Exploring the Multicomponent Synergy Mechanism of Yinzhihuang Granule in Inhibiting Inflammation-Cancer Transformation of Hepar Based on Integrated Bioinformatics and Network Pharmacology

Abstract

Background:
The Chinese patent drug Yinzhihuang granule (YZHG) is used to treat hepatitis B. This research is aimed at exploring the multicomponent synergistic mechanism of YZHG in the treatment of inflammation-cancer transformation of hepar and at providing new evidence and insights for its clinical application.

Methods:
To retrieve the components and targets of Yinzhihuang granules. The differentially expressed genes (DEGs) of hepar inflammation-cancer transformation were obtained from TTD, PharmGKB, and GEO databases. Construct the compound-prediction target network and the key module network using Cytoscape 3.7.1.

Results:
The results show that hepatitis B and hepatitis C shared a common target, MMP2. CDK1 and TOP2A may play an important role in the treatment with YZHG in hepatitis B inflammatory cancer transformation. KEGG pathway enrichment showed that key genes of modules 1, 2, and 4 were mainly enriched in the progesterone-mediated oocyte maturation signaling pathway and oocyte meiosis signaling pathway.

Conclusion:
The multicomponent, multitarget, and multichannel pharmacological benefits of YZHG in the therapy of inflammation-cancer transition of hepar are directly demonstrated by network pharmacology, providing a scientific basis for its mechanism.

Full text

Research Article
Exploring the Multicomponent Synergy Mechanism of
Yinzhihuang Granule in Inhibiting Inflammation-Cancer
Transformation of Hepar Based on Integrated Bioinformatics and
Network Pharmacology
Jingyuan Zhang,
1
Zhihong Huang,
1
Xinkui Liu,
1
Chao Wu,
1
Wei Zhou,
1
Peizhi Ye,
2
Antony Stalin,
3
Shan Lu,
1
Yingying Tan,
1
Zhishan Wu,
1
Xiaotian Fan,
1
Xiaomeng Zhang,
1
Miaomiao Wang,
1
Bingbing Li,
4
Guoliang Cheng,
4
Yanfang Mou,
4
and Jiarui Wu
1
1
Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11 of
North Three-Ring East Road, Chao Yang District, Beijing, China
2
National Clinical Research Center for Cancer, Chinese Medicine Department of the Caner Hospital of the Chinese Academy of
Medical Sciences and Peking Union Medical College, Beijing, China
3
State Key Laboratory of Subtropical Silviculture, Department of Traditional Chinese Medicine, Zhejiang University,
Hangzhou, China
4
State Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Linyi, China
Correspondence should be addressed to Jiarui Wu; exogamy@163.com
Received 3 September 2021; Revised 12 February 2022; Accepted 28 February 2022; Published 18 March 2022
Academic Editor: Shibiao Wan
Copyright © 2022 Jingyuan Zhang 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.
Background. The Chinese patent drug Yinzhihuang granule (YZHG) is used to treat hepatitis B. This research is aimed at
exploring the multicomponent synergistic mechanism of YZHG in the treatment of inflammation-cancer transformation of
hepar and at providing new evidence and insights for its clinical application. Methods. To retrieve the components and targets
of Yinzhihuang granules. The differentially expressed genes (DEGs) of hepar inflammation-cancer transformation were
obtained from TTD, PharmGKB, and GEO databases. Construct the compound-prediction target network and the key module
network using Cytoscape 3.7.1. Results. The results show that hepatitis B and hepatitis C shared a common target, MMP2.
CDK1 and TOP2A may play an important role in the treatment with YZHG in hepatitis B inflammatory cancer
transformation. KEGG pathway enrichment showed that key genes of modules 1, 2, and 4 were mainly enriched in the
progesterone-mediated oocyte maturation signaling pathway and oocyte meiosis signaling pathway. Conclusion. The
multicomponent, multitarget, and multichannel pharmacological benefits of YZHG in the therapy of inflammation-cancer
transition of hepar are directly demonstrated by network pharmacology, providing a scientific basis for its mechanism.
1. Introduction
Chronic viral hepatitis affects half of the people in the world,
which may lead to cirrhosis and liver cancer [1]. Liver cancer
is the third leading cause of cancer-related mortality, and
hepatocellular carcinoma (HCC) accounts for 90% of all pri-
mary liver cancers [2–4]. Solid tumors such as HCC are very
complex and have heterogeneous tumor genome maps, lead-
ing to complexity in diagnosis and management. Chronic
hepatitis B virus (HBV) and hepatitis C virus (HCV)
infection is the most significant cause of HCC. Common
mechanisms of HBV- and HCV-induced hepatocarcinogen-
esis include sustained liver inflammation, impaired antiviral
immune response, immune and viral protein-mediated oxi-
dative stress, and regulation of cellular signaling pathways
by viral proteins. Promotion of genomic instability through
Hindawi
BioMed Research International
Volume 2022, Article ID 6213865, 13 pages
https://doi.org/10.1155/2022/6213865

