Evidence-Based Complementary and Alternative Medicine

Evidence-Based Complementary and Alternative Medicine / 2018 / Article

Research Article | Open Access

Volume 2018 |Article ID 2178610 | https://doi.org/10.1155/2018/2178610

Guoming Chen, Chuyao Huang, Yunyun Liu, Tengyu Chen, Ruilan Huang, Miaozhen Liang, Jie Zhang, Hua Xu, "A Network Pharmacology Approach to Uncover the Potential Mechanism of Yinchensini Decoction", Evidence-Based Complementary and Alternative Medicine, vol. 2018, Article ID 2178610, 14 pages, 2018. https://doi.org/10.1155/2018/2178610

A Network Pharmacology Approach to Uncover the Potential Mechanism of Yinchensini Decoction

Academic Editor: Shuang-En Chuang
Received14 Jul 2018
Revised26 Oct 2018
Accepted26 Nov 2018
Published20 Dec 2018

Abstract

Objective. To predict and explore the potential mechanism of Yinchensini decoction (YCSND) based on systemic pharmacology. Method. TCMSP database was searched for the active constituents and related target proteins of YCSND. Cytoscape 3.5.1 was used to construct the active ingredient-target interaction of YCSND and network topology analysis, with STRING online database for protein-protein interaction (PPI) network construction and analysis; and collection from the UniProt database of target protein gene name, with the DAVID database for the gene ontology (GO) functional analysis, KEGG pathway enrichment analysis mechanism and targets of YCSND. Results. The results indicate the core compounds of YCSND, namely, kaempferol, 7-Methoxy-2-methyl isoflavone, and formononetin. And its core targets are prostaglandin G/H synthase 2, estrogen receptor, Calmodulin, heat shock protein HSP 90, etc. PPI network analysis shows that the key components of the active ingredients of YCSND are JUN, TP53, MARK1, RELA, MYC, and so on. The results of the GO analysis demonstrate that extracellular space, cytosol, and plasma membrane are the main cellular components of YCSND. Its molecular functions are mainly acting on enzyme binding, protein heterodimerization activity, and drug binding. The biological process of YCSND is focused on response to drug, positive regulation of transcription from RNA polymerase II promoter, the response to ethanol, etc. KEGG results suggest that the pathways, including pathways in cancer, hepatitis B, and pancreatic cancer, play a key role in YCSND. Conclusion. YCSND exerts its drug effect through various signaling pathways and acts on kinds of targets. By system pharmacology, the potential role of drugs and the mechanism of action can be well predicted.

1. Introduction

Yinchensini decoction (YCSND) is a classical traditional Chinese medicine (TCM) prescription which has originated and been in usage since Song dynasty. YCSND is composed of four Chinese medicinal herbs, namely, Artemisiae scopariae herba (Yinchen), Radix aconiti Carmichael (Fuzi), Rhizoma zingiberis (Ganjiang), and Liquorice (Gancao). Pharmacologic studies have shown that Yinchen and Gancao have an effect on protecting liver and opposing hepatitis virus and also have the cholagogue action and anti-inflammatory effect. Then, Fuzi has effects in enhancing immunity, combating inflammation, and easing pain, and Ganjiang plays a part in protecting the liver, benefitting the bile, improving blood circulation, and stopping vomiting [1, 2]. Moreover, a preexisting experimental study has shown that YCSND has been extensively put into clinical use for the treatment of Yin jaundice and liver diseases [3]. Chinese herbal compound prescription is composed of many different compounds with various structures and functions, and it is unscientific that a specific effective chemical compound contains its entire medicinal value. Many components act on its mechanism through multiple targets instead of a specific target. As far as known, experimental studies of YCSND have been reported, but the specific mechanism is not fully clear. Thus, to illustrate its mechanism more systematically and comprehensively, this research intends to analyze and expound the potential molecular mechanism of YCSND based on system pharmacology. As an emerging discipline, systems pharmacology includes many disciplines such as systems biology, pharmacology, computational biology, and network analysis, which to a great degree break the traditional framework (drug-target-disease)[4]. Constructing a multilevel network (disease-phenotype-gene-drug) and exploring the correlation between drugs and disease from the perspective of the whole, which has the characteristics of wholeness and systematicness, correspond with the principle of a holistic view and dialectical treatment of TCM.

Therefore, based on the characteristics and methods of system pharmacology, the analysis of the existing data and the collation of target points and their chemical molecules are carried out. Through analyzing the potential interaction between the various target points, the network of target points is constructed. Then the analysis of associated pathological pathways and the summary of the potential mechanism of YCSND are achieved.

