Research ArticleANetworkPharmacologyApproach to Explore theMechanisms ofShugan Jianpi Formula in Liver Fibrosis

Chang Fan ,1 Fu Rong Wu,2 Jia Fu Zhang,1 and Hui Jiang 1,3

1Experimental Center of Clinical Research, �e First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230031,Anhui, China2Department of Pharmacy, �e First Affiliated Hospital of University of Science and Technology of China, Hefei 230001,Anhui, China3Anhui Province Key Laboratory of Chinese Medicinal Formula, Hefei 230031, Anhui, China

Correspondence should be addressed to Hui Jiang; jianghui@ahtcm.edu.cn

Received 18 January 2020; Revised 28 April 2020; Accepted 26 May 2020; Published 13 June 2020

Academic Editor: Silvia Wein

Copyright © 2020 Chang Fan 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.

Purpose. We explored the mechanism of Shugan Jianpi Formula (SGJPF) and its effective components for the treatment of liverfibrosis (LF). Materials and Methods. We collected the active ingredients in SGJPF through the Traditional Chinese MedicineSystems Pharmacology Database and Analysis Platform and screened the effective components by absorption, distribution,metabolism, and excretion. Herb-associated target proteins were predicted and screened based on the Bioinformatics AnalysisTool for Molecular Mechanism of Traditional Chinese Medicine and Search Tool for Interactions of Chemicals databases. LF-associated target proteins were predicted and screened based on the Online Mendelian Inheritance in Man® Database andComparative Toxicogenomics Database. Common genes with LF and herbs were selected, and Cytoscape 3.5.1 software was usedto construct an herb pathway and component-LF common target network.)e Search Tool for the Retrieval of Interacting Genes/Proteins was used to build a protein-protein interaction, and quantitative PCR was used to verify the related target genes. Finally,clusterProfiler was applied for the analysis of Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways.Results. )e pharmacological network contained 252 active compounds (e.g., Astragaloside A, saikosaponin, linoleic acid, andPoria acid A), 84 common target genes, and 94 significant signaling pathways. Among them, interleukin 6 (IL-6), tumor protein 53p53 (TP53), prostaglandin-endoperoxide synthase 2 (PTGS2), AKT1, IL-1β, and the nucleotide-binding and oligomerizationdomain-like receptor and Janus kinase-signal transducer and activator of transcription signaling pathways were selected as thecritical target gene and critical signal pathway, respectively. Conclusion. )e mechanisms of SGJPF in protecting against LFinclude the regulation of multiple targets such as IL-6, TP53, PTGS2, and AKT1. )ese target proteins affect LF through varioussignal transduction pathways.

1. Introduction

Liver fibrosis (LF) is a pathological reaction of the liver tochronic liver injury [1]. Liver injury leads to activation ofhepatic stellate cells (HSCs) and an increase in extracellularmatrix (ECM) synthesis and secretion. A large amount ofECM causes insufficient blood supply to the liver, and someHSCs are transformed into proliferative and contractilemyofibroblasts, followed by the development of LF [2].Without proper intervention, there is a risk that LF willfurther develop into cirrhosis or even liver cancer. Although

LF is reversible, there is no specific drug for the treatment ofhepatic fibrosis. )erefore, it is necessary to identify effectiveclinical treatments for LF.

Traditional Chinese Medicine (TCM) has received anincreasing amount of attention due to its notable effec-tiveness and low side effects. TCM also has special advan-tages in antiliver fibrosis as it is characterized by holisticconcepts and TCM syndrome differentiation and treatment[3]. Shugan Jianpi Formula (SGJPF), a classical TCM for-mula, consists of Baishao (BS, Paeoniae Radix Alba), Baizhu(BZ, Atractylodis Macrocephalae Rhizoma), Banlan Gen

HindawiEvidence-Based Complementary and Alternative MedicineVolume 2020, Article ID 4780383, 13 pageshttps://doi.org/10.1155/2020/4780383

(BLG, Isatidis Radix), Chaihu (CH, Bupleuri Radix), Fuling(FL, Poria), Gancao (GC, Glycyrrhizae Radix et Rhizoma),Huangqi (HQ, Astragali Radix), Yiyi Ren (YYR, CoicisSemen), Yinchen (YC, Artemisiae Scopariae Herba), Zelan(ZeL, Lycopus lucidus Turcz), and Zhuling (ZL, Polyporus)and has been widely used to clinically treat liver diseases.Furthermore, SGJPF can reduce the levels of inflammatoryfactors, inhibit hepatic inflammatory injury, improve thedegree of hepatocyte infiltration, and prevent liver-relatedhistopathological changes; it may also exert antifibrosis ef-fects by directly inhibiting HSC activation [4]. Due to thecomplexity of Chinese herbal formula, it is difficult toidentify the effective mechanism of SGJPF with multi-components, multitargets, and multieffects relying on thetraditional pharmacological evaluation.

With the development of system biology and bio-informatics, analysis based on network pharmacologyprovides new methods that reveal the complex mechanismsof TCM. )e methodologies of network pharmacologyhighlight the paradigm shift from “one drug, one target” to“multicomponent therapeutics, biological network” [5].TCM has the advantages of multiple components and tar-gets, which correspond to the methodologies of networkpharmacology [6]. )us, network pharmacology is desirablefor exploring the mechanisms of TCM.

In this study, to reveal the mechanism of SGJPF in thetreatment of LF, we collected information on targets of activeingredients in SGJPF and targets of LF from several data-bases and performed network construction and topologicalstructural analysis. )e results of this study reveal the un-derlying synergistic mechanisms of SGJPF in treating LF(Figure 1).