DNA integration is a feature of HBV infection, and meta-
bolic reprogramming leading to steatosis is driven by HCV
infection [5–7]. Inflammation-cancer transformation is a
dynamic process of disease occurrence, development, and
transformation that represents a stage in developing and
transforming many diseases. Chronic hepatitis B- (hepatitis
C) cirrhosis-hepatocellular carcinoma is a common rule of
disease development and transformation in the clinic.
Researchers consider this transformation process a typical
transformation process of inflammation and cancer [8].
YZHG is a preparation of traditional Chinese medicine
made from the extracts of Artemisia scoparza Waldst. et
Kit. or Artemisia capillaris Thunb. (Yinchen, YC), Gardenia
jasminoides Ellis (Zhizi, ZZ), Scutellaria baicalensis Georgi
(Huangqin, HQ), and Lonicera japonica Thunb. (Jinyinhua,
JYH). It has the effects of clearing heat and detoxifying, nor-
malizing the gallbladder to cure jaundice. It is widely used in
neonatal jaundice, viral hepatitis, drug-induced hepatitis,
intrahepatic cholestasis of pregnancy, alcoholic liver disease,
and ABO maternal-fetal blood group incompatibility.
Currently, YZHG is widely used in the treatment of viral
hepatitis in clinic [9, 10]. Based on the treatment of viral
hepatitis with YZHG, this study explored the mechanism
of YZHG in inhibiting the inflammation-cancer transforma-
tion of hepar by integrated bioinformatics [11–14]. The
purpose of the present study is to discuss the disease process
of hepatitis B-hepatitis B-related cirrhosis, hepatitis B-
related cirrhosis-hepatitis B-related hepatocellular
carcinoma, hepatitis C-hepatitis C-related cirrhosis, hepatitis
C-related cirrhosis-hepatitis C-related hepatocellular carci-
noma 4. Due to the lack of available data in the GEO
database, this research included normal group-hepatitis B,
hepatitis B-related hepatocellular carcinoma, hepatitis C,
and hepatitis C-related cirrhosis-hepatitis C-related hepato-
cellular carcinoma 4 groups of data (3 groups of GEO data,
one set of disease database data). The overall flow chart of
this study is shown in Figure 1.
2. Methods
2.1. Genes Involved in the Inflammation-Cancer
Transformation of Hepar. In this study, human genes corre-
lated with advanced hepatitis B, hepatitis C, and associated
hepatocellular carcinoma were obtained from the following
four resources:
(1) The Gene Expression Omnibus database [15] (GEO,
http://www.ncbi.nlm.nih.gov/GEO/) is an interna-
tional public database that can freely store and
obtain high-throughput gene expression and other
functional genomic datasets. The included datasets
meet the following conditions: (1) human liver tissue
samples, including human normal liver tissue sam-
ples and hepatitis samples infected with HBV;
chronic HBV-related HCC adjacent normal tissue
and chronic HBV-related HCC liver tissue; and liver
cirrhosis group and liver cancer tissue samples. (2)
Each dataset contains at least 3 samples. The
“limma”package of R 4.1 software was used to
Gene expression profile data
Network establishment
Module analysis
Enrichment analysis
Molecular docking
Compound-compound target network of YZHG
Association network between drugs (YZHG)
and diseases (4 groups of liver disease genes)
Normal/HBV
(GEO) GSE83148 (GEO) GSE12148 (TTD)/pharmGKB)
DEGs profile of diseases
Drug and disease
association
network
Potential targets analysis
(GO and KEGG)
1 N-HBV 2 HBV-HBV.HCC 3 N-HCV 4 HCV.LC-HCV.HCC
(GEO) GSE17548
HBV/
HBV-related HCC HCV HCV-related LC
/HCV-related HCC
Figure 1: Network pharmacology analysis flow chart of YZHG in
the treatment of advanced hepatitis B, hepatitis C, and related
hepatocellular carcinoma. Firstly, the datasets of four different
disease stages are retrieved in GEO (the missing part is replaced
by the disease database). Analyze the differential genes of each
dataset. Combine the predicted targets of Yinzhihuang granule
components with four groups of disease differential genes, and get
the potential therapeutic targets of four groups of diseases at
different stages. Four groups of key genes were analyzed by the
protein interaction network and enrichment. The key targets are
verified by molecular docking.
2 BioMed Research International