2. Materials and Methods

2.1. Constructing Database of Candidate Compounds

In the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (http://lsp.nwu.edu.cn/tcmsp.php,TCMSP), five constituents of Yinchensini decoction are retrieved. A total of 106 compounds are achieved. Each candidate’s druggability was analyzed according to its oral bioavailability (OB) and drug-likeness (DL) indices recommended by TCMSP. OB refers to the degree and speed of absorbing drugs into the circulatory system, which is an important indicator to evaluate the intrinsic quality of drugs objectively. The higher the OB of the compound is, the more likely the compound is to be developed for clinical application. DL is the sum of the pharmacokinetic properties and safety, which comes from the interactions of physicochemical properties and structural factors, including solubility, permeability, and stability. It can be used to optimize compounds, analyze the results of drug activity, predict in vivo pharmacokinetics, direct structure modifications, etc. As TCMSP recommends, the molecules with OB≥30% and DL≥0.18 were considered to exhibit relatively better pharmacologically and were screened out as candidate compounds for further analysis.

2.2. Constructing the Network of Compound-Target

To comprehensively understand the molecular mechanisms, the compound-target networks were constructed using Cytoscape visualization software 3.5.1. All the candidate compounds were retrieved in TSMSP to obtain associated targets. Then, compounds and targets were inputted into the software and compound-target interaction network was carried out. In the process of constructing the network, the layout algorithm (attribute circle layout) was applied. Users can set the geometric position of every node and display visually network topology using color, graphics, symbols, making reasonable arrangements of every node and creating a clear visual effect. Degree and betweenness centrality are two important parameters of the topology structure, which were used to evaluate the essentiality of each target and compound. Therefore, targets and compounds that play a key role in the mechanisms of Yinchensini decoction were revealed and analyzed.

2.3. Conducting PPI Network

Since the chances of proteins achieving assigned functions individually are small, which means proteins involved in the biochemical process in the same cell tend to form macromolecular complexes through the interactions to complete biological functions; so, the exploration of protein interactions and the interaction network is the viral procedure of understanding cellular organization, bioprocess, and functions. In order to better understand protein interactions systematically, associated targets were input to STRING 10.5 (Search Tool for the Retrieval of Interacting Genes/Proteins) to obtain relevant information of protein interaction. STRING is a commonly used system for retrieval or prediction of protein-protein interaction, known interaction, predicted interaction, and others included. The network nodes represent proteins, and edges represent protein-protein associations. Its results are derived from experimental data, literature mining, databases, and bioinformatics projections. The scoring mechanism of the system itself can score the results from different paths, and the higher the score is, the higher the confidence of the protein action information is. To ensure high confidence information, the minimum score was set to the highest confidence as 0.9. Also, disconnected proteins in the network were excluded. Last, PPT network was exported and, according to it, statistics of protein interactions were carried out.

2.4. Gene Ontology (GO) Functional Enrichment Analysis

Gene Ontology (GO) Consortium database is established by Gene Ontology Consortium, which can describe and limit the functions of genes, and is applicable to all species. The object of this study is a group of genes, and if they are directly annotated, the number of functional nodes obtained is large and overlapping, which results in redundancy. Therefore, the data were analyzed by functional enrichment. The method can effectively identify biological processes related to biological phenomena and is useful for obtaining more meaningful gene functional information. GO enrichment analysis was performed using the functional annotation tool of DAVID (Database for Annotation, Visualization, and Integrated Discovery). Before the enrichment, protein names of all targets were converted into corresponding gene names in the website of UniProt, and then gene names were imported into DAVID to acquire GO enrichment analysis.

2.5. KEGG Pathway Enrichment Analysis

KEGG ((Kyoto Encyclopedia of Genes and Genomes) is a database developed by University of Tokyo and Kyoto University, Japan, which provides query path databases. The identification codes converted from UniProt were imported DAVID access database. Next, the pathway enrichment of all protein genes was conducted, and the KEGG pathway annotations were analyzed, to explore biological pathways which related proteins were involved in.

3. Results

3.1. Identification of the Active Compounds in YCSND

Using the TCMSP database, 546 compounds were retrieved: 53 in Yinchen, 65 in Fuzi, 148 in Ganjiang, and 280 in Gancao. With the criteria of OB≥30% and DL≥0.18, 131 chemical ingredients were screened out: 13 in Yinchen, 21 in Fuzi, 5 in Ganjiang, and 92 in Gancao. As shown in Table 1, actually 126 chemical constituents were accepted in our study after taking out the duplicated parts.