2. Materials and Methods

2.1. Building Database of Ingredients. All data on thechemical ingredients of 11 herbs (Baishao, Baizhu, BanlanGen, Chaihu, Fuling, Gancao, Huangqi, Yiyi Ren, Yinchen,Zelan, and Zhuling) were derived from the TCM SystemsPharmacology Database and Analysis Platform (TCMSP).TCMSP provides users with a platform for the analysis ofTCM components, including the identification of activecomponents [7].

2.2. Screening of Active Ingredients. In modern drug dis-covery, early assessment of absorption, distribution, meta-bolism, and excretion (ADME) screening has become anessential process. )e proper use of ADME results can givepreference to those drug candidates that are more likely tohave good pharmacokinetic properties and minimize po-tential drug-drug interactions. In the present work, twoADME-related models, oral bioavailability (OB) and drug-like (DL), were employed to screen the active componentsfrom SGJPF. DL helps to describe the pharmacokinetic andpharmaceutical properties of compounds, such as solubilityand chemical stability [8]. Usually, the selection criterion for“DL” compounds in TCM is 0.18. OB represents the relativeamount of an oral drug that is absorbed into the blood [9].

Because lowOB is the primary reason for the development ofTCMs into therapeutic drugs, it is vital to conduct OBscreening. Based on the literature and suggestions inTCMSP, we selected OB> 30% and DL> 0.18 as thescreening threshold. )e ingredients that meet the abovecriteria are screened out for further analysis. )e networkbetween herbs and active ingredients was visualized byCytoscape 3.5.1 [10].

2.3. LF-Associated Target Prediction. Different genes asso-ciated with LF were collected from Online Mendelian In-heritance in Man (OMIM) (http://www.omim.org/) withkeywords of “liver fibrosis” or “hepatic fibrosis” and theComparative Toxicogenomics Database (CTD) (https://www.ctdbase.org) [11] with the keyword of “fibrosis.” )etwo databases manually curate information about gen-e–disease relationships.

2.4. Herb-Associated Target Prediction. Two databases werecombined to predict relevant targets of active ingredients inSGJPF comprehensively. )e Bioinformatics Analysis Toolfor Molecular mechANism of TCM (BATMAN-TCM)(http://bionet.ncpsb.org/batman-tcm) ranks potential drug-target interactions based on their similarity to known drug-target interactions [12]. )e Search Tool for Interactions ofChemicals (STITCH) database (http://stitch.embl.de/) in-tegrates many sources of experimental andmanually curatedevidence with text-mining information and interactionpredictions [13]. First, the active constituents were enteredinto BATMAN-TCM and STITCH databases. )en, du-plications and unified names were removed from the targetsobtained from the aforementioned two tools.

2.5. Herb-Pathway Network Construction. To analyze themain function of herb-target genes, Kyoto Encyclopedia ofGenes and Genomes (KEGG) pathway analysis was per-formed using clusterProfiler with each herb’s targets. Ofnote, a P value was implemented to determine the statisticalsignificance of the modules. KEGG pathways with P< 0.05are significant signaling pathways. Cytoscape 3.5.1 [10], anopen-source software platform for visualizing complexnetworks, was employed to visualize the network.

2.6. Component-LF Common Target Network. Integratedtargets were matched with the LF-associated target, and theintersecting genes were preserved for further analysis. Acomponent-target network was established to identify thekey target. )en, Cytoscape 3.5.1 was used to visualize thenetwork [10]. Simultaneously, all node degrees of thecomponent-target (C-T) network were calculated.

2.7. Protein-Protein Interaction Network Construction andModule Analysis. )e Search Tool for the Retrieval ofInteracting Genes/Proteins (STRING) (https://string-db.org/) is a database that provides comprehensive informa-tion about interactions between proteins, including

2 Evidence-Based Complementary and Alternative Medicine

prediction and experimental interaction data [14]. In ourstudy, the STRING tool was used to generate protein-proteininteractions (PPIs) among the target, based on interactionswith a combined score ≥0.4. Cytoscape 3.5.1 was used tovisualize the network [10].

2.8. Gene Ontology and Pathway Functional EnrichmentAnalysis. To analyze the main function of the target genes,KEGG pathway analysis was performed using clusterProfilerwith each herb’s targets. Of note, a P value was implementedto determine the statistical significance of the modules.KEGG pathways with P< 0.05 are significant signalingpathways. Cytoscape 3.5.1 was used to visualize the network[10].

2.9. Quantitative PCR Verifies the Effect of SCJPF on GeneExpression. An animal model of liver fibrosis was estab-lished by a single subcutaneous injection of 0.05mL/10 gwith 20% carbon tetrachloride (CCl4) into the back skin ofmice, twice a week for 12 weeks. From the day after injectionwith 20% CCl4, SGJPF (7.80 g/kg) was administered bygavage once a day for 12 weeks. )en, total RNA from theliver was extracted using the EZ-10 Total RNAMini-Prep Kit(B618583; Sangon Biotech Co., Ltd., Shanghai, China) andfirst-strand cDNA was reverse-transcribed and synthesizedusing the ABscript II cDNA First-Strand Synthesis Kit

(RK20400; ABclonal Technology, Woburn, MA, USA). )ereaction system was established by TB Green™ Premix ExTaq™ II (RR820A; TAKARA, Tokyo, Japan), and quanti-tative PCR (qPCR) was performed using the Bio-Rad iQ5Real-Time PCR System (IQ5; Bio-Rad, Hercules, CA, USA).)e primers are shown in Table 1. )e relative expression ofeach target gene was quantified and normalized by 2−ΔΔCT.

2.10. Statistical Analysis. SPSS17.0 software was used fordata analysis and processing, and the results are expressed asmean± standard deviation (SD) (x ± s). One-way analysisof variance (ANOVA) designed by randomized block designwas used to analyze the differences in multiple groups. )eresult was considered statistically significant when P< 0.05.

