BioMed Research International
Volume 2021 (2021), Article ID 6616908, 22 pages
https://doi.org/10.1155/2021/6616908

Research Article

Interpreting the Molecular Mechanisms of Yinchenhao Decoction on Hepatocellular Carcinoma through Absorbed Components Based on Network Pharmacology

Jijia Sun,¹ Tao Han,¹ Tao Yang,1,2 Yunhui Chen,³ and Jihan Huang1,2

¹Center for Drug Clinical Research, Shanghai Traditional Chinese Medicine Health Service Collaborative Innovation Center, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
²Department of Cardiology, Cardiovascular Research Institute, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
³College of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China

Correspondence should be addressed to Tao Yang; yangtao8579@163.com and Jihan Huang; huangjihan@shutcm.edu.cn

Received 11 December 2020; Revised 23 April 2021; Accepted 7 May 2021; Published 20 May 2021

Academic Editor: Li-kang SUN

Copyright © 2021 Jijia Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

To investigate the mechanisms through which Yinchenhao decoction (YCHD) inhibits hepatocellular carcinoma (HCC), we analyzed YCHD ingredients absorbed into the bloodstream by using network pharmacology. We conducted a weighted gene coexpression network analysis on gene expression data collected from the Gene Expression Omnibus and The Cancer Genome Atlas databases to derive an HCC gene set; moreover, we used four online prediction system databases to predict the potential targets of YCHD ingredients absorbed into the bloodstream. We discovered that YCHD directly interfered with 17 HCC-related disease targets. Subsequent gene ontology enrichment analyses of these 17 disease targets revealed that YCHD exhibited effects through 17 biological processes, 7 molecular functions, and 9 cellular components. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated 14 pathways through which YCHD inhibits HCC. We observed similar trends in how the 17 small molecules interfered with the key target set. We surmised that YCHD inhibits HCC by regulating inflammatory and metabolic pathways. Network pharmacological analysis of YCHD ingredients absorbed into the bloodstream may provide new insights and serve as a new method for discovering the molecular mechanisms through which YCHD inhibits HCC.

1. Introduction

Hepatocellular carcinoma (HCC) is the fourth most common cancer and the third most deadly cancer in China, accounting for 85%–90% of primary malignant liver tumors [1]. Although the multikinase inhibitor sorafenib has been approved as a first-line treatment for late-stage HCC because of its favorable antiangiogenesis effects, it increases patients’ survival time by only a few months [2]. Traditional Chinese medicine (TCM), which has a long history of clinical application, exhibits multimolecule, multitarget, and synergistic effects. TCM has subsequently become a valuable alternative for HCC treatment. Therefore, the effectiveness of TCM in inhibiting HCC merits further investigation [3].

Introduced in Shanghan Lun (Treatise on Cold Damage Diseases), Yinchenhao decoction (YCHD)—which consists of yinchen (Artemisiae Scopariae Herba), dahuang (Radix et Rhizoma Rhei), and zhizi (Gardeniae Fructus)—is a popular remedy in TCM and is commonly used to treat yang jaundice. A study identified 160 potential targets related to 16 diseases, including cancer; of these targets, 96 were related to cancer [4]. Concerning oral TCM compounds (i.e., medicine comprising two or more ingredients), the ingredients must undergo absorption, distribution, metabolism, and excretion before they enter the bloodstream; only molecules that complete this process are considered active. These molecules merge with targets in the organism and then exhibit medicinal effects. In a preliminary study, we obtained active chemical molecules by using oral bioavailability (OB) screening [5], drug-likeness (DL) assessments [6], and intestinal epithelial permeability (Caco-2) screening. In that study, we were able to predict which molecules were beneficial; however, their effects were minimal if the molecule content was low or if the molecules were metabolized quickly. Sun et al. used the highly sensitive ultraperformance liquid chromatography–tandem mass technology to identify the 69 compounds in YCHD, among which 41 were absorbed into the bloodstream [7]. Wu et al. conducted a study on major anti-HCC components in YCHD in vitro and in vivo [8]. Nevertheless, the active molecules in YCHD compounds and the mechanisms through which these compounds mitigate HCC remain relatively unknown.

TCM incorporates complex chemical compositions, and effective research methods for examining these compositions are limited; additionally, the pharmacological mechanisms governing TCM have yet to be systematically explained [9]. Systems pharmacology [10] is an emerging discipline that involves systematically studying the interaction between medicine and organisms as well as the patterns, effects, and mechanisms of such medicine. Network pharmacology [11] is used to study active substances in TCM, identify the targets affected by these substances, determine the relationships between effective substances and diseases, and elucidate the molecular mechanisms governing the activities of TCM compounds. Therefore, systems pharmacology- and network pharmacology-based studies offer new perspectives and insights into TCM compounds. Regarding the use of network pharmacology to study TCM, our laboratory has performed numerous experiments and explored the pharmacological mechanisms governing zuojin pills [12], huanglian jiedu decoction [13], and huachansu capsules [14] in the treatment of HCC.