Mol IDMolecule NameOBDLSource

MOL004609Areapillin48.960.41Yinchen
MOL005573Genkwanin37.130.24
MOL007274Skrofulein30.350.3
MOL008039Isoarcapillin57.40.41
MOL008040Eupalitin46.110.33
MOL008041Eupatolitin42.550.37
MOL008043Capillarisin57.560.31
MOL0080454'-Methylcapillarisin72.180.35
MOL008046Demethoxycapillarisin52.330.25
MOL008047Artepillin A68.320.24
MOL00221111,14-eicosadienoic acid39.990.2Fuzi
MOL002388Delphin_qt57.760.28
MOL002392Deltoin46.690.37
MOL002393Demethyldelavaine A34.520.18
MOL002394Demethyldelavaine B34.520.18
MOL002395Deoxyandrographolide56.30.31
MOL002397Karakoline51.730.73
MOL002398Karanjin69.560.34
MOL002401Neokadsuranic acid B43.10.85
MOL0024062,7-Dideacetyl-2,7-dibenzoyl-taxayunnanine F39.430.38
MOL002410Benzoylnapelline34.060.53
MOL0024156-Demethyldesoline51.870.66
MOL002416deoxyaconitine30.960.24
MOL002419(R)-Norcoclaurine82.540.21
MOL002421Ignavine84.080.25
MOL002422Isotalatizidine50.820.73
MOL002423Jesaconitine33.410.19
MOL002433(3R,8S,9R,10R,13R,14S,17R)-3-hydroxy-4,4,9,13,14-pentamethyl-17-[(E,2R)-6-methyl-7-[(2R,3R,4S,5S,6R)-3,4,5-trihydroxy-6-[[(2R,3R,4S,5S,6R)-3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxymethyl]oxan-2-yl]oxyhept-5-en-2-yl]-1,2,3,7,8,10,12,15,16,17-decahydr41.520.22
MOL002434Carnosifloside I_qt38.160.8
MOL000538Hypaconitine31.390.26
MOL0024641-Monolinolein37.180.3
MOL002501[(1S)-3-[(E)-but-2-enyl]-2-methyl-4-oxo-1-cyclopent-2-enyl] (1R,3R)-3-[(E)-3-methoxy-2-methyl-3-oxoprop-1-enyl]-2,2-dimethylcyclopropane-1-carboxylate62.520.31Ganjiang
MOL002514Sexangularetin62.860.3
MOL004898(E)-3-[3,4-dihydroxy-5-(3-methylbut-2-enyl)phenyl]-1-(2,4-dihydroxyphenyl)prop-2-en-1-one46.270.31
MOL004903Liquiritin65.690.74
MOL004904Licopyranocoumarin80.360.65
MOL0049053,22-Dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid34.320.55
MOL004907Glyzaglabrin61.070.35
MOL004908Glabridin53.250.47
MOL004910Glabranin52.90.31
MOL004911Glabrene46.270.44
MOL004912Glabrone52.510.5
MOL0049131,3-dihydroxy-9-methoxy-6-benzofurano[3,2-c]chromenone48.140.43
MOL0049141,3-dihydroxy-8,9-dimethoxy-6-benzofurano[3,2-c]chromenone62.90.53
MOL004915Eurycarpin A43.280.37
MOL004917Glycyroside37.250.79
MOL004924(-)-Medicocarpin40.990.95Gancao
MOL004935Sigmoidin-B34.880.41
MOL004941(2R)-7-hydroxy-2-(4-hydroxyphenyl)chroman-4-one71.120.18
MOL004945(2S)-7-hydroxy-2-(4-hydroxyphenyl)-8-(3-methylbut-2-enyl)chroman-4-one36.570.32
MOL004948Isoglycyrol44.70.84
MOL004949Isolicoflavonol45.170.42
MOL004957HMO38.370.21
MOL0049591-Methoxyphaseollidin69.980.64
MOL004961Quercetin der.46.450.33
MOL0049663'-Hydroxy-4'-O-Methylglabridin43.710.57
MOL000497licochalcone a40.790.29
MOL0049743'-Methoxyglabridin46.160.57
MOL0049782-[(3R)-8,8-dimethyl-3,4-dihydro-2H-pyrano[6,5-f]chromen-3-yl]-5-methoxyphenol36.210.52