3. Results

3.1. Component Intersection in SGJPF. In total, 11 types ofherbs in the SGJPF contained 1048 compounds; detailedinformation about all compounds can be visualized in theUpset plot, which demonstrates the quantitative results ofmultiple interactive sets more effectively than the traditionalVenn diagram [15]. As shown in Figure 2, it is noteworthythat >10% of compounds were found in two or more herbs,and different combinations of these herbs may provide thelargest specific effect. For example, palmitic acid was in sixtypes of herbs including Chaihu, Huangqi, Baizhu, Fuling,

Active component

Therapeutic mechanism of liver and spleen for liver fibrosis

Targets of compounds

ADME screening

OB > 0.3 DL > 0.8

Baishao

Baizhu

Banlan Gen

ChaihuFuling

Gancao

Huangqi

Yiyi Ren

Yinchen Zelan

Zhuling

Herbs

Common target

Liver fibrosis

LF-associated targets

Synergistic mechanisms

Go and pathwayanalysis C-T networkHerbs-pathway

Herbs-targetPPI

Figure 1: )e reconstruction pipeline of the network pharmacology approach to explore the mechanisms of SGJPF in liver fibrosis.

Evidence-Based Complementary and Alternative Medicine 3

Yiyi Ren, and Banlan Gen. However, some compounds wereunique to one herb. For instance, 284 components were onlyfound in Chaihu, and 135 compounds were only found inGancao.

3.2.ActiveComponentsof SGJPF. According to the screeningthreshold of OB> 30% and DL> 0.18, 252 active compoundsfrom all compounds were selected as the active componentsof SGJPF including astragaloside A, saikosaponin, linoleicacid, and Poria acid A (Figure 3).

3.3. Herb-Pathway Network. To explore the biologicalfunctions of herb-target genes, the KEGG pathway wasanalyzed, and then the herb-pathway network was recon-structed using Cytoscape 3.5.1. As shown in Figure 4, theherb-pathway network was composed of 11 herbs nodes, 109pathways nodes, and 603 edges. In this research, 109

pathways were retrieved based on 1136 target genes pre-dicted by herbs.)ese findings indicated that SGJPF inhibitsLF through multiple pathways, which is consistent with thecharacteristics of TCM.

3.4. Common Target Prediction. TCM exerts its pharma-cological action through the interaction of various com-ponents and targets.)erefore, in addition to the predictivecomponent, studying the target is also necessary [16].However, searching for targets through the literature istime consuming and labor intensive. In this work, pre-dictive models including BATMAN-TCM and STITCHwere used to predict 5544 targets, which interacted with theactive ingredients. In addition, the OMIM database andCTD were also used to predict 983 targets relating to LF.After merging these genes, 170 common target genes wereacquired.

Table 1: Information of primers for RT-qPCR.

Targeted gene Forward sequence and reverse sequence Product length (bp)

IL-6 F: 5′-TTCACAAGTCGGAGGCTTA-3′ 105R: 5′-CAAGTGCATCATCGTTGTTC-3′

TP53 F: 5′-ACCTGAAGACCAAGAAGGG-3′ 225R: 5′-GACCGGGAGGATTGTGT-3′

AKT1 F: 5′-TAACGGACTTCGGGCTGT-3′ 480R: 5′-TTCTCGTGGTCCTGGTTGT-3′

IL-1β F: 5′-AGTTGACGGACCCCAAA-3′ 424R: 5′-TCTTGTTGATGTGCTGCTG-3′

0100200300400

CHYCHQBSBZFL

YYRZLZeLBLGGC

284

251

135

645842

32272521

9 8 7 7 7 6 5 4 4 4 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 10

50

100

150

200

250

300

Figure 2: Upset chart showing the overlapping ingredients in each herb. )e identified ingredients shared by different herbs are shown inthe top bar chart. )e specific names of each herb are indicated by the solid points below the bar chart.

4 Evidence-Based Complementary and Alternative Medicine

3.5. Compound-LF Common Target Network. To reflect theintrinsic connection among compounds, diseases, and targetgenes, a network among compounds and common targetgenes was reconstructed and visualized. As shown in Figure 5,the compound-target network comprised 301 nodes including131 active ingredient nodes, 170 target genes nodes, and 1181edges. According to Figure 5, we also found that rather than aone-to-one relationship between compounds and genes, acompound could correspond with multiple target genes, justas a gene can correspond with multiple compounds.

3.6. PPI Network. After removing isolated genes, the PPInetwork was delineated using the STRING database. As

shown in Figure 6, the PPI network contained 84 nodes and570 edges. Genes with a high degree of connectivity in thePPI networkmay be potential targets for diseases [17]. Geneswere ranked by degree as shown in Table 2 and Figure 7,suggesting that those with the highest degree may playcrucial roles in the origin and development of LF.