Accordingly, the present study analyzed data obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases to identify the differentially expressed genes (DEGs) of HCC. On the basis of the YCHD ingredients absorbed into the bloodstream, network pharmacology was then used to identify active HCC-inhibitive pharmaceutical ingredients and their possible molecular targets. The workflow of our bioinformatics and network pharmacology analyses of the effects of YCHD on HCC is presented in Figure 1. The results may serve as a basis for subsequent experimental research.

Figure 1: Network pharmacology-based flowchart depicting how YCHD inhibits HCC.

2. Materials and Methods

2.1. Data Collection

The GEO is a public gene expression profile database of the National Center for Biotechnology Information, National Institutes of Health (USA). By mining public databases, medical researchers can obtain a clearer understanding of the molecular mechanisms underlying the onset of cancer. Such an understanding can enhance early detection, diagnosis, and treatment of various cancers [15]. In the current study, we used “liver cancer” and “hepatocellular carcinoma” as search terms in the high-throughput GEO database and collected disease-related gene expression profile chips. After analyzing and comparing the different chips, we selected the GSE121248 chip for analysis. This chip originated from the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array platform, which contains 70 liver cancer samples and 37 normal samples. TCGA is a joint project initiated by the American National Cancer Institute and the National Human Genome Research Institute in 2006. TCGA is the largest cancer gene database worldwide and contains a wealth of clinical information [16]. We searched TCGA for (and subsequently downloaded) liver cancer-related RNA-seq gene expression data and the corresponding clinical data files, which contained 373 tumor samples and 49 normal samples.

2.2. DEG-Based Disease Gene Analysis

We used the limma package (version: 3.42.2) in R language to analyze DEGs identified in the GSE121248 and TCGA data. Subsequently, we filtered out upregulated and downregulated DEGs from the GEO and TCGA chip data by using the following conditions: and . Finally, we employed the ggplot2 package (version: 3.3.1) and pheatmap package (version: 1.0.12) to draw volcano maps and heat maps corresponding to the DEGs, and we used Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html) to determine the intersection of TCGA and GEO data in order to derive the differential gene analysis-based gene set .

2.3. Weighted Gene Coexpression Network Disease Gene Analysis

We performed a weighted gene coexpression network analysis (WGCNA) to analyze the gene expression data from the GEO and TCGA. From this analysis, we identified modules and hub genes related to clinical phenotypes (i.e., normal and disease-based).

A gene coexpression network was constructed using a weighted gene expression correlation network analysis. The soft-threshold power was extracted from PowerEstimated and the topological overlap matrix (TOM). Subsequently, on the basis of the weighted gene expression correlation network, the TOM was used to cluster all the genes, and dynamic tree cutting was employed to identify various gene modules. To determine the correlation between the different modules of the clustered genes, a minimum gene module size of 50 and a dynamic shear height value of 0.25 were set. The different gene modules are represented by various colors. Finally, the correlation between different gene modules and clinical phenotypes was calculated, and a heat map of the correlation between the modules and clinical phenotypic traits was drawn. According to the correlation between the gene module and the normal and control groups, we determined modules with the highest positive and negative correlation values in the GEO and TCGA data, respectively. Subsequently, gene modules with the highest positive and negative correlation values in the GEO and TCGA data were intersected, and two intersecting genes were combined to constitute the HCC gene set GW through the WGCNA.

2.4. HCC Protein–Protein Interaction Network Construction and Analysis

We combined the HCC gene set obtained from the DEG-based analyses with the HCC gene set obtained from the WGCNA to form the liver cancer disease gene set ; these gene sets were imported into the STRING database (version: 11.0; https://string-db.org/). Regarding the parameter settings, we set the parameter “organism” to “homo sapiens” and set the “combine score” threshold to 0.9 to produce an interaction network of all gene targets. We then used Cytoscape (version: 3.7.2, https://cytoscape.org/) to produce a protein–protein interaction (PPI) network for the target proteins.

Furthermore, we used a Cytoscape-based CytoHubba plug-in to mine the core genes in the PPI network and selected the maximal clique centrality (MCC) algorithm for the network. We also used default values for the other parameters and calculated the core gene set in the liver cancer PPI network.

2.5. Gene Ontology Function and Kyoto Encyclopedia of Genes and Genomes Pathway Enrichment Analysis

The Gene Ontology (GO) resource is a standard, structured biological model constructed by the GO Consortium in 2000 and encompasses information on the biological processes, molecular functions, and cellular components of genes. A group of genes, rather than a single gene, typically participates in a biological process or pathway. Enrichment analyses are performed on the premise that if a biological process or pathway is known to be abnormal, genes that function together are extremely likely to be selected into the gene set related to this process or pathway.

We used OmicShare (http://www.omicshare.com/tools) to annotate the GO functions for the HCC gene set and applied the clusterProfiler package (version: 3.14.3) to conduct Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Moreover, we selected genes using the conditions and and employed Cytoscape to construct an HCC target–pathway network according to the enrichment analysis results.

