Based on the Transcriptome Study of the Effect of Sanyeqing Active
Ingredients on Liver Cancer and the Impact of Resveratrol on
MAPK3 and PRKCD
Yuexing Ma
1,2,3,a,†
, Zhixin Zhu
1,2,3,b,†
, Yaqiong Deng
1,2,3,c,*,†
, Wei Xu
1,2,3,d,*
, Zirong Peng
1,2,3,e
,
Rongbin Pan
1,4,f
, Feng Zhou
1,2,3,g
, Huiming Hu
1,2,3,h
, Xiaoqi Meng
1,2,3,i
, Xin Qiao
1,j
, Xuening Huang
1,k
and Mengyu Hou
1,l
1
Jiangxi University of Chinese Medicine, 330004, Nanchang, Jiangxi, China
2
Science and Technology College, Jiangxi University of Chinese Medicine, 330004, Nanchang, Jiangxi, China
3
Nanchang Medical College, 330004, Nanchang, Jiangxi, China
4
Jiangzhong Cancer Research Center, Jiangxi University of Chinese Medicine, 330004, Nanchang, Jiangxi, China
g h i j k
l
*
Co-corresponding author
†The same contribution to research
Keywords: Tetrastigmatis Hemsleyani (Sanyeqing), Liver Cancer, Component-Protein Interaction Network, Molecular
Docking, Immune Microenvironment Assessment.
Abstract: Tetrastigmatis hemsleyani (Sanyeqing) has pharmacological effects such as clearing away heat and
detoxifying, anti-tumor, and regulating immunity. This research is dedicated to discovering and verifying the
impact of Sanyeqing on liver cancer. Download the practical components and corresponding targets in
Sanyeqing and liver cancer-related genes, and take the intersection of the two. Perform enrichment analysis
and related gene interaction network analysis on the intersection genes. Subsequently, the core genes and
pivot genes were screened out by Cytoscape to select different indicators for the intersection genes. Carry out
survival analysis, tumor differential expression analysis, and cancer cell visual summary of core genes and
pivot genes, and screen out the two most critical genes for molecular docking with corresponding active
ingredients. The KEGG analysis results enriched the final two genes with the highest-ranking pathways and
TIMER data tumor immune assessment. After the gene and the target were crossed, 131 crossed genes were
obtained. The subsequent screening got 38 core genes and 20 pivot genes, and finally, the two most critical
genes of MAPK3 and PRKCD were comprehensively screened through survival analysis. Resveratrol, the
main chemical component, affects MAPK3 and PRKCD, and the docking results show that there is an effect
between the two. The tumor immune assessment results also indicate that MAPK3 and PRKCD are expressed
differently in normal tissues. Resveratrol has a particular effect on MAPK3 and PRKCD, which may have
new thinking for treating liver cancer in the future.
1 INTRODUCTION
Tetrastigmatis hemsleyani (Sanyeqing) is the root
tuber of Tetrastigrna hemsleyanum Diels et Gilg in
the grape family. San Ye Qing is used as medicine
with tuber roots to clear away heat, detoxify, expel
wind and phlegm, promote blood circulation, and
relieve pain (Liu et al. 2021, Liu et al. 2019, Liu et al.
2018). Modern pharmacological research shows that
Tetrastigmatis hemsleyani has anti-inflammatory,
analgesic and antipyretic, anti-tumor, anti-virus, and
immune-regulating effects (Pu et al. 2021, Wu et al.
2018, Zhong et al. 2016). Since cancer is a worldwide
health problem, it has brought huge safety risks to
people worldwide. In the latest report on the national
cancer burden released by IARC, newly diagnosed
cases of liver cancer accounted for 4.7% of the newly
diagnosed patients and deaths. The rate has reached
8.3%. This is enough to show that liver cancer is a
disease that cannot be ignored. In this paper, by
studying the relationship between Sanyeqing and
Ma, Y., Zhu, Z., Deng, Y., Xu, W., Peng, Z., Pan, R., Zhou, F., Hu, H., Meng, X., Qiao, X., Huang, X. and Hou, M.
Based on the Transcriptome Study of the Effect of Sanyeqing Active Ingredients on Liver Cancer and the Impact of Resveratrol on MAPK3 and PRKCD.