3.7. Biological Function and Pathway Enrichment Analysis ofCore Targets. To explore the biological functions of coretargets, Gene Ontology (GO) and KEGG pathways wereanalyzed. GO analysis revealed that there were 3626 GOterms. Among them, biological processes included positiveregulation of type 2 immune response, chronic

3′-Methoxyglabridin

GlabroneGlepidotin B

Glyasperin C

7-Methoxy-2-methylisoflavone

16alpha-Hydroxydehydrotrametenolicacid

beta-sitosterol

sitosterol

DFV

CH

ZL

Licoarylcoumarin

oleic acid

DBPquercetin

Glyzaglabrin

Jaranol Mairin

DIBP

Gancaonin G

licochalcone aKanzonol W

formononetin

kaempferol

METHYLLINOLEATE

Calycosin

(+)-catechinLopac-I-3766

Isotrifoliol

HQ

(3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol

BZ

Astrapterocarpan

5-Prenylbutein

FA

Artepillin C linolenic acid

EIC

Areapillin

Palbinone

BS isorhamnetin

YC

atractylenolide iii

13-hydroxy-9,11-octadecadienoicacid

Eupalitin

1,7-Dihydroxy-3,9-dimethoxypterocarpene

Skrofulein

Genkwanin

(24S)-24-Propylcholesta-5-ene-3beta-ol

Methyllinolelaidate

poricoic acid C

7,9(11)-dehydropachymicacid

Cerevisterol

ergosta-7,22E-dien-3beta-ol

FL Poricoic acid AEburicoic Acid

Dehydrotumulosicacid

Linoleic

PolyporenicAcid C

3′,4′,5′,3,5,6,7-Heptamethoxyflavone

ZFL

polyporusteroneE

Linoleyl acetate

Licoagroisoflavone

Glabrene

ZINC13521995

SemilicoisoflavoneB

GlycyrinLupiwighteone

Quercetin der.

HMO

Gancaonin B

atractylenolide II

Glabridin

Gancaonin H

Licocoumarone

Sigmoidin-B

Isobavachin

HedysarimcoumestanB

Phaseolinisoflavan

Inflacoumarin A

ZINC519174

Licoisoflavone icos-5-enoic acid

1,3-Dihydroxy-8,9-dimethoxy-[1]benzofuro[3,2-c]chromen-6-one

4′-Methoxyglabridin

Licoricone

7,2′,4′-trihydroxy5-methoxy-3arylcoumarin

8-(6-hydroxy-2-benzofuranyl)-2,2-dimethyl-5-chromenol

Medicarpin

liquiritin

Glycyrol

Glypallichalcone

Gancaonin A

GC

Odoratin1-Methoxyphaseollidin

Glepidotin A

Phaseol Glabranin

Shinflavanone

Kanzonol B

Vestitol

gadelaidic acid Poricoic acid B

Licoisoflavone B

licochalcone G

Isolicoflavonol

naringenin

5,7-dihydroxy-3-(4-methoxyphenyl)-8-(3-methylbut-2-enyl)chromone

Pinocembrin

Eurycarpin A

Sainfuran

methyl(2E,4E)-octadeca-2,4-dienoate

136458-42-9 spathulenol

Dinatin

poriferast-5-en-3beta-ol

Methyl oleate

6-(3-oxoindolin-2-ylidene)indolo[2,1-b]quinazolin-12-one

acacetin

Sinensetin

(-)-taxifolinMethyl vaccenate

3-[(3,5-dimethoxy-4-oxo-1-cyclohexa-2,5-dienylidene)methyl]-2,4-dihydro-1H-pyrrolo[2,1-b]quinazolin-9-one

3-(2-hydroxyphenyl)quinazolin-4-one

(E,E,E,E)-Squalene

EUPATORIN

24-Ethylcholest-4-en-3-one

Ineketone

ZINC03860434

neohesperidin_qtquindoline

Stigmasterol

3-Oleoyl-Sn-Glycerol

CLR

methyl(E)-octadec-8-enoate Methyllinolenate

Indirubin

BLG

YYR

Glyceryltrilinoleate

2-Monoolein

Indigo

Figure 3: Herb-active compounds network. Blue squares represent 11 herbs in SGJPF. Red diamonds represent active compounds in herbs.Edges represent interaction between compounds and herbs.

Evidence-Based Complementary and Alternative Medicine 5

inflammatory response to antigenic stimulus, negativeregulation of receptor biosynthetic process, and positiveregulation of natural killer cell chemotaxis. )e molecularfunctions mainly included the chemokine metabolic process,type II transforming growth factor beta receptor binding,interleukin-12 (IL-12) receptor binding, and toll-like re-ceptor (TLR) binding. )e top 30 significant GO terms areshown in Figure 8(a). KEGG pathway analysis indicated that94 enrichment pathways were screened by P< 0.05, whichwere mainly related to the TLR and nucleotide-bindingoligomerization domain (NOD)-like receptor signalingpathways. )e top 30 pathways are shown in Figure 8(b).

3.8. Effect of SGJPF on the mRNA Expression of IL-6, TumorProtein p53, AKT1, and IL-1β. We chose four target geneswith higher degrees to verify the influence of SGJPF. Asshown in Figure 9, compared with the control group, theexpression levels of IL-6, tumor protein p53 (TP53), AKT1,and IL-1β in the model group were significantly increased,while the expression levels of IL-6, TP53, AKT1, and IL-1β in

the SCJPF group were significantly lower than those in themodel group.

4. Discussion

LF is considered the common pathological process of allchronic liver diseases, with complex formation processesand mechanisms, many influencing factors, and great in-dividual differences. )erefore, the clinical treatment of LFstill faces great challenges [18]. In view of the pathogenesis ofLF, inhibition of HSC proliferation, anti-inflammation, andprotection of hepatocytes are key to the treatment of LF. Ithas been proven that SGJPF has curative effects on LF in-duced by CCl4 in rats [19].