identify DEGs when ∣log 2FC ∣≥1and adj:P<0:05
were considered to be DEGs [16]
(2) The Therapeutic Target Database [17] (TTD, http://
db.idrblab.net/ttd/) is a database containing known
targets, explored therapeutic proteins, disease tar-
gets, nucleic acid targets, pathway information, and
corresponding drugs. The keyword “hepatitis C”
was used to search for hepatitis C-related targets in
TTD
(3) Pharmacogenomics Knowledgebase [18]
(PharmGKB, https://www.pharmgkb.org) is applied
to collect, guide, and disseminate knowledge on
clinical operable gene-drug associations and
genotype-phenotype relationships. The keyword
“hepatitis C”was screened in PharmGKB to obtain
known hepatitis C-related targets
2.2. Active Ingredients and Putative Targets of YZHG. A lit-
erature search in CNKI and PubMed was performed to find
the chemical ingredients of YZHG in this investigation. To
collect their simplified molecular input line entry specifica-
tion (SMILES) information, the obtained compounds were
entered into the PubChem database [19–23] (https://
pubchem.ncbi.nlm.nih.gov). ChemDraw was used to depict
the structures of compounds without the standard informa-
tion from SMILES [24], because the targets of compounds
cannot be properly predicted without correct structural
information.
All compounds’SMILES information was entered into
the SuperPred and SwissTargetPrediction databases search
tools. The server for chemical composition, ATC code, and
target prediction is the SuperPred database (https://
prediction.charite.de). It can anticipate the ATC coding or
target of small molecules and acquire compound
information, which is useful for drug development [25].
SwissTargetPrediction (https://www.swisstargetprediction
.ch/) is a site that uses the reverse screening similarity prin-
ciple to suggest the most likely protein targets for small
compounds [26]. Only human-related targets were chosen
for further study once the matching known or anticipated
targets were collected from the SuperPred and SwissTarget-
Prediction databases. Cytoscape 3.7.1 [27] was used to create
the YZHG compound-putative target network. Three indi-
ces are assigned to each node in the network. Three indices
(important parameters) are assessed for each node in the
network to evaluate its topological features, including
degree, betweenness, and proximity. The number of edges
related to the node is represented by “degree.”The stronger
the relationship between nodes, the greater the degree. The
ratio of the number of the shortest pathways through this
node to the total number of paths through all nodes is called
“betweenness,”and “closeness”is the reciprocal of the sum
of the distances between this node and other nodes. The
higher the node’s three parameters, the more important it
is in the network [28–30].
2.3. YZHG-Inflammation-Cancer Transformation of Hepar
Network Construction. Protein-protein interaction (PPI)
analysis was performed on the hepatitis C data. The 4 groups
of disease data (normal group-hepatitis B DEG data, hepati-
tis B-hepatitis B-related hepatocellular carcinoma DEG data,
hepatitis C protein-protein interaction data, hepatitis C-
related cirrhosis-hepatitis C-related hepatocellular
carcinoma DEG data) and the common targets in the
compound-putative target of the YZHG network were deter-
mined to be the potential targets in the treatment of
inflammation-cancer transformation of the hepar
process [31].
2.4. Construction of PPI and Key Module Network. In this
study, the potential targets of the above 4 groups of YZHG
in the treatment of inflammation-cancer transformation of
the hepar process were entered into the STRING [32]
(https://string-db.org/) database, and the relevant informa-
tion of PPI was retrieved, respectively, with the species
limited to “Homo sapiens”and confidence scores higher than
0.7 (low: <0.4; medium: 0.4 to 0.7; and high: >0.7). The
“CytoHubba”plug-in sorts the nodes in the network
according to the network characteristics and provides 11
topological analysis methods to discover the most important
targets and subnetworks of complex networks [33]. In this
study, Cytoscape was used to visualize the selected modules
and CytoHubba was used to analyze the module network.
The maximum clique centrality (MCC) algorithm was
selected to determine the top 10 genes with the highest score
as key genes.
2.5. Enrichment Analysis of Key Genes. To elucidate the role
of potential targets in gene function and signal transduction
pathways, GO enrichment analysis and KEGG pathway
enrichment analysis of targets in the inflammation-cancer
transformation of the hepar target network were performed
using the g:Profile (https://biit.cs.ut.ee/gprofiler/gost) data-
base [34, 35]. GO enrichment analysis is divided into three
aspects: cellular components, molecular functions, and bio-
logical processes, revealing possible biological processes
associated with key targets. GO and KEGG results were visu-
alized using the “GOplot”package in R software [36].
2.6. Molecular Docking. The small-molecule ligands (mol2
format) of YZHG were downloaded from the PubChem
database (https://pubchem.ncbi.nlm.nih.gov/). They were
processed using AutoDock Tool 1.5.6 software, including
the addition of polar hydrogen atoms and Gasteiger charges,
and then, the corresponding pdbqt format was saved. The
crystal structure of the protein receptor was obtained from
the RCSB PDB (http://www.rcsb.org/) protein database.
Water molecules and original ligands in protein receptors
were removed and saved in pdb format. A script file was cre-
ated containing the three-dimensional coordinates of the
active sites and the number of independent docking calcula-
tions; then, polar hydrogen atoms and Gasteiger charge were
added to the macromolecular receptor using AutoDock Tool
1.5.6 software, and the corresponding pdbqt format was
saved [37, 38]. Finally, molecular docking was performed
using AutoDock Vina, with results set to return 20
3BioMed Research International

