer Journal for Clinicians, vol. 62, no. 2, pp. 118–128, 2012. [8] D. J. Erstad, B. C. Fuchs, and K. K. Tanabe, “Molecular signatures in hepatocellular carcinoma: A step toward rationally designed cancer therapy,” Cancer, vol. 124, no. 15, pp. 3084–3104, 2018. [9] R. Dutta and R. I. Mahato, “Recent advances in hepatocellular carcinoma therapy,” Pharmacology & Therapeutics, vol. 173, pp. 106–117, 2017. [10] Y. K. Lee, S. U. Kim, D. Y. Kim et al., “Prognostic value of 𝛼-fetoprotein and des-𝛾-carboxy prothrombin responses in patients with hepatocellular carcinoma treated with transarterial chemoembolization,” BMC Cancer, vol. 13, no. 1, 2013. [11] A. B. Benson, M. I. D’Angelica, T. A. Abrams et al., “Hepatobiliary Cancers, Version 2.2014,” Journal of the National Comprehensive Cancer Network, vol. 12, no. 8, pp. 1152–1182, 2014. [12] S. Qiao, R. Shi, M. Liu et al., “Simultaneous quantification of flavonoids and phenolic acids in Herba Scutellariae barbatae and its confused plants by high performance liquid chromatography-tandem mass spectrometry,” Food Chemistry, vol. 129, no. 3, pp. 1297–1304, 2011. [13] C.-L. Ye and Q. Huang, “Extraction of polysaccharides from herbal Scutellaria barbata D. Don (Ban-Zhi-Lian) and their antioxidant activity,” Carbohydrate Polymers, vol. 89, no. 4, pp. 1131–1137, 2012.

Evidence-Based Complementary and Alternative Medicine [14] T. Lee, H. Cho, D. Kim, Y. Lee, and C. Kim, “Scutellaria barbata D. Don induces c-fos gene expression in human uterine leiomyomal cells by activating beta2-adrenergic receptors,” International Journal of Gynecological Cancer, vol. 14, no. 3, pp. 526–531, 2004. [15] X. Yin, J. Zhou, Y. Zhang, D. Xing, and C. Jie, “Anticancer activity and mechanism of Scutellaria barbata extract on human lung cancer cell line A549,” Life Sciences, vol. 75, no. 18, pp. 2233– 2244, 2004. [16] Z. Dai, X. Liu, Z. Ji et al., “The effect-enhancing and toxicityreducing action of the extract of Herba Scutellariae Barbatae for chemotherapy in hepatoma H22 tumor-bearing mice,” Journal of Traditional Chinese Medicine, vol. 28, no. 3, pp. 205–210, 2008. [17] X. Yang, Y. Yang, S. Tang et al., “Anti-tumor effect of polysaccharides from Scutellaria barbata d. don on the 95-D xenograft model via inhibition of the C-met pathway,” Journal of Pharmacological Sciences, vol. 125, no. 3, pp. 255–263, 2014. [18] Z.-J. Dai, W.-F. Lu, J. Gao et al., “Anti-angiogenic effect of the total flavonoids in Scutellaria barbata D. Don,” BMC Complementary and Alternative Medicine, vol. 13, article no. 150, 2013. [19] S. Lin, C. Lin, Y. Lin, and C. Chen, “Protective and Therapeutic Effects of Ban-zhi-lian on Hepatotoxin-induced Liver Injuries,” American Journal of Chinese Medicine, vol. 22, no. 01, pp. 29–42, 1994. [20] H. Tang, S. He, X. Zhang et al., “A Network Pharmacology Approach to Uncover the Pharmacological Mechanism of XuanHuSuo Powder on Osteoarthritis,” Evidence-Based Complementary and Alternative Medicine, vol. 2016, Article ID 3246946, 10 pages, 2016. [21] F. Tang, Q. Tang, Y. Tian, Q. Fan, Y. Huang, and X. Tan, “Network pharmacology-based prediction of the active ingredients and potential targets of Mahuang Fuzi Xixin decoction for application to allergic rhinitis,” Journal of Ethnopharmacology, vol. 176, pp. 402–412, 2015. [22] A. L. Hopkins, “Network pharmacology,” Nature Biotechnology, vol. 25, no. 10, pp. 1110-1111, 2007. [23] L. Gao, X. Wang, Y. Niu et al., “Molecular targets of Chinese herbs: a clinical study of hepatoma based on network pharmacology,” Scientific Reports, vol. 6, no. 1, 2016. [24] L. Wang, T. Wu, C. Yang et al., “Network pharmacology-based study on the mechanism of action for herbal medicines in Alzheimer treatment,” Journal of Ethnopharmacology, vol. 196, pp. 281–292, 2017. [25] J. Fang, C. Liu, Q. Wang, P. Lin, and F. Cheng, “In silico polypharmacology of natural products,” Briefings in Bioinformatics, 2017. [26] J. Ru, P. Li, J. Wang et al., “TCMSP: a database of systems pharmacology for drug discovery from herbal medicines,” Journal of Cheminformatics, vol. 6, no. 1, article 13, 2014. [27] S. Kim, P. A. Thiessen, E. E. Bolton et al., “PubChem substance and compound databases,” Nucleic Acids Research, vol. 44, no. 1, pp. D1202–D1213, 2016. [28] X. Wang, Y. Shen, S. Wang et al., “PharmMapper 2017 update: A web server for potential drug target identification with a comprehensive target pharmacophore database,” Nucleic Acids Research, vol. 45, no. 1, pp. W356–W360, 2017. [29] W.-H. Su, C.-C. Chao, S.-H. Yeh, D.-S. Chen, P.-J. Chen, and Y.S. Jou, “OncoDB.HCC: An integrated oncogenomic database of hepatocellular carcinoma revealed aberrant cancer target genes and loci,” Nucleic Acids Research, vol. 35, no. 1, pp. D727–D731, 2007.

