Evidence-Based Complementary and Alternative Medicine
Volume 2018 (2018), Article ID 2178610, 14 pages
https://doi.org/10.1155/2018/2178610

Research Article

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

Guoming Chen,¹ Chuyao Huang,¹ Yunyun Liu,¹ Tengyu Chen,¹ Ruilan Huang,¹ Miaozhen Liang,¹ Jie Zhang,¹ and Hua Xu²

¹Guangzhou University of Chinese Medicine, Guangzhou, China
²Department of Paediatrics, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China

Correspondence should be addressed to Hua Xu; ekxuhua@126.com

Received 14 July 2018; Revised 26 October 2018; Accepted 26 November 2018; Published 20 December 2018

Academic Editor: Shuang-En Chuang

Copyright © 2018 Guoming Chen 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.

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.

Table 1: Information for 126 chemical ingredients of YCSD.

Mol ID
Molecule Name
OB
DL
Source

MOL004609
Areapillin
48.96
0.41
Yinchen

MOL005573
Genkwanin
37.13
0.24

MOL007274
Skrofulein
30.35
0.3

MOL008039
Isoarcapillin
57.4
0.41

MOL008040
Eupalitin
46.11
0.33

MOL008041
Eupatolitin
42.55
0.37

MOL008043
Capillarisin
57.56
0.31

MOL008045
4'-Methylcapillarisin
72.18
0.35

MOL008046
Demethoxycapillarisin
52.33
0.25

MOL008047
Artepillin A
68.32
0.24

MOL002211
11,14-eicosadienoic acid
39.99
0.2
Fuzi

MOL002388
Delphin_qt
57.76
0.28

MOL002392
Deltoin
46.69
0.37

MOL002393
Demethyldelavaine A
34.52
0.18

MOL002394
Demethyldelavaine B
34.52
0.18

MOL002395
Deoxyandrographolide
56.3
0.31

MOL002397
Karakoline
51.73
0.73

MOL002398
Karanjin
69.56
0.34

MOL002401
Neokadsuranic acid B
43.1
0.85

MOL002406
2,7-Dideacetyl-2,7-dibenzoyl-taxayunnanine F
39.43
0.38

MOL002410
Benzoylnapelline
34.06
0.53

MOL002415
6-Demethyldesoline
51.87
0.66

MOL002416
deoxyaconitine
30.96
0.24

MOL002419
(R)-Norcoclaurine
82.54
0.21

MOL002421
Ignavine
84.08
0.25

MOL002422
Isotalatizidine
50.82
0.73

MOL002423
Jesaconitine
33.41
0.19

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-decahydr
41.52
0.22

MOL002434
Carnosifloside I_qt
38.16
0.8

MOL000538
Hypaconitine
31.39
0.26

MOL002464
1-Monolinolein
37.18
0.3

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-carboxylate
62.52
0.31
Ganjiang

MOL002514
Sexangularetin
62.86
0.3

MOL004898
(E)-3-[3,4-dihydroxy-5-(3-methylbut-2-enyl)phenyl]-1-(2,4-dihydroxyphenyl)prop-2-en-1-one
46.27
0.31

MOL004903
Liquiritin
65.69
0.74

MOL004904
Licopyranocoumarin
80.36
0.65

MOL004905
3,22-Dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid
34.32
0.55

MOL004907
Glyzaglabrin
61.07
0.35

MOL004908
Glabridin
53.25
0.47

MOL004910
Glabranin
52.9
0.31

MOL004911
Glabrene
46.27
0.44

MOL004912
Glabrone
52.51
0.5

MOL004913
1,3-dihydroxy-9-methoxy-6-benzofurano[3,2-c]chromenone
48.14
0.43

MOL004914
1,3-dihydroxy-8,9-dimethoxy-6-benzofurano[3,2-c]chromenone
62.9
0.53

MOL004915
Eurycarpin A
43.28
0.37

MOL004917
Glycyroside
37.25
0.79

MOL004924
(-)-Medicocarpin
40.99
0.95
Gancao

MOL004935
Sigmoidin-B
34.88
0.41

MOL004941
(2R)-7-hydroxy-2-(4-hydroxyphenyl)chroman-4-one
71.12
0.18

MOL004945
(2S)-7-hydroxy-2-(4-hydroxyphenyl)-8-(3-methylbut-2-enyl)chroman-4-one
36.57
0.32