DOI: 10.5220/0011376900003443
In Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics (ICBEB 2022), pages 1041-1050
ISBN: 978-989-758-595-1
Copyright
c
 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
1041

liver cancer, we are committed to discovering and
verifying the effect of Sanyeqing on liver cancer. We
hope that it will be helpful in the treatment of liver
cancer (Zhong et al. 2013, Zhong et al. 2014).
2 MATERIALS AND METHODS
2.1 Data Source
Use the Computational Systems Biology Laboratory
(https://tcmspw.com/) to query and download the
active ingredients in San Ye Qing, and screen the
effective ingredients with OB greater than or equal to
30%, DL greater than or equal to 0.18, and the targets
of San Ye Qing.
We download human liver cancer genes in gene
banks (GeneCards, OMIM, PharmGKB, TTD,
DurgBank), use R (4.0.3) and (Venn) packages to
make a Venn diagram, and merge all liver cancer
genes.
2.2 Associated Tetrastigmatis
Hemsleyani Components, Targets,
and Human Liver Cancer Genes
It intersects the target genes of Sanyeqing and human
liver cancer genes to obtain shared genes, using R
(4.0.3) and (venn) program packages to screen out the
corresponding practical components and the
intersection genes and make a Venn diagram. Use
Cytoscape (3.8.0) to make a tri-leaf green-liver
cancer gene regulatory network to see the relationship
between the effective ingredients in tri-leaf green and
human liver cancer genes.
2.3 Related Gene Enrichment Analysis
The intersection genes were analyzed for GO analysis
using R (4.0.3) and clusterProfiler, org.Hs.eg.db,
enrichplot, and ggplot2 packages. In addition, cluster
profile, org.Hs.eg.db, enrichplot, ggplot2, pathview
packages are used to perform KEGG analysis to
predict the enrichment of these components in each
pathway.
2.4 Protein Protein Interaction
Network (PPI) and Enrichment
Analysis
Metascape analyzed the intersection genes for protein
interaction network enrichment analysis. Show the
relationship between the enriched terms (paths) in a
network diagram, and use String to calculate the
similarity between the enriched pathways.
2.5 Screening of Core Genes
Use Cytoscape (3.8.0) to import the protein
interaction relationship of the intersection gene, and
use R (4.0.3) to get the part of each item whose score
is greater than the average through 6 scoring methods
(Betweenness, Closeness, Degree, Eigenvector,
LAC, Network). Survival analysis was performed
using e Kaplan Meier plotter database.
2.6 Screening of Pivot Genes
Use Cytoscape (3.8.0) to introduce the interaction
relationship of the intersection gene and protein,
calculate the score by Degree, and use the Kaplan
Meier plotter database for survival analysis of the top
20 parts.
2.7 Comprehensive Screening of
Intersection Genes for Prognostic
Analysis
Firstly, Combines the two screening methods of 1.5
and 1.6 to obtain effective genes, select the two with
the smallest log-rank P (MAPK3, PRKCD), and find
the corresponding effective ingredient Resveratrol
from the previous correspondence. Secondly,
download MAPK3 and PRKCD models in PubChem,
then use ChemOffice to build 3D models and
optimize them. Thirdly, download the 3D model of
Resveratrol in UniProt and use PyMOL to remove
water molecules. Fourthly, use AutoDock to
hydrogenate Resveratrol and use its center as the
center of the active pocket. The size of the functional
bag is 40, 40, and 40. Finally, PyMOL was used to
dock the active ingredients with MAPK3 and
PRKCD, respectively molecularly.
2.8 MAPK3, PRKCD Enrichment
Pathway Analysis
R (4.0.3) was used to analyze the pathway with the
highest enrichment ranking of MAPK3 and PRKCD.
2.9 TIMER Data Tumor Immunity
Assessment
Use the TIMER algorithm to evaluate the abundance
of six immune infiltration fluids (B cells, CD4 + T
cells, CD8 + T cells, neutrophils, macrophages, and
ICBEB 2022 - The International Conference on Biomedical Engineering and Bioinformatics
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dendritic cells), and explore the presence of MAPK3
and PRKCD in tumors and Differential gene
expression between normal tissues.