conformations, and then, PyMOL (http://www.PyMOL.org)
was used to view and analyze the results.
3. Results
3.1. Identification of Inflammation-Cancer Transformation
of Hepar Genes. The GSE83148 dataset included 6 normal
human liver tissue samples and 122 HBV-infected hepatitis
samples. Supplement Table S1 (Figure 2(a)) shows the
results of difference analysis of the GSE83148 dataset,
including 263 DEGs composed of 83 downregulated genes
and 180 upregulated genes. The GSE121248 dataset
contains 37 chronic hepatitis B-induced HCC adjacent
normal tissues and 70 chronic hepatitis B-induced HCC
liver tissues. Supplement Table S2 (Figure 2(b)) shows the
difference analysis results of the GSE121248 dataset,
including 798 DEGs composed of 559 downregulated genes
and 239 upregulated genes. The GSE17548 dataset
comprises 3 hepatitis C-related cirrhosis samples and 3
hepatitis C-related hepatocellular carcinoma samples.
Supplement Table S4 (Figure 2(d)) shows the results of
difference analysis of the GSE17548 dataset, including 706
DEGs containing 223 upregulated genes and 483
downregulated genes. Around 39 hepatitis C targets were
retrieved in TTD and PharmGKB, and protein-protein
interactions between 72 targets were analyzed using the
STRING database (Supplementary Table S3, Table 1,
Figure 2(c)). The target with the highest degree is PIK3CA.
3.2. Analysis of Active Ingredients and Putative Target
Network of YZHG. A literature search in CNKI and PubMed
identified 25 compounds in YZHG (Supplementary
Table S5) [11]. The components in YZHG may play a
synergistic effect in treating diseases. The network of active
ingredients and putative targets of YZHG was constructed
by using Cytoscape. As shown in Figure 3, the network
consists of 281 nodes (25 active ingredients and 256
putative targets) and 614 edges (Supplementary Tables S6
and S7). In addition, the network analysis showed that the
average degree of the compounds is 24.56, indicating that
YZHG has the characteristics of a multitarget in the
treatment of hepatitis B. There are 8 compounds with a
degree value ≥24:56 in the network, and the first 3
compounds that play an important role in the network are
luteolin (degree = 120), baicalein (degree =86), and caffeic
acid (degree = 80).
3.3. YZHG-Inflammation-Cancer Transformation of the
Hepar Network. Take the intersection of the YZHG target
and the above four groups of disease differential genes, and
get four groups of disease data. Normal group-hepatitis B
(GSE83148) identified 263 DEGs, hepatitis B-hepatitis B-
related hepatocellular carcinoma (GSE121248) contained
798 DEGs, 72 hepatitis C protein-protein interaction data
and hepatitis C-related cirrhosis-hepatitis C-related hepato-
cellular carcinoma (GSE17548) contained 706 DEGs. The
above DEG data (Supplementary Table S8) were combined
with the putative targets of YZHG to obtain a drug-disease
association network (Supplementary Table S9, Figure 4).
There are 7 common targets in the data of normal group-
hepatitis B and the putative targets of YZHG, 26 common
targets between the data of hepatitis B-hepatitis B-related
hepatocellular carcinoma and the putative targets of
YZHG, 8 common targets between the data of protein-
protein interaction of hepatitis C and the putative targets
of YZHG, and 19 common targets between the data of
hepatitis C-related cirrhosis-hepatitis C-related
hepatocellular carcinoma and the putative targets of
YZHG. It is worth noting that the normal group-hepatitis
B and hepatitis C protein-protein interaction data share
the common target MMP2 with the 5predicted targets of
YZHG. The normal group-hepatitis B, hepatitis B-hepatitis
B-related hepatocellular carcinoma, and hepatitis C-related
cirrhosis-hepatitis C-related hepatocellular carcinoma data
have 2 common targets CDK1 and TOP2A with the
predicted targets of YZHG.
3.4. PPI Analysis and Module Analysis. The PPI analysis was
performed for the common targets of the YZHG-disease
association network, and the PPI network was constructed
by Cytoscape (Figure 5). The normal group-common target
PPI network between hepatitis B data and predicted targets
of YZHG includes 14 nodes and 66 edges. Module analysis
of the CytoHubba plug-in identified the top 10 genes to con-
struct key module network 1, which includes 10 nodes and
45 edges (Figure 5(a)). The hepatitis B-hepatitis B-related
hepatocellular carcinoma data and the PPI network of
YZHG predicting common targets contain 27 nodes and
100 edges. Module analysis was performed using the Cyto-
Hubba plug-in to obtain the top 10 genes and construct
key module network 2, which consists of 10 nodes and 45
edges (Figure 5(b)). Hepatitis C PPI data and PPI network
of YZHG predicting common targets comprise 18 nodes
and 64 edges. The top 10 genes were obtained to construct
key module network 3 after the CytoHubba plug-in module
analysis, which includes 10 nodes and 39 edges (Figure 5(c)).
The PPI network of hepatitis C-related cirrhosis-hepatitis C-
related hepatocellular carcinoma and YZHG predicting
common targets embodies 22 nodes and 84 edges. Module
analysis was performed by applying the CytoHubba plug-
in to acquire the top 10 genes and construct key module
network 4, which includes 10 nodes and 45 edges
(Figure 5(d)).
3.5. Enrichment Analysis of Key Genes. Gene Ontology is
used to measure gene function in terms of biological
processes (BP), molecular functions (MF), and cellular
components (CC). Gene function is comprehensively
defined from three different perspectives. Among them,
the biological process shows the involvement of genes in
different biological processes, the molecular function
shows the function of the gene, and the cellular compo-
nent shows the distribution of the gene in the cell. In this
study, GO and KEGG enrichment analysis of the key
genes obtained by 4 groups of module analysis was per-
formed to systematically elucidate the multiple mecha-
nisms of YZHG in the treatment of inflammation-cancer
transformation of hepar (Figure 6). The results indicated
4 BioMed Research International

4
2
0
–2
–4
Type
Type
HBV
Normal
(a)
4
2
0
–2
–4
Type
Type
HBV
HBV-tumor
(b)
(c)
2
1
0
–1
–2
Type
Type
HCV-cirrhosis
HCV-HCC
(d)
Figure 2: Differentially expressed genes in advanced hepatitis B, hepatitis C, and related hepatocellular carcinoma: (a) heat map of
GSE83148; (b) heat map of GSE121248; (c) PPI network of the HCV disease database; (d) heat map of GSE17548.
Table 1: Four groups of disease data retrieval.
Record PMID Platform Group Note
GSE83148
28328162
30466390
31282064
GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus
2.0 Array Normal/HBV (6/122) —
GSE121248 17975138 GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus
2.0 Array
HBV/HBV-HCC (37/
70)
—
—— — HCV TTD,
PharmGKB
GSE17548 23691139 GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus
2.0 Array
HCV-LC/HCV-HCC
(3/3)
—
5BioMed Research International