11 [30] L. Lee, K. Wang, G. Li et al., “Liverome: a curated database of liver cancer-related gene signatures with self-contained context information,” BMC Genomics, vol. 12, no. Suppl 3, p. S3, 2011. [31] D. Szklarczyk, J. H. Morris, H. Cook et al., “The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible,” Nucleic Acids Research, vol. 45, no. 1, pp. D362–D368, 2017. [32] P. Shannon, A. Markiel, O. Ozier et al., “Cytoscape: a software Environment for integrated models of biomolecular interaction networks,” Genome Research, vol. 13, no. 11, pp. 2498–2504, 2003. [33] X. Liu, S. Ouyang, B. Yu et al., “PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach,” Nucleic Acids Research, vol. 38, no. 2, pp. W609–W614, 2010. [34] A. Carazo Fernández, T. Smutny, L. Hyrsová, K. Berka, and P. Pavek, “Chrysin, baicalein and galangin are indirect activators of the human constitutive androstane receptor (CAR),” Toxicology Letters, vol. 233, no. 2, pp. 68–77, 2015. [35] C. Tsai, S. Tzeng, S. Hsieh et al., “A Standardized Wedelia chinensis Extract Overcomes the Feedback Activation of HER2/3 Signaling upon Androgen-Ablation in Prostate Cancer,” Frontiers in Pharmacology, vol. 8, 2017. [36] S. B. Kim, S. H. Hwang, H.-W. Suh, and S. S. Lim, “Phytochemical analysis of Agrimonia pilosa Ledeb, its antioxidant activity and aldose reductase inhibitory potential,” International Journal of Molecular Sciences, vol. 18, no. 2, article no. 379, 2017. [37] Y. Xu, X. Ma, Y. Lu et al., “Gene Expression Profiling of Human Kidneys Undergoing Laparoscopic Donor Nephrectomy,” JSLS, Journal of the Society of Laparoendoscopic Surgeons, vol. 18, no. 1, pp. 102–109, 2014. [38] T. Zhang, Y. Kimura, S. Jiang, K. Harada, Y. Yamashita, and H. Ashida, “Luteolin modulates expression of drug-metabolizing enzymes through the AhR and Nrf2 pathways in hepatic cells,” Archives of Biochemistry and Biophysics, vol. 557, pp. 36–46, 2014. [39] G. Carrasco-Torres, H. C. Monroy-Ram´ırez, A. A. Mart´ınezGuerra et al., “Quercetin Reverses Rat Liver Preneoplastic Lesions Induced by Chemical Carcinogenesis,” Oxidative Medicine and Cellular Longevity, vol. 2017, Article ID 4674918, 8 pages, 2017. [40] S. Balakrishnan, S. Mukherjee, S. Das et al., “Gold nanoparticles–conjugated quercetin induces apoptosis via inhibition of EGFR/PI3K/Akt–mediated pathway in breast cancer cell lines (MCF-7 and MDA-MB-231),” Cell Biochemistry & Function, vol. 35, no. 4, pp. 217–231, 2017. [41] C.-Y. Chan, C.-H. Lien, M.-F. Lee, and C.-Y. Huang, “Quercetin suppresses cellular migration and invasion in human head and neck squamous cell carcinoma (HNSCC),” BioMedicine (Netherlands), vol. 6, no. 3, pp. 10–15, 2016. [42] F. A. Bhat, G. Sharmila, S. Balakrishnan et al., “Quercetin reverses EGF-induced epithelial to mesenchymal transition and invasiveness in prostate cancer (PC-3) cell line via EGFR/PI3K/Akt pathway,” The Journal of Nutritional Biochemistry, vol. 25, no. 11, pp. 1132–1139, 2014. [43] C.-Y. Huang, C.-Y. Chan, I.-T. Chou, C.-H. Lien, H.-C. Hung, and M.-F. Lee, “Quercetin induces growth arrest through activation of FOXO1 transcription factor in EGFR-overexpressing oral cancer cells,” The Journal of Nutritional Biochemistry, vol. 24, no. 9, pp. 1596–1603, 2013. [44] L. T. Lee, Y. T. Huang, J. J. Hwang et al., “Blockade of the epidermal growth factor receptor tyrosine kinase activity by