In light of the complexity of the active ingredients inSGJPF and the diversity of potential regulatory targets in thehuman body, we analyzed themain components of SGJPF bynetwork pharmacology. )e holistic and systematic natureof network pharmacology coincides with the holistic view ofTCM and syndrome differentiation. It can use many existingdatabases and pathway and network analyses to explore the

T cell receptorsignalingpathway

B cell receptorsignalingpathway

Allograftrejection

Steroidbiosynthesis

Tryptophanmetabolism

Bilesecretion

Retinolmetabolism

Oocytemeiosis

Linoleic acid metabolism

alpha-Linolenicacid

metabolism

Fatty acid degradation

Arachidonicacid

metabolism

Leishmaniasis

Antigenprocessing

andpresentation

Valine,leucine and isoleucine

degradation

Aldosterone-regulatedsodium

reabsorption

beta-Alaninemetabolism

Drugmetabolism

Chagasdisease

(Americantrypanosomiasis)

Hematopoieticcell lineage

Fc epsilon RI signaling pathway

Metabolicpathways

Asthma

Glioma

Glycolysis / Gluconeogenesis

Propanoatemetabolism

Histidinemetabolism

Toll-likereceptorsignalingpathway

Long-termdepression

Metabolismof

xenobioticsby

cytochromeP450

Calciumsignalingpathway

Pathwaysin cancer

Rheumatoidarthritis

PathogenicEscherichia

coli infection

Adipocytokinesignalingpathway

Shigellosis

Gap junction

Complementand

coagulationcascades

mTORsignalingpathway

GnRHsignalingpathway

Autoimmunethyroiddisease

RIG-I-likereceptorsignalingpathway

ABCtransporters

Focaladhesion

Renin-angiotensinsystem

Type I diabetesmellitus

Wntsignalingpathway

Neurotrophinsignalingpathway

Alzheimer’sdisease

Gastric acid secretion

Progesterone-mediatedoocyte

maturation

Hepatitis C

Steroidhormone

biosynthesis

Graft-versus-hostdisease

Vascularsmoothmuscle

contractionp53

signalingpathway

Salivarysecretion

Small cell lung cancer

Melanoma

Neuroactiveligand-receptor

interaction

Endometrialcancer

CytosolicDNA-sensing

pathway

MAPKsignalingpathway

Insulinsignalingpathway

Cytokine-cytokinereceptor

interaction

Tightjunction

Priondiseases

PancreaticcancerECM-receptor

interactionRenal cell carcinoma

Osteoclastdifferentiation

Dilatedcardiomyopathy

Carbohydratedigestion

andabsorption

Jak-STATsignalingpathway

Chemokinesignalingpathway

Chronicmyeloidleukemia

Toxoplasmosis

Malaria

Apoptosis

ZL

YYR

BZ

ErbBsignalingpathway

YC

ZFL

Adherensjunction

Africantrypanosomiasis

Amoebiasis

Acutemyeloidleukemia

Prostatecancer

Epithelialcell

signaling in Helicobacter

pyloriinfection

Arrhythmogenicright

ventricularcardiomyopathy

(ARVC)

Vibriocholeraeinfection

Thyroidcancer

Phosphatidylinositolsignalingsystem

CH

BLG

VEGFsignalingpathway

NOD-likereceptorsignalingpathway

Fc gamma R-mediated

phagocytosisHypertrophic

cardiomyopathy(HCM)

FLBS

GC

HQ

Non-smallcell lung

cancer

Intestinalimmune

network for IgA

production

Natural killer cell

mediatedcytotoxicity

Galactosemetabolism Leukocyte

transendothelialmigration

TGF-betasignalingpathway

Type II diabetesmellitus

Viralmyocarditis

Ether lipid metabolism

Fat digestion and

absorption Colorectalcancer

Regulation of actin

cytoskeleton

Staphylococcusaureus

infection

Melanogenesis

Non-homologousend-joining

PPARsignalingpathway

Inositolphosphate

metabolism

Bacterialinvasion of

epithelialcells

Figure 4: Herb-pathway network. Blue squares represent 11 herbs in SGJPF. Red circles represent enriched reactome pathways.

6 Evidence-Based Complementary and Alternative Medicine

molecular mechanism of TCM for the treatment of diseases[20]. )en, we constructed the SGJPF-compound target-LFnetwork, and the potential antihepatic fibrosis mechanism ofSGJPF was predicted. Network pharmacological analysis ofSGJPF identified 11 herbs, 252 active compounds, 84common target genes, and 94 significant signal pathwaysregulated by target genes.

Astragaloside, the effective component of Astragalusmembranaceus [21], attenuates LF by inhibiting the ex-pression of proteinase-activated receptor 2 signal trans-duction, and its effect is greatly enhanced in diabetic animals[22]. Astragaloside also inhibits oxidative stress and acti-vation of the p38/MAPK to reduce HSC activation [23].Saikosaponins and linoleic acid in Bupleurum have

protective effects on the liver, and saikosaponins have goodantifibrosis effects, which are related to increasing super-oxide dismutase activity, reducing the production of oxygenfree radicals, and inhibiting activation of the transforminggrowth factor beta 1 signal pathway [24, 25]. Linoleic acidcan inhibit the development of LF by inhibiting the acti-vation or proliferation of HSCs [26]. Poria acid A is an activeingredient present in sputum and a well-known medicinalbacterium with various biological functions such as anti-inflammatory and antitumor effects. Studies have shownthat Poria acid A has inhibitory effects on the formation ofcell fibrosis [27].