MOL004980Inflacoumarin A39.710.33
MOL004985icos-5-enoic acid30.70.2
MOL004988Kanzonol F32.470.89
MOL0049896-prenylated eriodictyol39.220.41
MOL0049907,2',4'-trihydroxy—5-methoxy-3—arylcoumarin83.710.27
MOL0049917-Acetoxy-2-methylisoflavone38.920.26
MOL0049938-prenylated eriodictyol53.790.4
MOL004996gadelaidic acid30.70.2
MOL000500Vestitol74.660.21
MOL005000Gancaonin G60.440.39
MOL005001Gancaonin H50.10.78
MOL005003Licoagrocarpin58.810.58
MOL005007Glyasperins M72.670.59
MOL005008Glycyrrhiza flavonol A41.280.6
MOL005012Licoagroisoflavone57.280.49
MOL00501318α-hydroxyglycyrrhetic acid41.160.71
MOL005016Odoratin49.950.3
MOL005017Phaseol78.770.58
MOL005018Xambioona54.850.87
MOL001484Inermine75.180.54
MOL001792DFV32.760.18
MOL000211Mairin55.380.78
MOL002311Glycyrol90.780.67
MOL000239Jaranol50.830.29
MOL002565Medicarpin49.220.34
MOL003656Lupiwighteone51.640.37
MOL0038967-Methoxy-2-methyl isoflavone42.560.2
MOL000392Formononetin69.670.21
MOL000417Calycosin47.750.24
MOL000422Kaempferol41.880.24
MOL004328Naringenin59.290.21
MOL004805(2S)-2-[4-hydroxy-3-(3-methylbut-2-enyl)phenyl]-8,8-dimethyl-2,3-dihydropyrano[2,3-f]chromen-4-one31.790.72
MOL004806euchrenone30.290.57
MOL004808Glyasperin B65.220.44
MOL004810Glyasperin F75.840.54
MOL004811Glyasperin C45.560.4
MOL004814Isotrifoliol31.940.42
MOL004815(E)-1-(2,4-dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one39.620.35
MOL004820Kanzonol W50.480.52
MOL004824(2S)-6-(2,4-dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2,3-dihydrofuro[3,2-g]chromen-7-one60.250.63
MOL004827Semilicoisoflavone B48.780.55
MOL004828Glepidotin A44.720.35
MOL004829Glepidotin B64.460.34
MOL004833Phaseolinisoflavan32.010.45
MOL004835Glypallichalcone61.60.19
MOL0048388-(6-hydroxy-2-benzofuranyl)-2,2-dimethyl-5-chromenol58.440.38
MOL004841Licochalcone B76.760.19
MOL004848licochalcone G49.250.32
MOL0048493-(2,4-dihydroxyphenyl)-8-(1,1-dimethylprop-2-enyl)-7-hydroxy-5-methoxy-coumarin59.620.43
MOL004855Licoricone63.580.47
MOL004856Gancaonin A51.080.4
MOL004857Gancaonin B48.790.45
MOL004860licorice glycoside E32.890.27
MOL0048633-(3,4-dihydroxyphenyl)-5,7-dihydroxy-8-(3-methylbut-2-enyl)chromone66.370.41
MOL0048645,7-dihydroxy-3-(4-methoxyphenyl)-8-(3-methylbut-2-enyl)chromone30.490.41
MOL0048662-(3,4-dihydroxyphenyl)-5,7-dihydroxy-6-(3-methylbut-2-enyl)chromone44.150.41
MOL004879Glycyrin52.610.47
MOL004882Licocoumarone33.210.36
MOL004883Licoisoflavone41.610.42
MOL004884Licoisoflavone B38.930.55
MOL004885Licoisoflavanone52.470.54
MOL004891Shinpterocarpin80.30.73
MOL005020Dehydroglyasperin C53.820.37
MOL000354Isorhamnetin49.60.31Yinchen
Gancao
MOL000358Beta-sitosterol36.910.75Yinchen
Ganjiang
MOL000098Quercetin46.430.28Yinchen
Gancao
MOL000359Sitosterol36.910.75Fuzi
Ganjiang
Gancao