that key module network 1 (Figure 6(a)) was highly corre-
lated with the biological process, and the key targets were
enriched in the nuclear division, organelle fission, mitotic
nuclear fission, condensed chromosomes, spindles and
chromosome regions, progesterone-mediated oocyte matu-
ration pathway, oocyte meiosis pathway, and p53 signaling
pathway. Key module network 2 (Figure 6(b)) was closely
associated with mitotic nuclear division, nuclear division,
organelle fission, spindle, centrosome, spindle microtu-
bules, oocyte meiosis signaling pathway, progesterone-
mediated oocyte maturation signaling pathway, and cell
cycle signaling pathway. Key module network 3
(Figure 6(c)) was highly associated with the epidermal
growth factor receptor signaling pathway, ERBB signaling
pathway, ERBB2 signaling pathway, plasma membrane
protein complexes, membrane rafts, membrane microdo-
mains, epidermal growth factor receptor binding, growth
factor receptor binding, ephrin receptor binding, ErbB sig-
naling pathway, phospholipase D signaling pathway, and
Ras signaling pathway. Key module network 4
(Figure 6(d)) was closely linked to mitotic nuclear divi-
sion, nuclear division, organ fission, spindle, centrosome,
spindle microtubules, oocyte meiosis signaling pathway,
progesterone-mediated oocyte maturation signaling path-
way, and cell cycle pathway.
3.6. Molecular Docking Results. The mechanism of YZHG in
the treatment of inflammation-cancer transformation of
hepar is reflected in the interaction between compounds
and targets. Through the construction of the PPI network
and module analysis, the directly related targets in the key
modules, namely, CDK1, TOP2A, EGFR, and CCNB2, were
selected to investigate the interaction of other corresponding
compounds. The three-dimensional crystal structures of 4
targets were derived from the PDB database as their PDB
codes. For CCNB2, the corresponding PDB code was not
found. AutoDock Vina was applied to dock the above 3 tar-
gets and their corresponding small-molecule drug structural
ligands. As shown in Supplementary Table S10, 7 groups of
molecular docking results were obtained. The highest affinity
docking results were luteolin and TOP2A. In addition, the
positive drug entecavir was selected as a control group for
HBV-positive liver disease, and the baseline of HBV-
positive entecavir was established. The positive drug
ribavirin was selected as the control group for HCV-
positive liver disease, and the baseline of HCV-positive
ribavirin was established. The results showed that only the
docking result of caffeic acid and EGFR was lower than
that of the positive control group, and the molecular
docking affinity results of the other groups were more
significant than those of the positive control group. The
docking combination of the 6 groups with higher docking
results than those of the positive control is shown in
Figure 7.
4. Discussion
YZHG is a commonly used drug in the clinic for the treat-
ment of liver diseases [39–41]. However, its mechanism of
action in the adjuvant treatment of acute and chronic liver
disease has not been fully elucidated. This study focuses on
the effective active ingredients and the mechanism of YZHG
in inhibiting the inflammation-cancer transformation of
hepar and expends the scope of YZHG. The aim is to reveal
the targeting of YZHG in the treatment of hepatitis and to
provide a scientific and reasonable basis for the adjuvant
medicinal use of inflammation-cancer transformation of
hepar. Chronic viral hepatitis affects 500 million people
worldwide and leads to cirrhosis, cancer, and liver failure
1. Due to the difficulty of viral clearance in chronic viral hep-
atitis, liver damage is associated with the body’s immune
function. Liver protection is usually recommended as the
primary treatment. In patients with hepatitis B and hepatitis
C, antiviral therapy should be taken actively. For patients
with liver cancer due to hepatitis and cirrhosis, we should
pay attention to the importance of antiviral therapy, and
early antiviral therapy can effectively control the progression
of liver cancer. In the present study, the network pharmacol-
ogy method was used to explore the active components and
targets of YZHG and the common targets of YZHG in the
process of inflammation-cancer transformation of hepar by
module analysis, enrichment analysis, and molecular dock-
ing to understand and predict the potential mechanism of
YZHG in inhibiting the inflammation-cancer transforma-
tion of hepar.
Figure 3: YZHG compound-putative target network. Yellow
means putative targets; red indicates the YZHG compound.
H-HBV (263) HCV (72)
(8)(7) MMP2
CDK1
TOP2A YZHG
(26) (19)
HBV-HBV.HCC
(798)
HCV.LC-HCV.HCC
(675)
Figure 4: YZHG-disease association network.
6 BioMed Research International