3 RESULTS
3.1 Screening of Genes Related to the
Role of Sanyeqing and Human
Liver Cancer
Downloaded the active ingredients (Chlorogenic
acid, caffeic acid, quercetin, Resveratrol, orientin,
rutin) and targets of Sanyeqing from the
Computational Systems Biology Laboratory
downloaded the union of human liver cancer genes
from five gene libraries (Fig1.a). The intersection of
component targets and liver cancer genes was used to
screen out related genes (Fig.1.b), 177 trifoliate
targets and 3024 liver cancer genes, and 131 common
genes were obtained. We used Cytoscape (3.8.0) to
create a trifoliate-human liver cancer gene regulatory
network (Fig1.c). Resveratrol and quercetin have the
most connections with genes and have the best
correlation.
3.2 Shared Gene Enrichment Analysis
The top 5 essential pathways from the GO enrichment
analysis diagram (Fig. 2Aa) show that in BP, CC,
MF, KEGG pathway analysis, the critical pathways
of intersection genes include Lipid and
atherosclerosis, Fluid shear stress, and atherosclerosis
(Fig. 2Ab).
3.3 Enrichment Analysis of Related
Gene Protein Interaction Network
We constructed a PPI network using intersection
genes. Metascape uses the protein interaction
relationships in BioGrid, InWeb_IM, and OminPath
databases and uses the MCODE algorithm to cluster
the PPI (protein interaction) network to identify the
subnetworks.
Figure 1: Screening of trifoliate green-hepatocarcinoma genes. a. There are 131 intersecting targets of Sanyeqing and human
liver cancer genes (There are 3024 human liver cancer genes.). b. Trefoil green-human liver cancer gene regulatory network,
in which yellow is the main component of Trefoil green, blue is the intersection gene, and the size of blue indicates the
strength of the effect.
Based on the Transcriptome Study of the Effect of Sanyeqing Active Ingredients on Liver Cancer and the Impact of Resveratrol on MAPK3
and PRKCD
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3.4 Screening Core Genes
We used Cytoscape (3.8.0) and R (4.0.3) to obtain 38
genes with scores above the average (Fig. 3A). We
analyzed the prognostic information of these 38 core
genes at different stages for the liver cancer group and
the normal group by using the Kaplan Meier plotter
database. The part with log-rank P<0.05 was screened
out, and the ten core genes with the smallest log-rank
value (AR, EGFR, EGF, IGF2, MAPK3, MMP9,
PPARA, PRKCD, STAT3, VEGFA) were obtained.
The results show that in patients with early LIHC,
EGFR, IGF2, PPARA, STAT3 expressed higher.
(Fig.3B)
In addition, the GEPIA database is used to verify
the expression levels of 10 core genes in tumors and
normal tissues (Fig.4Ba-j). Results LIHC patients
based on the GEPIA database had higher expressions
of MAPK3, MMP9, and PRKCD than healthy
people.
3.5 Screening for Pivot Genes
Using CytoHubba plug-in in Cytoscape (3.8.0), we
calculated Degree to determine the 20 highest-
scoring genes (Fig.5A).
Figure 2: A. Enrichment analysis of related genes. (a. Gene Ontology (GO), Biological Process (BP), Cell Component (CC),
and Molecular Function (MF). b is the KEGG analysis of related genes.) B. Enrichment network diagram and PPI protein
interaction network diagram (a. In the enrichment network Figure. The same color nodes are enriched as a label. b. Identify
densely connected regions of proteins through MCODE.)
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Figure 3: A. Screening of core genes, the picture shows the intersection genes, and the yellow ones are the core genes screened
out. B. Survival analysis and forest map of core genes. (log-rank P<0.05).
Subsequently, we used cBioPortal to determine
the genetic change information of 10 core genes, as
shown in the figure (Fig. 4A). VEGFA and AR have
the most frequent mutations (8% and 2.7%,
respectively).
The prognostic information of these 20 pivot
genes on the liver cancer group and the normal group
at different stages was analyzed by using the Kaplan
Meier plotter database. The part with logrank P<0.05
was screened out, and the 10 pivot genes with the
smallest logrank value (CCND1, EGFR, EGF,
MAPK3, PRKCD, RXRA, STAT1, STAT3, TP53,
VEGFA) were selected. Early-stage LIHC patients
CCND1, EGFR, RXRA, STAT1, STAT3, TP53have
higher expressions (Fig.5B). Subsequently,
cBioPortal was used to determine the genetic change
information of the ten pivot genes, as shown in the
figure (Fig.6A). TP53, CCND1, and VEGFA were
most frequently mutated (33%, 8%, and 8%,
respectively).