3.2. Constructing the Network of Compound-Target

After importing data into Cytoscape 3.5.1, compound-target network (Figure 1) was constructed. In the network, the orange node (107) represents the YCSND active compound; the blue (138) and purple (112) nodes represent the target protein; the network contains 357 nodes and 1986 edges in total; the degree of a node indicates the number of routes in which the network is connected to the node. The outer blue node is the target which degree is 1; the inside purple node is of the degree more than 2 of the target (communicate orange node). Results of the network topology analysis are as follows: network density (0.031), network heterogeneity (1.603), and shortest paths (127092, 100%). The average degree of nodes is 11.12605, and there are 112 nodes larger than the average degree. The average betweenness centrality of nodes is 0.00529, and there are 52 nodes larger than the average betweenness centrality. The key core nodes (compound or target) are screened based on the topological properties of degree and betweenness centrality of network nodes, as shown in Table 2. It is suggested that the connections between key compounds and target nodes play a pivotal role in the network.


Node NameNode TypeDegreeBetweenness Centrality

QuercetinCompound1540.51220399
Prostaglandin G/H synthase 2Target980.09990361
Estrogen receptorTarget800.03095638
CalmodulinTarget770.01919701
Heat shock protein HSP 90Target740.06433216
Nitric oxide synthase, inducibleTarget740.01539794
Androgen receptorTarget730.03326497
KaempferolCompound630.10360504
Trypsin-1Target630.02571407
Cell division protein kinase 2Target620.00804666
Glycogen synthase kinase-3 betaTarget610.0087541
Peroxisome proliferator-activated receptor gammaTarget610.02602051
Proto-oncogene serine/threonine-protein kinase Pim-1Target610.00731391
Estrogen receptor betaTarget600.00760087
Coagulation factor XaTarget550.01878226
Cyclin-A2Target530.00546001
Nuclear receptor coactivator 2Target520.05181668
Prostaglandin G/H synthase 1Target520.04921123
Sodium channel protein type 5 subunit alphaTarget510.02323399
Dipeptidyl peptidase IVTarget450.02031966
7-Methoxy-2-methyl isoflavoneCompound430.0274993
FormononetinCompound390.0438877
beta-sitosterolCompound380.06644929
ThrombinTarget380.01452236
IsorhamnetinCompound370.03169965
NaringeninCompound370.1201062
MedicarpinCompound340.02391904
mRNA of PKA Catalytic Subunit C-alphaTarget340.03269624
licochalcone aCompound320.02913105
2-[(3R)-8,8-dimethyl-3,4-dihydro-2H-pyrano[6,5-f]chromen-3-yl]-5-methoxyphenolCompound310.00718614
Beta-2 adrenergic receptorTarget310.01440293
DNA topoisomerase IITarget310.00830063
shinpterocarpinCompound300.01309466
VestitolCompound300.00877921
LicoagrocarpinCompound290.00658159
Retinoic acid receptor RXR-alphaTarget280.01008831
GlypallichalconeCompound270.00712433
Glyasperins MCompound260.00616704
AcetylcholinesteraseTarget240.00976155
Coagulation factor VIITarget220.00700284
Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit, gamma isoformTarget170.01365074
Potassium voltage-gated channel subfamily H member 2Target160.00643202
Nitric-oxide synthase, endothelialTarget140.00565094
DemethoxycapillarisinCompound130.01972483

3.3. Analysis of the Targets in the PPI Network

PPI network is conducted to better analyze and understand the mechanisms of YCSND based on the study of protein-protein interactions by using STRING software. A total of 856 interrelations, as well as 179 related targets, are obtained in PPI network after setting the confidence level greater than 0.9 and rejecting the target protein independent of the network. The importance prioritization of key proteins is analyzed according to the degree of the node exported from STRING database. Among them, the JUN value (degree=54) is much higher than that of other protein nodes, which indicates that this protein might play a role of bridge to connect other nodes in PPI network (Figure 2). The PPI network combined scores were showed in supplementary information file (available here).

3.4. Gene Ontology (GO) Functional Enrichment Analysis

GO annotation and enrichment of YCSND target protein genes in three aspects of cell composition (CC), molecular function (MF), and biological process (BP) were carried out through the DAVID database. The enrichment results showed that there were 79 enrichment results in the related items of cell composition, involving extracellular space, cytosol, plasma membrane, and other cell components; 161 enrichment results are related to molecular function, which includes enzyme binding, protein heterodimerization activity, and drug binding; 748 enrichment processes are related to the biological processes which cover the response to drug, positive regulation of transcription from RNA polymerase II promoter, response to ethanol, etc. Each p value of enrichment results was calculated (corrected by using the Bonferroni method, p values < 0.01 were considered to be significantly enriched), ranking p values according to the order from small to large. The top 10 enrichment results are displayed, and details are shown in Tables 3, 4, and 5.