According to the common target gene degree, thisstudy predicted that SGJPF treats LF mainly through

LIG4

HMGA2

C5

UTS2

LTA

PF4

XRCC4

PIK3R1

SLC26A6 CTNNB1

AGTSRC

CXCL13 IL13

CDC42

ADRB1

CX3CR1

PIK3CBACTN3CID9841735

ADIPOQ

CID5484202 CYP4F2

CID178323 MAPK9SERPINB3

IDO1

PTK2B IL10

HPS4

GNAS

CHUK

IL2

CD40

IL6

IL18R1

MPO

PFKL

TPMT

MPLPLA2G2D

TBX21 NOS2

CDKN1A

MTHFR

TIMP3ALK

B4GALT1

CXCR1

COX8A

CFTR

HSD17B6

CID10331849

CID6603886

CID145659

IKBKB

CID389001

CID354368

COX1

CID5283628

CID21159006

CID5471852

AKR1C2

CID5280590

CID5284421

CRP

CID9805290

HSPA5

IL5

GC

COX3

AKR1C3

CYP2R1

CYP27B1 CYP3A4

CID11267805

NFKB1

MUC2

CID5472440

RNASE3

CCL24

IFNG

PLA2G2E

CID9927807

CID5320083

CID193679 CID197678

CID162412

CID439246

CID68071

CNR2

CID480873

CID503737

SERPINE1

CID5280378

CID97214 MTTP

CID158311

CID9064

PPP2R1A

ABCB1

CID177149

CID5316760 CID185034

CID480787

CPT1ACID14077830

CID124052

CID10380176

CID336327

AKT1

CID480859

SREBF1

CCR7

CID15228663 CID5280442

ABCC8 ACVRL1

ABCB11

CID222284

ABL1

ENPP1

AKT2

CALM3NR1I2

ACADMCID457801

NR1H4

CID5280794 CID5319879

CID64971 ADH1B

CID44575602

CID12303645

CID6037

COX2

IVD

CID5281617

CID5280448 CID5318869

CID73205 CID188323

CID5317652

CCL3

AKT3

SOCS1

ABCB4

ACSL1

CID10881804 CID5481948 CID5319013

CID636883

CID10090416 CID480780

CID13965473

CID5317478 CID5317479 CID5481234

CID14604078

CID5316900

CID44257530

CID5318585

CID5317300 CID5317777

CID49856081

CID73402

CID10743008

CID10405595

CID5282457

NQO1

FMR1

CNR1

CID15225964

CID6442145 CID14448070

C3

ESR1

CID42607889

ATM

PRKDC

CCL5

EGFR

SOD2

RHOA

ACE

CID5748611 CID11602329

BCL2

CID5280343

CID98912 HTR2A

CD36CID5280863

CID5481949

ACE2F2

VCAM1

PIK3CA

ADRB2

CID3035728

CID5312521

CID5318432

CID928837

DRD2

CID114829

CID480774

CID5318679

CID503731

CID10542808

CID11558452

CID25015742

CID5281654

CID712316 CID3764

CID442411 CID5318998

CID15380912 CID124049 CID5317768

PIK3CD

PDE1BUTS2R

AQP2EGR1

AGER

GAS6

DRD4 CID5318437

HCK CID5281619

CID5281628 CID5317480

CID5281789

ANK2

NFKBIAGJA5

CID11975273

S100A9

SPARC

LEP

EDNRANKX2-1

PIK3R5EDN1 PLCB1

ITGB3

PPARD IGF1

CSF2

CID7057921

CID6782

PAH

COL1A1

IL1B

CID3026 TP53

CID10181133

CID155948 ITGAVPPARGC1B

IL4KCNA5 TNFNOS3

CID5362793

PPARG

CID5364422

CID611001 PPARA

PTGS1 CID5318433

FASLG

CID5460988

AGTR2

CID5280934

CID56668247

CID445639 CID15226717 CID5364432

CID11451146

CID5319706

NOS1

INS

CID5319042 CID188456

PTGS2

CYP1A1

AGTR1

WNT5A

CID5280450

S100A8

PDGFBCID9794468 TGFB1

CID15976101

NR3C1 CID5322095

CID5471851 CID5997 SRD5A1

HIF1A

ALDH2VDR

APOE

CYP2E1

STX1A

FAS

ASPA

PGR

Figure 5: Ingredient-target network. Red circles represent active compounds in SGJPF. )e blue rhombus represents common targetsbetween compounds’ targets from SGJPF and LF significant targets. Edges represent interaction between compounds and targets.

Evidence-Based Complementary and Alternative Medicine 7

targeting IL-6, TP53, PTGS2, and AKT1, suggesting thatSGJPF has multicomponents and multitargets characteris-tics for the treatment of LF [28]. )e abnormal dynamicbalance of cytokine networks is an important pathogenesisin the development and progression of LF. IL-6, a type of Tcell and macrophage, is an important central gene [29].Cytokines are involved in a variety of biological activities. IL-6 not only promotes the occurrence of LF by mediatinginflammation but also participates in the formation of LF bypromoting the synthesis of ECM. Some studies have shownthat the level of serum IL-6 increases with the aggravation ofLF, which is positively correlated with the levels of serumhyaluronic acid, type III procollagen, type IV collagen, andstage of LF, indicating that the increase of IL-6 level can leadto the formation and aggravation of LF [30]. TP53 is atranscription factor that is widely expressed in embryos andtumor tissues. Studies have shown that TP53 plays a reg-ulatory role in muscle cell differentiation, suggesting that therecognition of new target molecules may bring new im-plications for the treatment of LF. Prostaglandin-endoper-oxide synthase 2 gene is generally highly expressed in tissueswith inflammation or tumorigenesis, stimulated by certaincytokines or inflammatory factors, suggesting that it mayplay a key role in cell proliferation and differentiation such aspromoting the proliferation and differentiation of HSCs

[31]. AKT1 is a serine/threonine protein kinase. Serine is animportant amino acid responsible for the synthesis of cellmembrane, muscle tissue, and nerve cells. )reonine pro-motes the synthesis of phospholipids and plays an importantrole in protecting the cell membrane [32]. By autophos-phorylation, AKT1 regulates key signaling pathways such asthe Janus kinase-signal transducer and activator of tran-scription (JAK-STAT), apoptosis, mammalian target ofrapamycin, and mitogen-activated protein kinase (MAPK)signaling pathways [33, 34], regulates apoptosis and relatedproteins, thus affecting the process of LF. IL-1β, a criticalmediator of the inflammatory response, and can promotethe proliferation and differentiation of HSCs in a dose-dependent manner, leading to the occurrence and devel-opment of LF [35, 36]. )rough qPCR, we confirmed thatSCJPF can downregulate the expression of IL-6, TP53,AKT1, and IL-1β in the pathogenesis of LF to play a role inthe treatment of LF.