In addition, we also used the GEPIA database to
verify the expression levels of 10 pivot genes in
tumors and normal tissues. According to the gene
expression profiles of Cancer Genome Atlas (TCGA)
and Genotype-Tissue Expression (GTEx) projects
(Fig6a-k). Results The LIHC patients based on the
GEPIA database had higher expressions of CCND1,
MAPK3, PRKCD, STAT1, TP53 than healthy people
(P <0.01).
Figure 4: A. Visual summary of cancer cells for core analysis. B. Tissue expression analysis of core genes. (a-j) are the gene
expression status in LIHC tissues and normal tissues (LIHC num(T)=369, num(N)=160).
Based on the Transcriptome Study of the Effect of Sanyeqing Active Ingredients on Liver Cancer and the Impact of Resveratrol on MAPK3
and PRKCD
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Figure 5: A. Screen the pivot genes. The darker the color, the higher the Degree. B. Hub gene survival analysis forest map.
(log-rank P<0.05)
3.6 Comprehensive Screening of Genes
for Prognostic Analysis
According to the 2.4 2.5 screening method results, the
shared genes are EGFR, EGF, MAPK3, PRKCD,
STAT3, VEGFA, and the two genes with the smallest
log-rank value (MAPK3, PRKCD) are molecularly
docked with their corresponding active ingredients.
We use AutoDock to hydrogenate MAPK3 and
PRKCD and use their center as the center of the active
pocket, the size is 40, 40, and 40. Then, use Vina and
PyMOL for molecular docking to select the
conformation with the lowest Grid Score (Fig.7A).
The optimal conformation Grid Score of the docking
results of MAPK3 and Resveratrol is -8.5, indicating
a good combination between the two. The optimal
conformational Grid Score score of PRKCD and
resveratrol docking results is -6.2, showing a good
mix between the two.
3.7 MAPK3 and PRKCD Pathway
Both MAPK3 and PRKCD are enriched in the AGE-
RAGE signaling pathway (Fig. 7B).
3.8 Immune Microenvironment
The correlation analysis between MAPK3, PRKCD
gene expression and a large number of immune
infiltration (fig.8a). P<0.05 has statistical
significance. The differential expression of MAPK3
and PRKCD in other tumor tissues compared with
normal tissues (fig.8b).
Figure 6: A. Visual summary of hub gene cancer cells. B. Hub gene tissue expression analysis (a-j) The gene expression status
of patients in LIHC tissues and normal tissues (LIHC num(T)=369, num(N)=160).
ICBEB 2022 - The International Conference on Biomedical Engineering and Bioinformatics
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4 DISCUSS
Sanyeqing is often used to clear heat and detoxify in
Chinese medicine and can be used for anticancer in
western medicine. Internet pharmacology ended the
research model of "one component, one target" in
traditional Chinese medicine and opened a new
"multi-component, multi-target" model (Medvedev et
al. 1997, Salkovic-Petrisic et al. 2001). Due to the
numerous chemical components in Trifolium repens,
we explored the interaction network between the six
elements of Resveratrol (Bailey et al. 2021, Najafi et
al. 2019, Pandey et al. 2014, Tabibiazar et al. 2019),
quercetin (Bui Van et al., 2022 , Oh et al., 2021 ,
Ozkok et al. 2021, Safi et al. 2021, Zhang et al. 2021),
orientin (Jiang et al. 2021), chlorogenic acid (Le-Bail
et al. 2015, Wu et al. 2010, Xing et al. 2015), caffeic
acid(Akyol et al., 2016 , Celli et al., 2007 , Petelinc et
al. 2017, Schaller and Holler, 1976), and rutin (Cao
et al. 2019, Ojha et al. 2016, Park et al. 2014) in the
liver cancer gene library. We screened that
Resveratrol and quercetin are the most critical
anticancer effects of Resveratrol and quercetin
Element.