MOL004948
Isoglycyrol
44.7
0.84

MOL004949
Isolicoflavonol
45.17
0.42

MOL004957
HMO
38.37
0.21

MOL004959
1-Methoxyphaseollidin
69.98
0.64

MOL004961
Quercetin der.
46.45
0.33

MOL004966
3'-Hydroxy-4'-O-Methylglabridin
43.71
0.57

MOL000497
licochalcone a
40.79
0.29

MOL004974
3'-Methoxyglabridin
46.16
0.57

MOL004978
2-[(3R)-8,8-dimethyl-3,4-dihydro-2H-pyrano[6,5-f]chromen-3-yl]-5-methoxyphenol
36.21
0.52

MOL004980
Inflacoumarin A
39.71
0.33

MOL004985
icos-5-enoic acid
30.7
0.2

MOL004988
Kanzonol F
32.47
0.89

MOL004989
6-prenylated eriodictyol
39.22
0.41

MOL004990
7,2',4'-trihydroxy—5-methoxy-3—arylcoumarin
83.71
0.27

MOL004991
7-Acetoxy-2-methylisoflavone
38.92
0.26

MOL004993
8-prenylated eriodictyol
53.79
0.4

MOL004996
gadelaidic acid
30.7
0.2

MOL000500
Vestitol
74.66
0.21

MOL005000
Gancaonin G
60.44
0.39

MOL005001
Gancaonin H
50.1
0.78

MOL005003
Licoagrocarpin
58.81
0.58

MOL005007
Glyasperins M
72.67
0.59

MOL005008
Glycyrrhiza flavonol A
41.28
0.6

MOL005012
Licoagroisoflavone
57.28
0.49

MOL005013
18α-hydroxyglycyrrhetic acid
41.16
0.71

MOL005016
Odoratin
49.95
0.3

MOL005017
Phaseol
78.77
0.58

MOL005018
Xambioona
54.85
0.87

MOL001484
Inermine
75.18
0.54

MOL001792
DFV
32.76
0.18

MOL000211
Mairin
55.38
0.78

MOL002311
Glycyrol
90.78
0.67

MOL000239
Jaranol
50.83
0.29

MOL002565
Medicarpin
49.22
0.34

MOL003656
Lupiwighteone
51.64
0.37

MOL003896
7-Methoxy-2-methyl isoflavone
42.56
0.2

MOL000392
Formononetin
69.67
0.21

MOL000417
Calycosin
47.75
0.24

MOL000422
Kaempferol
41.88
0.24

MOL004328
Naringenin
59.29
0.21

MOL004805
(2S)-2-[4-hydroxy-3-(3-methylbut-2-enyl)phenyl]-8,8-dimethyl-2,3-dihydropyrano[2,3-f]chromen-4-one
31.79
0.72

MOL004806
euchrenone
30.29
0.57

MOL004808
Glyasperin B
65.22
0.44

MOL004810
Glyasperin F
75.84
0.54

MOL004811
Glyasperin C
45.56
0.4

MOL004814
Isotrifoliol
31.94
0.42

MOL004815
(E)-1-(2,4-dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one
39.62
0.35

MOL004820
Kanzonol W
50.48
0.52

MOL004824
(2S)-6-(2,4-dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2,3-dihydrofuro[3,2-g]chromen-7-one
60.25
0.63