)e results of KEGG metabolic pathway enrichmentanalysis showed that the TLR, NOD-like receptor, and JAK-STAT signaling pathways were mainly involved in thetreatment of LF with SGJPF. )e TLR signaling pathway iscomplex and has a wide range of effects. Its effect on LF ismainly through TLR4, TLR9, and its signal transductionpathway, especially the signal pathway of myeloid

TNF

NOS2

TPMT

IVD

IL6

ABCB11

CYP2R1

S100A8

NR3C1

ADIPOQ

ACADM

ABCB1

PPARA

CYP3A4

NR1H4

ESR1CYP27B1

ACSL1

ADH1B

ABL1

CYP4F2

CYP1A1

PTGS1

NQO1

PGR

AKR1C3

IFNG

AKR1C2

ALDH2

PLA2G2E

CHUK

NOS1

FASLG

MT-CO2

SPARC

COX8A MT-CO1

SRD5A1

VCAM1

PPARD

AKT1TP53

PPARG

NFKBIA

MPO

HSPA5

ACVRL1

ANK2 IL18R1IL1B

GCALK

PTGS2

HCK

CNR1

VDR

IKBKB

NR1I2

MTTP

S100A9

NOS3

F2

TBX21

NFKB1 DRD4CRP

IL2IGF1 KCNA5

PFKL DRD2CNR2PAH

HTR2APLCB1

EDNRAGJA5

STX1A

ABCC8 IL4

MPL

EDN1

CD40

CSF2

Figure 6: PPI network of common targets. Red circles represent common targets between ingredient targets from SGJPF and LF significanttargets.

8 Evidence-Based Complementary and Alternative Medicine

differentiation factor 88-nuclear factor kappa B (NF-κB)[37, 38]. After recognizing its ligand, TLR initiates thedownstream signal pathway, produces a large number ofinflammatory cytokines, and causes an inflammatory re-sponse, which in turn promotes the activation of HSCs [39].NOD-like receptors, a part of the innate immune response,participates in the disease process of LF mainly through theNF-κB pathway [40]. Activation of the NF-κB pathwaypromotes the release of proinflammatory cytokines, duringthe occurrence of liver inflammation. NLR Family PyrinDomain Containing 3 (NLRP3) inflammatory bodies areactivated, and the expression of NLRP3, apoptosis-associ-ated speck-like protein containing a CARD, and caspase-1 inthe liver increases, which induces pyroptosis, furtherexpanding the inflammatory response and promoting the

Table 2: Common target gene dimension ranking.

Genes DegreeIL6 45TP53 40PTGS2 39AKT1 38TNF 37IL1β 35NFKB1 34F2 34IL4 30NOS3 30IL2 28PPARG 28IGF1 28IFNG 27NOS2 27EDN1 27CSF2 26ESR1 25VCAM1 25NFKBIA 24CRP 24PPARA 24MPO 23NR3C1 23NOS1 22CYP3A4 20FASLG 20CD40 20ADIPOQ 19IKBKB 17ABL1 16PTGS1 16CYP1A1 15CHUK 14TBX21 11S100A8 10AKR1C3 10PGR 10VDR 10NR1I2 9ACSL1 9CNR1 8MT-CO2 9ABCB1 8MPL 8ALDH2 7CYP27B1 7HCK 7S100A9 7EDNRA 6NQO1 6HSPA5 6CYP4F2 5HTR2A 5GC 5CYP2R1 5MT-CO1 4PLCB1 4IL18R1 4ACADM 4PPARD 4

Table 2: Continued.

Genes DegreeMTTP 4NR1H4 4PLA2G2E 4ALK 4DRD2 3DRD4 3ABCB11 3AKR1C2 3CNR2 3ABCC8 3SRD5A1 2SPARC 2COX8A 2IVD 2ACVRL1 2PFKL 1STX1A 1KCNA5 1TPMT 1ANK2 1ADH1B 1PAH 1GJA5 1

10

20

30

40

50

60

70

00 50 100 150 200 250 300 350

All node degrees

Num

ber o

f nod

es

Figure 7: All-node degree analysis reveals specific targets of PPInetwork.

Evidence-Based Complementary and Alternative Medicine 9

Artery smooth muscle contractionCaffeine oxidase activity

CCR1 chemokine receptor bindingCCR5 chemokine receptor binding

Chemokine biosynthetic processChemokine metabolic process

Chronic inflammatory response to antigenic stimulusCXCR3 chemokine receptor binding

Interferon receptor activityInterleukin-12 receptor binding

Monoterpenoid metabolic processNegative regulation of membrane protein ectodomain proteolysis

Negative regulation of myosin-light-chain-phosphatase activityNegative regulation of receptor biosynthetic process

Positive regulation of chemokine biosynthetic processPositive regulation of interferon−alpha biosynthetic process

Positive regulation of interferon-beta biosynthetic processPositive regulation of interleukin-13 production

Positive regulation of isotype switching to IgG isotypesPositive regulation of natural killer cell chemotaxis

Positive regulation of natural killer cell proliferationPositive regulation of type 2 immune response

Receptor−mediated virion attachment to host cellRegulation of calcidiol 1-monooxygenase activity

Regulation of natural killer cell proliferationToll-like receptor binding

Type I interferon biosynthetic processType II transforming growth factor beta receptor binding