GOTERM - CC - DIRECTCountP value

extracellular space661.2×10−19
Cytosol941.3×10−12
plasma membrane1031.3×10−10
membrane raft196.0×10−10
external side of plasma membrane187.2×10−9
extracellular exosome751.3×10−8
extracellular region521.5×10−8
Caveola112.0×10−8
endoplasmic reticulum membrane341.2×10−7
endoplasmic reticulum331.5×10−7


GOTERM - MF – DIRECTCountP value

enzyme binding491.4×10−33
protein heterodimerization activity384.9×10−17
drug binding188.2×10−16
protein homodimerization activity451.6×10−15
identical protein binding423.9×10−13
RNA polymerase II transcription factor activity, ligand-activated sequence-specific DNA binding122.7×10−12
transcription factor binding256.7×10−12
protein kinase binding281.4×10−11
protein binding1832.0×10−11
steroid hormone receptor activity132.8×10−11


GOTERM - BP - DIRECTCountP value

response to drug402.2×10−25
positive regulation of transcription from RNA polymerase II promoter582.0×10−19
response to ethanol223.6×10−18
positive regulation of gene expression302.6×10−17
aging243.8×10−16
positive regulation of transcription, DNA-templated381.5×10−15
xenobiotic glucuronidation91.9×10−14
negative regulation of the fatty acid metabolic process91.9×10−14
response to estradiol182.2×10−14
positive regulation of nitric oxide biosynthetic process143.2×10−14

3.5. KEGG Pathway Enrichment Analysis

The related pathway of YCSND was obtained by KEGG pathway enrichment analysis through the DAVID database. 135 pathways are enriched, and each p value of enrichment results was calculated (corrected by using the Bonferroni method, p values < 0.01 were considered to be significantly enriched). After sorting the p values, the top 10 are analyzed (Table 6).


KEGG PathwayCountP value

Pathways in cancer603.7×10−25
Hepatitis B394.0×10−25
Pancreatic cancer284.2×10−24
Prostate cancer293.2×10−21
TNF signaling pathway298.7×10−19
Bladder cancer202.3×10−18
Chagas disease (American trypanosomiasis)277.2×10−17
Toxoplasmosis282.0×10−16
Nonsmall cell lung cancer202.7×10−15
Hepatitis C285.4×10−15

4. Discussion

Yinchensini decoction, first recorded in Song dynasty, which can warm Yang to improve jaundice as well as excrete excess water, is used for hepatitis, jaundice, biliary atresia, liver cancer etc. Based on the theory of traditional Chinese medicine, the main indications of Yinchensini decoction includes Yin jaundice, cold extremities, sweating, and drop in blood pressure. As a new subject, network pharmacology can build a component-target network, combine with the proven literature to demonstrate the mechanism of prescription on diseases, and predict the possible mechanism. As the Yinchensini decoction implements its efficacy through multiple targets and multiple approaches, we used network pharmacology to break the limitation of single pharmacology study and explored the mechanism of Yinchensini decoction for its indications more comprehensively. Through algorithm statistics for GO enrichment and KEGG pathway enrichment, we found that the important components of Yinchensini decoction, such as quercetin and kaempferol, may act on key targets, including JUN, RELA, and IL-6, ultimately improving jaundice, inhibiting inflammatory factors, promoting cell proliferation, and regulating RNA polymerase II transcription factor activity via TNF signaling pathway, bladder cancer, and other pathways.

In vitro, quercetin can not only inhibit the proliferation and apoptosis of many tumor cells through multiple signaling pathways, such as the Wnt signaling pathway (cholangiocarcinoma) and the JNK signaling pathway, but enhance the sensitivity of other anticancer drugs and reverse the drug resistance of tumor cells [5]. What is more, Quercetin has all kinds of pharmacological activities such as antioxidant and hepatoprotective effects [6]. It is reported that quercetin can attenuate oxidative stress in alcohol-induced liver disease via heme oxygenase-1 restoration, decreased lipid oxidation, and diminished ROS generation [7].

Prostaglandin G/H synthase 2(PTGS2/COX-2) is closely tied with cancer [8]. Meanwhile, COX-2, as an inflammatory factor, can cause inflammation and oxidative stress injury. COX-2, involved in prostaglandin synthesis, can be detected in several liver pathologies [9]. It is known that liver ischemia-reperfusion injury is common in liver transplantation, shock or acute hemorrhage, with cold limbs and hypotension. Several reports supported that hepatocyte-specific constitutive expression of COX-2 plays a protective role in liver ischemia-reperfusion injury by diminished proinflammatory cytokines (i.e., IL-1β, IL-6, and TNF-α), increased antiapoptosis (i.e., BAX/BCL-2 radio), and activated AKT and AMPK [1012]. Previous studies have suggested that calmodulin is relevant to high-grade serous ovarian cancer [13]. As a high frequency of complications in chronic cholestasis, hypogonadism was observed on the ovary of adult cycling rats with chronic obstructive jaundice, which lead to marked stromal fibrosis and diminished expression of estrogen receptors [14]. Kaempferol and some glycosides of kaempferol have a wide range of pharmacological activities, such as antioxidant, anti-inflammatory, antimicrobial, anticancer, antidiabetic, antiosteoporotic, anxiolytic, analgesic, and antiallergic activities as numerous preclinical studies have shown [15]. Kaempferol is one of the active fractions in Glycosmis pentaphylla (Retz.) DC, which is traditionally used for the treatment of rheumatism, anemia, jaundice, bronchitis, etc. [16].