Firstly, continue the GO and KEGG enrichment
analysis through the component target interaction
network. In the BP category, the intersection genes
are mainly involved in cellular response to chemical
stress (Kultz 2003, Szewczyk et al. 2020), oxidative
stress, response to reactive oxygen species
(Molassiotis and Fotopoulos 2011), cellular response
to oxidative stress, regulation of apoptotic signaling
pathway. The intersection genes are related to
membrane raft (Levental et al. 2014), membrane
microdomain, membrane region, caveola, and plasma
membrane raft for class CC. For MF, the molecular
functions of the intersection genes are mainly DNA-
binding transcription factor binding, RNA
polymerase II-specific DNA-binding transcription
factor binding, phosphatase binding, protein
phosphatase binding, and ubiquitin-like protein ligase
binding. For KEGG pathway analysis, the top five
essential KEGG pathways of intersection genes
include Lipid and atherosclerosis (Feng et al. 2021,
Ni et al. 2021), Fluid shear stress and atherosclerosis,
Kaposi sarcoma-associated herpesvirus infection,
AGE-RAGE signaling pathway in diabetic
complications, and Hepatitis C.
Figure 7: A. Intersection genes for predictive analysis. a . the docking of MAPK3 and resveratrol molecules. b . the molecular
docking of PRKCD and Resveratrol. B. MAPK3, PRKCD enrichment pathway analysis. The enrichment pathway is the AGE-
RAGE signaling pathway in diabetic complications.
Based on the Transcriptome Study of the Effect of Sanyeqing Active Ingredients on Liver Cancer and the Impact of Resveratrol on MAPK3
and PRKCD
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Secondly, we calculated ten core genes AR,
EGFR, EGF, IGF2, MAPK3, MMP9, PPARA,
PRKCD, STAT3, VEGFA through Cytoscape. We
calculated 20 hub genes through CytoHubba (Chen et
al. 2021, Chin et al. 2014, Sang et al. 2018, You et al.
2020). We conducted survival rate analysis and
differential expression analysis and finally
determined that EGFR, EGF, MAPK3, PRKCD,
STAT3, VEGFA have significant effects on liver
cancer.
Finally, let Resveratrol and PRKCD, and MAPK3
be analyzed and docked. The results show that
Resveratrol has a particular therapeutic effect on liver
cancer's differentially expressed PRKCD and
MAPK3. In addition, the enrichment results show
that MAPK3 and PRKCD have a more significant
impact on the AGE-RAGE signal pathway, indicating
that the trifoliate component resveratrol has a
particular effect on liver cancer through the AGE-
RAGE signal pathway (Kay et al. 2016, Ren and Yan,
2021, Waghela et al. 2021, Zong et al. 2011).
During the analysis of the immune
microenvironment (Cui et al. 2021, Liu 2019, Zhang
et al. 2020), we found that MAPK3 and PRKCD are
significantly correlated with B cell, CD8+ T Cell,
CD4+ T Cell, Macrophage, Neuropil, and Dendritic
Cell in the tumor environment. And MAPK3 and
PRKCD not only have significant differential
expression in liver cancer but also in BLCA, BRCA,
CHOL, COAD, ESCA, HNSC, HVCE-HPVpos,
KICH, KIRP, LUAD, LUSC, PRAD, READ, SKCM,
STAD, THCA, UCEC, and other cancer tissues also
have significant differential expression. In the future,
we can continue to explore the effect of Sanyeqing on
other cancer tissues.
Figure 8: Correlation analysis between MAPK3, PRKCD gene expression, and a large number of immune infiltrations (A.
Estimate six resistant infiltration fluids (B cells, CD4 + T cells, CD8 + T cells, neutrophils, macrophages, Dendritic CellS)
through the TIMER algorithm (And dendritic cells), B. MAPK3, PRKCD differential gene expression between tumor and
normal tissue, the shaded area indicates that the cancer is significantly different from normal tissue)
(https://cistrome.shinyapps.io/timer/).
ICBEB 2022 - The International Conference on Biomedical Engineering and Bioinformatics
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5 CONCLUSIONS
(1) Resveratrol and quercetin were screened for
anticancer pharmacological components Resveratrol
and quercetin through the chemical constituents of
the Sanyeqing-liver cancer target network.
(2) Through the screening of liver cancer core
genes and pivot genes, and survival verification and
differential expression, it is verified that MAPK3 and
PRKCD are the most critical to the occurrence and
development of liver cancer, and the active
ingredients Sanyeqing have a specific binding effect
on them.
ACKNOWLEDGMENTS
This work was financially supported by Science and
Technology Project of Jiangxi Provincial Department of
Education, GJJ181580
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