MOL004827
Semilicoisoflavone B
48.78
0.55

MOL004828
Glepidotin A
44.72
0.35

MOL004829
Glepidotin B
64.46
0.34

MOL004833
Phaseolinisoflavan
32.01
0.45

MOL004835
Glypallichalcone
61.6
0.19

MOL004838
8-(6-hydroxy-2-benzofuranyl)-2,2-dimethyl-5-chromenol
58.44
0.38

MOL004841
Licochalcone B
76.76
0.19

MOL004848
licochalcone G
49.25
0.32

MOL004849
3-(2,4-dihydroxyphenyl)-8-(1,1-dimethylprop-2-enyl)-7-hydroxy-5-methoxy-coumarin
59.62
0.43

MOL004855
Licoricone
63.58
0.47

MOL004856
Gancaonin A
51.08
0.4

MOL004857
Gancaonin B
48.79
0.45

MOL004860
licorice glycoside E
32.89
0.27

MOL004863
3-(3,4-dihydroxyphenyl)-5,7-dihydroxy-8-(3-methylbut-2-enyl)chromone
66.37
0.41

MOL004864
5,7-dihydroxy-3-(4-methoxyphenyl)-8-(3-methylbut-2-enyl)chromone
30.49
0.41

MOL004866
2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-6-(3-methylbut-2-enyl)chromone
44.15
0.41

MOL004879
Glycyrin
52.61
0.47

MOL004882
Licocoumarone
33.21
0.36

MOL004883
Licoisoflavone
41.61
0.42

MOL004884
Licoisoflavone B
38.93
0.55

MOL004885
Licoisoflavanone
52.47
0.54

MOL004891
Shinpterocarpin
80.3
0.73

MOL005020
Dehydroglyasperin C
53.82
0.37

MOL000354
Isorhamnetin
49.6
0.31
Yinchen
Gancao

MOL000358
Beta-sitosterol
36.91
0.75
Yinchen
Ganjiang

MOL000098
Quercetin
46.43
0.28
Yinchen
Gancao

MOL000359
Sitosterol
36.91
0.75
Fuzi
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.

Table 2: Important node of YCSND compound-target network.

Node Name
Node Type
Degree
Betweenness Centrality

Quercetin
Compound
154
0.51220399

Prostaglandin G/H synthase 2
Target
98
0.09990361

Estrogen receptor
Target
80
0.03095638

Calmodulin
Target
77
0.01919701

Heat shock protein HSP 90
Target
74
0.06433216

Nitric oxide synthase, inducible
Target
74
0.01539794

Androgen receptor
Target
73
0.03326497

Kaempferol
Compound
63
0.10360504

Trypsin-1
Target
63
0.02571407

Cell division protein kinase 2
Target
62
0.00804666

Glycogen synthase kinase-3 beta
Target
61
0.0087541

Peroxisome proliferator-activated receptor gamma
Target
61
0.02602051

Proto-oncogene serine/threonine-protein kinase Pim-1
Target
61
0.00731391

Estrogen receptor beta
Target
60
0.00760087

Coagulation factor Xa
Target
55
0.01878226

Cyclin-A2
Target
53
0.00546001

Nuclear receptor coactivator 2
Target
52
0.05181668

Prostaglandin G/H synthase 1
Target
52
0.04921123

Sodium channel protein type 5 subunit alpha
Target
51
0.02323399

Dipeptidyl peptidase IV
Target
45
0.02031966

7-Methoxy-2-methyl isoflavone
Compound
43
0.0274993

Formononetin
Compound
39
0.0438877

beta-sitosterol
Compound
38
0.06644929

Thrombin
Target
38
0.01452236

Isorhamnetin
Compound
37
0.03169965

Naringenin
Compound
37
0.1201062

Medicarpin
Compound
34
0.02391904

mRNA of PKA Catalytic Subunit C-alpha
Target
34
0.03269624

licochalcone a
Compound
32
0.02913105

2-[(3R)-8,8-dimethyl-3,4-dihydro-2H-pyrano[6,5-f]chromen-3-yl]-5-methoxyphenol
Compound
31
0.00718614

