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

Authors: Guoming Chen; Chuyao Huang; Yunyun Liu; Tengyu Chen; Ruilan Huang; Miaozhen Liang; Jie Zhang; Hua Xu
Journal: Evidence-Based Complementary and Alternative Medicine
 (2018)

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## 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.


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## Body

## 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.


## 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 Table1, 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 YinchenGancao MOL000358 Beta-sitosterol 36.91 0.75 YinchenGanjiang MOL000098 Quercetin 46.43 0.28 YinchenGancao MOL000359 Sitosterol 36.91 0.75 FuziGanjiangGancao


### 3.2. Constructing the Network of Compound-Target

After importing data into Cytoscape 3.5.1, compound-target network (Figure1) 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 0.01972483Figure 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 (Figure2). 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 Tables3, 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  1.5×10−7Table 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  2.8×10−11Table 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 (Table6).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


## 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 Table1, 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 YinchenGancao MOL000358 Beta-sitosterol 36.91 0.75 YinchenGanjiang MOL000098 Quercetin 46.43 0.28 YinchenGancao MOL000359 Sitosterol 36.91 0.75 FuziGanjiangGancao


## 3.2. Constructing the Network of Compound-Target

After importing data into Cytoscape 3.5.1, compound-target network (Figure1) 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 0.01972483Figure 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 (Figure2). 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 Tables3, 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  1.5×10−7Table 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  2.8×10−11Table 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 (Table6).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.



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*Source: 2178610-2018-12-20.xml*