PPI network analysis shows that score and confidence level of JUN, TP53, FOS, MAPK1, RELA, MYC, MAPK14, MAPK3, EGF, IL6, IL8, and MAPK8 were significantly higher than others. The coding genes of JUN and FOS target protein belong to the immediate early genes of the protooncogenes, which can rapidly be expressed under the stimulation of external factors. The expression products, FOS and JUN, form heterodimer FOS: JUN or homodimer JUN: JUN in a series of the modification process, then combine with the binding sites of activated protein 1(AP -1), and at last have an effect on the expression of target genes [17]. The expression and activity regulation of c-jun is regulated by various protein kinases which play the role of active sites of signaling pathways. C-jun is also involved in the process of tumor cell growth regulation in various growth factors, cytokines and extracellular stimuli [18].

At present, many experimental studies have confirmed that high c-jun expression is highly correlated with the occurrence and prognosis of various malignant tumors [19]. For example, Yang yuewu [20] found that the expression of c-jun in hepatocellular carcinoma (HCC) correlates with HBsAg, AFP, tumor diameter, tumor capsule, tumor vascular invasion and so on, suggesting the c-jun may play an important role in the occurrence and development of liver cancer. RELA is also known as nuclear factor kappa B, a nucleoprotein factor with multidirectional transcriptional regulation effect, which widely exists in various cells of mammals. RELA can activate a variety of related gene transcription and participate in the cell carcinogenesis so that both are confirmedly related to cell growth and apoptosis [21]. A study found that the positive expression rate of the RELA in the tissue of HCC is significantly higher than that of liver tissue adjacent to carcinoma, suggesting that the RELA is closely related to the occurrence of HCC. At the same time, the experiment points out that the expression of the RELA is associated with the malignant degree of tissue of HCC. If cancer tissue becomes worse on differentiation, the expression of RELA will be higher. It can speculate that the RELA could control the transcription of the downstream antiapoptotic gene, thus inhibiting cell apoptosis of liver cancer and causing the proliferation ability of hepatoma carcinoma cell to be strengthened [22].

EGF is an effective mitogenic factor that can stimulate cell division and proliferation in multiple tissues and promote infiltration and metastasis of tumor cells. EGFR is a kind of cell membrane protein kinase receptor which plays a key role in maintaining cell growth and proliferation.

Its excessive activation can spur proliferation and inhibit apoptosis of malignant tumor cells and also can promote tumor metastasis and angiogenesis [23]. The study shows that EGF and EGFR can promote DNA synthesis of HCC by means of the ion channel, signal conduction, and gene expression, thus promoting the occurrence and development of HCC [24]. Jaundice is one of the main symptoms of HCC and YCSND is used to treat Yin jaundice which is a pattern of jaundice in TCM in the clinic. It is speculated that the YCSND can treat jaundice by inhibiting the expression of c-jun, RELA, EGF, and EGFR. In addition, RELA plays an important role in the body's immune and inflammatory response and apoptosis regulation and its excessive activation can cause many kinds of pathophysiological reaction. Abdominal pain, nausea, vomiting, fever, and jaundice can be often seen in acute pancreatitis (AP). And the excessive activation of RELA can raise gene expression related to a variety of inflammatory responses in the occurrence and development process of acute pancreatitis, causing large numbers of cytokines and inflammatory mediators being involved in the inflammatory process of AP [25]. Studies have shown that inflammation of the pancreas can be improved effectively by inhibiting activation of RELA, which can reduce expression of TNF alpha mRNA [26].