12

[45]

[46]

[47]

[48]

[49]

[50]

[51]

[52]

[53]

[54]

Evidence-Based Complementary and Alternative Medicine quercetin and luteolin leads to growth inhibition and apoptosis of pancreatic tumor cells,” Anticancer Reseach, vol. 22, no. 3, pp. 1615–1627, 2002. U. H. Weidle, F. Birzele, and A. Krüger, “Molecular targets and pathways involved in liver metastasis of colorectal cancer,” Clinical & Experimental Metastasis, vol. 32, no. 6, pp. 623–635, 2015. V. P. Seshachalam, K. Sekar, and K. M. Hui, “Insights into the etiology-associated gene regulatory networks in hepatocellular carcinoma from The Cancer Genome Atlas,” Journal of Gastroenterology and Hepatology. A. K. Singh, A. S. Bhadauria, U. Kumar et al., “Novel Indolefused benzo-oxazepines (IFBOs) inhibit invasion of hepatocellular carcinoma by targeting IL-6 mediated JAK2/STAT3 oncogenic signals,” Scientific Reports, vol. 8, no. 1, 2018. T. Hayashi, M. Usui, J. Nishioka, Z. X. Zhang, and K. Suzuki, “Regulation of the human protein C inhibitor gene expression in HepG2 cells: Role of Sp1 and AP2,” Biochemical Journal, vol. 332, no. 2, pp. 573–582, 1998. K. Okazaki, M. Yanagawa, T. Mitsuyama, and K. Uchida, “Recent advances in the concept and pathogenesis of IgG4related disease in the hepato-bilio-pancreatic system,” Gut and Liver, vol. 8, no. 5, pp. 462–470, 2014. X. Liu, N. Tan, H. Liao et al., “High GSTP1 inhibits cell proliferation by reducing Akt phosphorylation and is associated with a better prognosis in hepatocellular carcinoma,” Oncotarget , vol. 9, no. 10, pp. 8957–8971, 2018. Y. Zhang, S. Takahashi, A. Tasaka, T. Yoshima, H. Ochi, and K. Chayama, “Involvement of microRNA-224 in cell proliferation, migration, invasion, and anti-apoptosis in hepatocellular carcinoma,” Journal of Gastroenterology and Hepatology, vol. 28, no. 3, pp. 565–575, 2013. X. Xiang, X. You, and L. Li, “Expression of HSP90AA1/HSPA8 in hepatocellular carcinoma patients with depression,” OncoTargets and Therapy, vol. 11, pp. 3013–3023, 2018. S. Cheng, M. Luo, C. Ding et al., “Downregulation of Peptidylprolyl isomerase A promotes cell death and enhances doxorubicin-induced apoptosis in hepatocellular carcinoma,” Gene, vol. 592, no. 1, pp. 236–244, 2016. T. Shimura, M. Shibata, Y. Kofunato et al., “Clinical significance of serum transthyretin level in patients with hepatocellular carcinoma,” ANZ Journal of Surgery, 2018.

MEDIATORS of

INFLAMMATION

The Scientific World Journal Hindawi Publishing Corporation http://www.hindawi.com www.hindawi.com

Volume 2018 2013

Gastroenterology Research and Practice Hindawi www.hindawi.com

Volume 2018

Journal of

Hindawi www.hindawi.com

Diabetes Research Volume 2018


Volume 2018


Volume 2018

International Journal of

Journal of

Endocrinology

Immunology Research Hindawi www.hindawi.com

Disease Markers


Volume 2018

Volume 2018

Submit your manuscripts at www.hindawi.com BioMed Research International

PPAR Research Hindawi www.hindawi.com


Volume 2018

Volume 2018

Journal of

Obesity

Journal of

Ophthalmology Hindawi www.hindawi.com

Volume 2018


Stem Cells International Hindawi www.hindawi.com

Volume 2018


Volume 2018

Journal of

Oncology Hindawi www.hindawi.com

Volume 2018


Volume 2013

Parkinson’s Disease

Computational and Mathematical Methods in Medicine Hindawi www.hindawi.com

Volume 2018

AIDS

Behavioural Neurology Hindawi www.hindawi.com

Research and Treatment Volume 2018


Volume 2018


Volume 2018

Oxidative Medicine and Cellular Longevity Hindawi www.hindawi.com

Volume 2018