Type III transforming growth factor beta receptor bindingVein smooth muscle contraction

18 20 22Enrich factor

GO_domainbiological_processmolecular_function

2e − 05

4e − 05

6e − 05P value

Top 30 of GO enrichment

Diff_gene_count46810

(a)

Adipocytokine signaling pathwayAfrican trypanosomiasis

Allograft rejectionAsthma

Autoimmune thyroid diseaseBladder cancer

Chagas disease (American trypanosomiasis)Chemokine signaling pathway

Chronic myeloid leukemiaColorectal cancer

Cytokine−cytokine receptor interactionEndometrial cancer

Fc epsilon RI signaling pathwayGlioma

Graft-versus-host diseaseHepatitis C

LeishmaniasisLinoleic acid metabolism

MalariaNOD-like receptor signaling pathway

Non-small-cell lung cancerOsteoclast differentiation

Pancreatic cancerProstate cancer

RIG-I-like receptor signaling pathwayToll-like receptor signaling pathway

ToxoplasmosisType I diabetes mellitus

Type II diabetes mellitusVEGF signaling pathway

4.0 4.5 5.0 5.5 6.0Enrich factor

Diff_gene_count

255075

5e−07

1e−06

P value

Top 30 of pathway enrichment

(b)

Figure 8: GO and pathway analysis of the targets of common targets. (a))e top 30 significant GO terms. (b))e top 30 signaling pathways.)e different colors from green to red represent the P value.)e different sizes of the shapes represent the gene count number.)e larger theproportion is, the larger the dot; the redder the dot color, the more significant the P value.

0

1

2

3

IL-6

mRN

A re

lativ

e exp

ress

ion

(n =

3) # #

Control Model SCJPF

∗∗

(a)

0

1

2

3

TP53

mRN

A re

lativ

e exp

ress

ion

(n =

3) # #

Control Model SCJPF

∗∗

(b)

0

1

2

3

AKT

1 m

RNA

rela

tive e

xpre

ssio

n (n

= 3

) # #

Control Model SCJPF

∗∗

(c)

0

1

2

3

IL-1β

mRN

A re

lativ

e exp

ress

ion

(n =

3)

# #

Control Model SCJPF

∗∗

(d)

Figure 9: Change in IL6, TP53, AKT1, and IL-1β mRNA expression was observed by qPCR. (a) Change in IL-6 mRNA expression wasobserved by qPCR. (b) Change in TP53 mRNA expression was observed by qPCR. (c) Change in AKT1 mRNA expression was observed byqPCR. (d) Change in IL-1β mRNA expression was observed by qPCR. ##P< 0.01 compared with the control group, ∗∗P< 0.01 comparedwith the model group.

10 Evidence-Based Complementary and Alternative Medicine

occurrence and development of LF [41]. )e JAK/STATsignaling pathway is widely involved in the processes of cellproliferation, differentiation, apoptosis, and inflammationand is an important signal transduction pathway for manycytokines [42, 43]. It also plays an important role in theformation of LF. Various cytokines such as leptin, interferongamma, and IL-4 activate the intracellular JAK/STATpathway through specific receptors on the HSC membraneand transduction signals into the nucleus, promoting thetranscription and expression of related target genes [44].

)ere are still some limitations in using networkpharmacology to predict the effective components andpotential mechanism of SGJPF. First, when predicting thepotential active components of SGJPF, OB and DL in TCMpowder were used as screening indexes. However, com-pared with the data from other databases (e.g., BATMAN-TCM and TCMID), the screening indicators are different.After consideration, we only retained the data fromTCMSP, but did not rule out the existence of other activeingredients for the treatment of LF. Second, the data ob-tained in this study were based on previous experimentalresults and computer simulation predictions. Although weobtained some action targets and the pathways in whichthese targets are located to provide a direction for follow-up research, the more exact therapeutic mechanism ofSGJPF on LF was not reflected in this study. )erefore, thisstill needs to be studied or verified in detail in follow-upanimal and human experiments to further implement thedevelopment of new drugs and improve clinical medicationthinking.)ird, during the treatment of LF with SGJPF, thedrug components actually absorbed into the patient’s bodyafter oral administration are not necessarily the same as theactive components selected; thus, the effect of drugs on thetarget may also be different, and the effects of other factorsmay have led to deviations in the results. )erefore, toverify the theoretical prediction, further experiments onthe effective components are needed.

5. Conclusion

In conclusion, the mechanism of SGJPF in the treatment ofLF involves a variety of active components, targets, andpathways. In this study, 252 active components, 84 potentialtargets, and 94 related signaling pathways in SGJPF werepredicted. Among them, astragaloside A, saikosaponin,Poria acid A, and other components may act on IL-6, TP53,PTGS2, AKT1, and other targets, affecting TLR, NLR, andJAK/STAT, among other signaling pathways and partici-pating in the inflammatory response, HSC proliferation anddifferentiation, apoptosis, pyroptosis, and other processes.)is appears to be the mechanism underlying the thera-peutic effect of SGJPF on LF, and it broadens the train ofthought for follow-up pharmacological research.

Data Availability

)e datasets used and/or analyzed during the present studyare available from the corresponding author upon reason-able request.

Conflicts of Interest

)e authors declare that they have no conflicts of interestregarding the publication of this paper.

Acknowledgments

)e authors are grateful to Mr. Qiang Fan (Aoji Bio-techCo., Ltd., Shanghai, China) for providing help with dataanalysis.)e authors thank LetPub (http://www.letpub.com)for the linguistic assistance during the preparation of thismanuscript. )is study was financially supported by theNational Natural Science Foundation of China (Grant no.81973648).

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