According to the YZHG-disease association network, the
hepatitis B and hepatitis C PPI data in the normal group had
a common target, MMP2, with the predicted target of
YZHG. The data of normal group-hepatitis B, hepatitis B-
hepatitis B-related hepatocellular carcinoma, and hepatitis
C-related cirrhosis-hepatitis C-related hepatocellular carci-
noma all had two common targets CDK1 and TOP2A with
YZHG putative targets.
Matrix metalloproteinase-2 (MMP2), a member of the
matrix metalloproteinase (MMP) gene family, is a zinc-
dependent enzyme that can cleave extracellular matrix
components and participate in signal transduction. The pro-
tein encoded by the gene is gelatinase A, type IV collagenase,
and its catalytic site contains 3 fibronectin type II repeats
that bind denatured type IV collagen and type V collagen
to elastin. Unlike most members of the MMP family, activa-
tion of this protein can occur at the cell membrane. It can
inhibit cell migration and adhesion, activate
mitochondrial-nuclear stress signaling, induce the activation
signal of nuclear transcription factor B, and activate T
nuclear factor interferon regulatory factor, promoting vascu-
lar remodeling and angiogenesis to form tumor invasion
tissues [42]. Yang et al. found that MMP2/MMP9-mediated
CD100 shedding is important for the induction of intrahe-
patic anti-HBV CD8 T-cell response and HBV clearance
[43]. The expression of mCD100 on T-cells was higher in
patients with chronic hepatitis B, and the serum sCD100
level was lower than that in the healthy control group. Ther-
apeutic sCD100 treatment led to activation of dendritic cells
and hepatic sinusoidal endothelial cells, enhanced HBV-
specific CD8 T-cell response, and accelerated HBV
clearance, while blockade of its receptor CD72 attenuated
intrahepatic anti-HBV CD8 T-cell response. Together with
MMP9, MMP2 mediates shedding of mCD100 from the sur-
face of T-cells. Serum MMP2 levels in patients with chronic
hepatitis B were significantly decreased, which was positively
correlated with serum levels of the soluble intercellular adhe-
sion molecule D100. Inhibition of MMP2/MMP9 activity
resulted in reduced anti-HBV T-cell response and delayed
HBV clearance in mice. In chronic HCV infection, patholog-
ical accumulation of the extracellular matrix is the main
feature of liver fibrosis. Degradation of connective tissue
proteins was shown to reduce the increase in matrix synthe-
sis. Matrix metalloproteinases (MMPs) play a crucial role in
extracellular matrix remodeling. Abdel-Latif studied plasma
MMP2 in 15 cases of liver fibrosis with HCV RNA detection,
10 cases of liver cirrhosis with HCV, and 15 age-matched
and gender-matched control subjects and found that
MMP2 could be used as a prognostic marker for liver
fibrosis [44]. Therefore, MMP2 plays an important role in
HBV- and HCV-related hepatitis in the published studies.
CDK (cyclin-dependent kinases) is a Ser/Thr kinase sys-
tem that corresponds to cell cycle progression. Activation of
CDK1 can phosphorylate the target protein and cause corre-
sponding physiological effects, such as nuclear fiber layer
protein phosphorylation leading to nuclear fiber layer disin-
tegration, nuclear membrane disappearance, and H1
phosphorylation leading to chromosome condensation.
The end results of these effects make the cell cycle continu-
ous. Hepatitis B virus X protein (HBx) is a multifunctional
regulatory protein known to be involved in viral prolifera-
tion, transcriptional activation, and cell growth control
(a) (b)
(c) (d)
Figure 5: PPI network and module analysis network. (a) (left) Normal group-common target PPI network between hepatitis B data and
predicted targets of YZHG; (right) module analysis of the CytoHubba plug-in identified the top 10 genes to construct key module
network 1. (b) (left) The hepatitis B-hepatitis B-related hepatocellular carcinoma data and the PPI network of YZHG; (right) module
analysis was performed using the CytoHubba plug-in to obtain the top 10 genes and construct key module network 2. (c) (left) Hepatitis
C PPI data and PPI network of YZHG; (right) the top 10 genes were obtained to construct key module network 3 after the CytoHubba
plug-in module analysis. (d) (left) PPI network of hepatitis C-related cirrhosis-hepatitis C-related hepatocellular carcinoma; (right)
module analysis of the CytoHubba plug-in identified the top 10 genes to construct key module network 4. Left: orange circle represents a
common target, and purple means related protein information obtained by PPI. Right: the top five genes with the highest scores are
represented by yellow to red, and the redder the color, the higher the MCC score.
7BioMed Research International

Nuclear division
Organelle fission
Mitotic nuclear division
Regulation of mitotic nuclear division
Regulation of nuclear division
Chromosome segregation
Chromosome condensation
Chromosome chromosome
Chromosome separation
Chromosomal region
Mitotic sister chromatid segregation
Sister chromatid segregation
Spindle
Spindle microtubule
Centrosome
Mitotic spindle
Condensed chromosome, centromeric region
Condensed chromosome outer kinetochore
Spindle pole
Chromosome, centromeric region
BP CC
0.2 0.4 0.6 0.8
Generatio
p.adjust
2e–05
4e–05
6e–05
Count
2
4
6
8
p.adjust
0.50 0.55 0.60 0.65 0.70 0.75
Generatio
Progesterone-mediated
oocyte maturation
Oocyte meiosis
p53 signaling pathway
Cell cycle
Cellular sensecence
Human immunodeficiency
virus 1 infection
0.002
0.004
0.006
Count
2.00
2.25
2.50
2.75
3.00
(a)
Nuclear division
Organelle fission
Mitotic nuclear division
Regulation of mitotic nuclear division
Regulation of nuclear division
Chromosome condensation
Condensed chromosome
Chromosome separation
Nuclear chromosome segregation
Chromosomal region
Mitotic sister chromatid segregation
Sister chromatid segregation
Spindle
Spindle microtubule
Centrosome
Mitotic spindle
Condensed chromosome, centromeric region
Spindle pole centrosome
Spindle pole
Microtubule
BP CC
0.2 0.4 0.6 0.8
Generatio
p.adjust
2.5e–05
5.0e–05
7.5e–05
Count
2
3
4
5
6
7
8
Count
2
3
4
5
p.adjust
0.4 0.6 0.8 1.0
Generatio
Progesterone-mediated
oocyte maturation
Oocyte meiosis
p53 signaling pathway
Cell cycle
Cellular sensecence
FoxO signaling pathway
Human immunodeficiency
virus 1 infection
0.002
0.004
0.006
0.008
Human T-cell leukemia
virus 1 infection
2.5e
–
05
5.0e
–
05
7.5e–
0
5
Prog
esterone-me
d
iate
d
oocyte maturatio
n
O
oc
y
te meios
is
p53
sign
a
l
in
g pa
t
h
wa
y
Cell c
y
cle
C
ellular sensecence
F
oxO
sig
nali
ng p
athw
ay
H
uman immuno
d
e
fi
ciency
virus 1 in
f
ectio
n
Human T-cell leukemi
a
vir
us
1 in
fect
i
on
(b)
Figure 6: Continued.
8 BioMed Research International