IL-6 is one of the most biologically active cytokines and has many biological functions. In recent years, numerous experiments have confirmed that the abnormal expression of IL-6 and its receptor is associated with the pathogenesis of tumor and is related with the diagnosis, prognosis, and treatment of tumor [27]. It is reported that the concentration of IL-6 is significantly higher than normal levels in the patients with bile duct carcinoma (BDC), speculating that IL6 has the diagnostic significance of BDC [28]. At the same time, IL6 has an antitumor effect, which can directly or indirectly enhance the tumor-cytotoxic effect of the natural killer cell and cytotoxic lymphocytes [29]. Jaundice is the primary symptom of BDC and YCSND is used to treat Yin jaundice clinically. Thus YCSND is speculated to treat BDC by enhancing the antitumor effect of IL6. MAPK signaling pathway is one of the important signaling pathways of organisms, which is involved in the physiological processes of the cell, such as inflammatory reaction, cell growth, cell differentiation, cell proliferation, and cell survival. MAPK1 is also called ERK2. c-fos is its downstream target gene, closely related to the tumor malignant transformation and proliferation. In addition, MAPK14 belongs to a stress-induced type of MAPK family and the activated MAPK14 highly link to the expression of the downstream gene, c-myc, and induce transposition of BAX as well as enhance the expression of TNF alpha and induce cell physiological dysfunction and apoptosis by activating the hippocampus [30]. Many Chinese herbal medicines and their derivatives can prevent liver cancer by inhibiting the MAPK signaling pathway, such as Phyllanthus amarus, Benzyl sulforaphane, and Schizocarps plantaginea [3133]. So it can be speculated that YCSND can treat jaundice caused by HCC or AP by inhibiting MAPK signaling pathway which can inhibit inflammation and tumor proliferation.

As the purpose of this paper is to explore how YCSND plays its therapeutic role through its effective components acting on multiple targets and multiple pathways, KEGG were used to enrich pathways. And based on a large number of reported literatures, each link of the signal pathways is depicted in the KEGG database. Therefore, we can analyze the cellular components, biochemical processes, and molecular functions from the enriched pathways to corroborate the GO enriched results.

Hepatitis B is one of the significantly enriched pathways, which composes of many signal pathways and involves in complex biological processes. Double-stranded relaxed circular DNA (RC-DNA) is the main genetic material of hepatitis B virus. After entering the hepatocyte nucleus, RC-DNA is transformed into cccDNA. Then all viral RNAs including the pregenomic RNA (pgRNA) start transcribing through cccDNA, and HBV core and polymerase II are translated [34]. Ren JH [35] found that SIRT3 restricts the transcription of HBV by decreased host RNA polymerase II and transcription factor binding. Thus, YCSND may inhibit reverse transcription of HBV by regulating RNA polymerase II and transcription factors, which reduce the inflammatory response in HBV patients.

It is well known that many proteins are embedded in the surface of cell membrane and endoplasmic reticulum, which mediate biochemical reactions in the body and thus guarantee normal life activities. TNF is mainly produced by activatory mononuclear macrophages and is an important inflammatory factor. Activated TNF binds to its receptors (TNFR1, TNFR2) in the cell membrane resulting in the activation of many genes and initiating NF-kappa B pathway and the MAPK pathway [36]. TNF signaling pathway can mediate the inflammatory immune response together with the positive regulation of mRNA expression of transcription factors (c-fos, c-jun) and the level of inflammatory cytokines etc. [37]. There is a research showing that a large amount of TNF alpha which can be involved in inducing the expression of IL-1, IL-6, IL-8, and its own genes can be released in process of AP, resulting in a large release of cytokines and inflammatory mediators and causing the necrosis of pancreatic tissue [38]. Thus TNF signaling pathway is involved in the occurrence and development of AP as an important proinflammatory cytokine. Jaundice is one of the main symptoms of AP and YCSND is used to treat Yin jaundice. So it can be speculated that YCSND can treat jaundice by inhibiting the expression of TNF signaling pathway. Subsequently, the inflammation of AP is reduced because the generation of inflammatory mediators and cytokines is reduced.

Data Availability

The chemical ingredients of YCSND were extracted from TCMSP platform to support the findings of this study. The important nodes of YCSND compound-target network used to support the findings of this study are included within the article. The PPI network used to rank the importance of targets is performed by using STRING software. The degree of targets was collected from STRING software after setting the confidence level greater than 0.9 and rejecting the target protein independent of the network. The gene ontology (GO) functional enrichment analysis includes TOP10 of composition (CC), molecular function (MF), biological process (BP), and KEGG pathway enrichment used to elaborate the pharmacological mechanism of YCSD, which are included within the article. Besides, the rest of them used to support the findings of this study are included within the supplementary information file(s).

Disclosure

The present address of Hua Xu is The First Affiliated Hospital of Guangzhou University of Chinese Medicine, No. 16, Airport Road, Baiyun District, Guangzhou, Guangdong, China.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This study is supported by the National Natural Science Foundation of China (81373686).

Supplementary Materials

Supplementary 1. PPI combined score. (Supplementary Materials)

Supplementary Materials

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