Beta-2 adrenergic receptor
Target
31
0.01440293

DNA topoisomerase II
Target
31
0.00830063

shinpterocarpin
Compound
30
0.01309466

Vestitol
Compound
30
0.00877921

Licoagrocarpin
Compound
29
0.00658159

Retinoic acid receptor RXR-alpha
Target
28
0.01008831

Glypallichalcone
Compound
27
0.00712433

Glyasperins M
Compound
26
0.00616704

Acetylcholinesterase
Target
24
0.00976155

Coagulation factor VII
Target
22
0.00700284

Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit, gamma isoform
Target
17
0.01365074

Potassium voltage-gated channel subfamily H member 2
Target
16
0.00643202

Nitric-oxide synthase, endothelial
Target
14
0.00565094

Demethoxycapillarisin
Compound
13
0.01972483

Figure 1: The compound-target network of YCSND.

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).

Figure 2: The PPI network of YCSND (the larger the node, the deeper the color, representing the greater the degree of the node).

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.

Table 3: Gene ontology term, cellular component, direct (Top 10).

GOTERM - CC - DIRECT
Count
P value

extracellular space
66

1.2×10⁻¹⁹

Cytosol
94

1.3×10⁻¹²

plasma membrane
103

1.3×10⁻¹⁰

membrane raft
19

6.0×10⁻¹⁰

external side of plasma membrane
18

7.2×10⁻⁹

extracellular exosome
75

1.3×10⁻⁸

extracellular region
52

1.5×10⁻⁸

Caveola
11

2.0×10⁻⁸

endoplasmic reticulum membrane
34

1.2×10⁻⁷

endoplasmic reticulum
33

1.5×10⁻⁷

Table 4: Gene ontology term, molecular function, direct (Top 10).

GOTERM - MF – DIRECT
Count
P value

enzyme binding
49

1.4×10⁻³³

protein heterodimerization activity
38

4.9×10⁻¹⁷

drug binding
18

8.2×10⁻¹⁶

protein homodimerization activity
45

1.6×10⁻¹⁵

identical protein binding
42

3.9×10⁻¹³

RNA polymerase II transcription factor activity, ligand-activated sequence-specific DNA binding
12

2.7×10⁻¹²

transcription factor binding
25

6.7×10⁻¹²

protein kinase binding
28

1.4×10⁻¹¹

protein binding
183

2.0×10⁻¹¹

steroid hormone receptor activity
13

2.8×10⁻¹¹

Table 5: Gene ontology term, biological process, direct (Top 10).

GOTERM - BP - DIRECT
Count
P value

response to drug
40

2.2×10⁻²⁵

positive regulation of transcription from RNA polymerase II promoter
58

2.0×10⁻¹⁹

response to ethanol
22

3.6×10⁻¹⁸

positive regulation of gene expression
30

2.6×10⁻¹⁷

aging
24

3.8×10⁻¹⁶

positive regulation of transcription, DNA-templated
38

1.5×10⁻¹⁵

xenobiotic glucuronidation
9

1.9×10⁻¹⁴

negative regulation of the fatty acid metabolic process
9

1.9×10⁻¹⁴

response to estradiol
18

2.2×10⁻¹⁴

positive regulation of nitric oxide biosynthetic process
14

3.2×10⁻¹⁴

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).

Table 6: KEGG pathway enrichment (Top 10).

KEGG Pathway
Count
P value

Pathways in cancer
60

3.7×10⁻²⁵

Hepatitis B
39

4.0×10⁻²⁵

Pancreatic cancer
28

4.2×10⁻²⁴

Prostate cancer
29

3.2×10⁻²¹

TNF signaling pathway
29

8.7×10⁻¹⁹

Bladder cancer
20

2.3×10⁻¹⁸

Chagas disease (American trypanosomiasis)
27

7.2×10⁻¹⁷

Toxoplasmosis
28

2.0×10⁻¹⁶

Nonsmall cell lung cancer
20

2.7×10⁻¹⁵

Hepatitis C
28

5.4×10⁻¹⁵

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 [10–12]. 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 [31–33]. 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)

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