[45]. Cheng et al. further studied the effect of HBx on cell
growth in vitro and in vivo. HBx can inhibit the growth of
hepatocellular carcinoma (HCC) cells and induce G2/M
phase arrest in vitro. HBx continuously activates cyclin B1-
CDK1 kinase. In vivo, HBx inhibits tumor cell growth and
induces apoptosis and inhibits the vascular endothelial cell
growth [46].
In summary, HBx induces G2/M arrest and apoptosis
through continuous activation of cyclin B1-CDK1 kinase
and negatively regulates cell growth in vitro and in vivo.
HCV core proteins disrupt the G1/S and G2/M phases by
regulating the expression and activity of several cell cycle
regulators [46]. Viral proteins increase the activity of the
cyclin B1-CDK1 complex via p38 MAPK and JNK pathways.
CDK1 plays an essential role in HCV-related liver diseases.
TOP2A encodes a DNA topoisomerase that controls and
alters the topological state of DNA during transcription. The
ribozyme is involved in chromosome condensation, chro-
matid separation, and the reduction of torsional stress
during DNA transcription and replication [47]. It catalyzes
Epidermal growth factor receptor signaling pathway
ERBB signaling pathway
ERBB2 signaling pathway
Regulation of epidermal growth factor receptor signaling pathway
Regulation of ERBB signaling pathway
Immune response-regulating cell surface receptor signaling pathway
Fc-epsilon receptor signaling pathway
Negative regulation of epidermal growth factor receptor signaling pathway
Negative regulation of ERBB signaling pathway
Positive regulation of small GTPase mediated signal transduction
Plasma membrane protein complex
Membrane ra
Membrane region
Membrane microdomain
Focal adhes ion
Cell-substrate adherens junction
Adherens ju nction
Cell-substrate junction
Clathrin-coated vesicle membrane
Epidermal growth factor receptor binding
Growth factor receptor binding
Ephrin receptor binding
Phosphotyrosine residue binding
Protein phosphorylated amino acid binding
Phospoprotein binding
Signaling adaptor activity
Transmembrane receptor protein tyrosine kinase adaptor activity
Neurotrophin receptor binding
Insulin receptor substrate binding
BP CC MF
0.2 0.4 0.6 0.8
Generatio
Count
2
3
4
5
6
7
8
0.0025
0.0050
0.0075
p.adjust
ErbB signaling pathway
Phospholipase D signaling pathway
Ras signaling pathway
Glioma
Chronic myeloid leukemia
EGFR tyrosine kinase inhibitor resistance
Breast cancer
Gastric cancer
Endometrial cancer
Non-small cell lung cancer
Colorectal cancer
Prostate cancer
Endocrine resistance
Choline metabolism in cancer
Relaxin signaling pathway
FoxO signaling pathway
Insulin signaling pathway
Estrogen signaling pathway
Hepatitis C
Hepatocellular carcinoma
Focal adhesion
Proteoglycans in cancer
MicroRNAs in cancer
Human papillomavirus infection
Fc epsilon RI signaling pathway
Prolactin signaling pathway
Gap junction
Neurotrophin signaling pathway
Growth hormone synthesis, secretion and action
Natural killer cell mediated cytotoxicity
Count
5
6
7
8
0.6 0.7 0.8 0.9
Generatio
5e–07
1e–06
p.adjust
(c)
Nuclear division
Organelle fission
Mitotic nuclear division
Regulation of mitotic nuclear division
Regulation of nuclear division
Chromosome condensation
Condensation chromosome
Chromosome separation
Chromosomal region
Mitotic sister chromatid segregation
Sister chromatid segregation
Spindle microtubule
Centrosome
Mitotic spindle
Condensed chromosome, centromeric region
Spindle pole
Nuclear chromosome segregation
Spindle
Microtubule
Spindle pole centrosome
BP CC
0.2 0.4 0.6 0.8
p.adjust
2.5e–05
5.0e–05
7.5e–05
Generatio Generatio
Progesterone-mediated
oocyte maturation
Oocyte meiosis
p53 signaling pathway
Cell cycle
Cellular sensecence
Human immunodeficiency
virus 1 infection
FoxO signaling pathway
Human T-cell leukemia
virus 1 infection
0.4 0.6 0.8 1.0
p.adjust
0.002
0.004
0.006
0.008
Count
2
3
4
5
6
7
8
Count
2
3
4
5
(d)
Figure 6: Enrichment analysis of key genes. (a) Enrichment result of key module 1. (b) Enrichment result of key module 2. (c) Enrichment
result of key module 3. (d) Enrichment result of key module 4. The left side is the result of GO enrichment, and the right side is the result of
KEGG enrichment.
9BioMed Research International

the fast breaking and rebinding of the two strands of DNA,
making the two strands pass through each other, thus alter-
ing the topological structure of DNA. Genes encoding this
enzyme act as targets for various anticancer drugs, and
multiple mutations in this gene have been linked to the
development of drug resistance. Panvichian et al. found that
TOP2A overexpression was significantly associated with
HCC tumor tissue (P<0:001), serum HBsAg (P=0:004),
and Ki-67 (P=0:038). Ignat et al. used quantitative RT-
PCR to verify the coexpression of linking genes in a group
of normal liver tissues (n=8), chronic liver diseases (n=7
), and hepatocellular carcinoma (n=7) induced by hepatitis
C virus (n=9). TOP2A was significantly upregulated in ded-
ifferentiated hepatocellular carcinoma and hepatocellular
carcinoma with loss of chromosome 13q [48, 49].
Molecular docking results showed that the binding abil-
ity of CDK1, TOP2A, and EGFR and their corresponding
compounds was greater than that of the positive control
group. Epidermal growth factor receptor (EGFR) is the
receptor for cell proliferation and signal transduction of epi-
thelial growth factor (EGF). Studies have shown that EGFR
is highly or abnormally expressed in many solid tumors.
EGFR is associated with tumor cell proliferation, angiogene-
sis, tumor invasion, metastasis, and inhibition of apoptosis.
Wen et al. and Lo et al. found that EGFR has the most sig-
nificant antitumor effect in treating liver cancer [50, 51].
YZHG is used in clinical application for acute and
chronic hepatitis and severe hepatitis caused by the damp-
heat toxin and can also be used for comprehensive treatment
of other types of severe hepatitis [52–54]. In this study, net-
work pharmacology and bioinformatics methods were used
to explore and predict the key targets and potential mecha-
nisms of YZHG in inhibiting the inflammation-cancer
transformation of hepar. There is a common target MMP2
for hepatitis B and hepatitis C, which plays an important
role in treating hepatitis with YZHG. Also, CDK1 and
TOP2A may play an essential role in the inhibition of YZHG
in the inflammation-cancer transformation of hepatitis B.
KEGG pathway enrichment showed that key genes were
mainly enriched in the progesterone-mediated oocyte matu-
ration and oocyte meiosis pathway in modules 1, 2, and 4.
5. Conclusion
In conclusion, the present study explored and predicted the
key targets and potential mechanisms of YZHG in inhibiting
the inflammation-cancer transformation of hepar by net-
work pharmacology and bioinformatics. It is hoped that this
study will lay a good foundation for further experimental
research and contribute to the application of network phar-
macology in exploring the potential mechanism of complex
diseases. Since this study is based on data analysis, further
(a) (b)
(c) (d)
(e) (f)
Figure 7: Molecular docking results. (a) CDK1-baicalin; (b) CDK1-baicalein; (c) TOP2A-luteolin; (d) EGFR-oroxylin A-7-O-β-D-
glucuronide; (e) EGFR-baicalein; (f) EGFR-oroxylin A.
10 BioMed Research International

in vitro and in vivo data are needed to validate these findings
and optimize the method.
Abbreviations
HCC: Hepatocellular carcinoma
HBV: Hepatitis B virus
HCV: Hepatitis C virus
DEGs: Differentially expressed genes
MMP2: Matrix metalloproteinase-2
CDK1: Cyclin-dependent kinase 1
TOP2A: DNA topoisomerase 2-alpha
EGFR: Epidermal growth factor receptor
EGF: Epithelial growth factor
CCNB2: G2/mitotic-specific cyclin-B2.
Data Availability
The data used to support the findings of this study are
included within the supplementary information files.
Conflicts of Interest
The authors declare that they have no conflicts of interests.
Authors’Contributions
JYZ and JRW conceived and designed the study. XKL, CW,
WZ, PZY, and SL provided significant suggestions on the
methodology. YYT, ZSW, and XTF collected the data.
ZHH, XMZ, MMW, GLC, and BBL performed the data
analysis. JYZ, AS, and JRW wrote and revised the manu-
script. All authors were responsible for reviewing the data.
All authors read and approved the final manuscript.
Acknowledgments
This work was supported by the Young Scientists Training
Program of Beijing University of Chinese Medicine and the
National Natural Science Foundation of China (Grant no.
81673829).
Supplementary Materials
Supplementary materials contain eight tables. Supplemen-
tary Table S1: the information of differentially expressed
genes in GSE83148. Supplementary Table S2: the informa-
tion of differentially expressed genes in GSE121248.
Supplementary Table S3: the information of targets in the
PPI network of hepatitis C. Supplementary Table S4: the
information of differentially expressed genes in GSE17548.
Supplementary Table S5: the information of 25 compounds
in YZHG. Supplementary Table S6: relationship between
network points of target nodes of YZHG. Supplementary
Table S7: relationship between network points of target
edges of YZHG. Supplementary Table S8: the information
of 4-group disease data. Supplementary Table S9: the infor-
mation of the drug-disease association network. Supplemen-
tary Table S10: the molecular docking result analysis.
(Supplementary Materials)
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