2.6. Discovery of Key Disease Genes through Survival Analysis

We determined the key gene set through a survival analysis and included all genes that satisfied the following conditions in our subsequent analyses: (1) genes in the intersection between the gene sets and obtained from the DEG-based analyses and WGCNA, respectively, and (2) the core gene set obtained from the PPI network analyses described in Section 2.4.

We downloaded clinical data files from TCGA. After organizing the data, we used the survival package (version: 3.1) and survminer package (version: 0.4.7) to conduct survival analyses (where ) and plot gene survival curves.

2.7. Collection of Effective YCHD Ingredients and Assessment of Absorption, Distribution, Metabolism, Excretion, and Toxicity

We referenced data on YCHD ingredients collected in animal experiments performed in related studies [7]; we also collected chemical information or data on effective YCHD ingredients from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and organized them according to PubChem name and CID, molecular formula, and the simplified molecular-input line-entry system (SMILES) strings (hereafter referred to as canonical SMILES). Additionally, we used Advanced Chemistry Development, Inc. (ACD/Labs) software (version: 2019) and the SwissADME online prediction system (http://www.swissadme.ch/) to evaluate and analyze the absorption, distribution, metabolism, excretion, and toxicity (ADMET) of the ingredients.

2.8. Prediction and Identification of Potential Targets of Effective Ingredients

According to the canonical SMILES corresponding to the effective YCHD ingredients, we used four online prediction systems (i.e., HitPick (http://mips.helmholtz-muenchen.de/proj/hitpick), the similarity ensemble approach (SEA) (http://sea.bkslab.org/), SwissTargetPrediction (http://www.swisstargetprediction.ch/), and version 5.0 of the Search Tool for Interacting Chemicals (STITCH) (http://stitch.embl.de/)) to predict and select the potential targets of effective YCHD ingredients. The thresholds were as follows: ; ; ; and combined . Concurrently, all the predicted ingredients and targets were merged and sorted; the UniProt database (https://www.uniprot.org/) was used to verify target information, and Cytoscape was used to construct an ingredient–effect network for YCHD.

We obtained the intersection between the predicted target gene set and the disease gene set , identified the potential targets of YCHD effective in treating HCC, and obtained the intersection gene set . Additionally, we conducted survival analyses on all genes in the intersection gene set .

2.9. Calculation Model-Based Effective Ingredient Regulatory Network Analysis

In practice, some TCM ingredients do not exhibit direct effects on disease-related targets. However, such ingredients occasionally affect or regulate key disease targets by interfering with neighboring targets. Therefore, we adopted a network calculation model to detect small molecules consistent with the aforementioned description.

We downloaded a high-quality, all-human PPI background network integrating 15 commonly used databases and selected a high-quality PPI incorporating five types of evidence. The PPI network contained a total of 16,677 proteins and 243,603 interactive relationships, and it was subsequently set as the background network of the current study to determine the intensity of YCHD ingredients’ interference with and regulation of key genes and the key gene set .

2.9.1. Analysis of Key Gene Set Interference with the Target Sets of Different Ingredients

We performed the analyses by using three network topological distances (i.e., , , and ). These distances can be expressed as follows:

where is the target set of an ingredient, is the number of targets in the target set, is the key gene set, is the number of targets in the key gene set, and is the distance between two nodes in the PPI network.

2.9.2. Analysis of Network Proximity between the Key Gene Set and the Target Sets of Different Ingredients

can be expressed as follows:

where is the average distance between the targets of an ingredient, is the average distance between key genes, and is the average distance between the target set of an ingredient and the key target set. signifies that the target set of an ingredient and the key gene set are close together in the network topological structure, indicating that the ingredient can regulate the key gene set by interfering with the target set . signifies that the target set of an ingredient and the key gene set are far apart in the network topological structure, indicating that the ingredient cannot markedly regulate the key gene set . Therefore, by calculating , , , and , one can determine whether YCHD contains ingredients that directly or indirectly affect key disease targets by interfering in certain target (set) proteins, thereby achieving the goal of alleviating or curing diseases.

In this study, we used R language (version 3.6.2) and the igraph package (version 1.2.5) to complete the aforementioned programming, calculations, and analyses.

3. Results

3.1. DEG-Based Disease Gene Analysis Results

We used the limma package to conduct a DEG analysis on liver cancer data obtained from GSE121248 and TCGA. For GSE121248, we obtained 558 DEGs, 167 and 413 of which were upregulated and downregulated genes, respectively. For TCGA, we obtained 2713 DEGs, 1024 and 1689 of which were upregulated and downregulated genes, respectively. Figures 2(a)–2(d) illustrate the volcano and heat maps of the analysis results. By obtaining the intersection of TCGA and GSE121248 data, we identified that the two sets of data shared 493 genes ( set) (Figure 2(e)).

(a)

(b)

(c)

(d)

(e)

Figure 2: (a) Volcano map of the GEO-based DEG analysis. (b) Heat map of the GEO-based DEG analysis. (c) Volcano map of TCGA-based DEG analysis. (d) Heat map of TCGA-based DEG analysis. (e) Venn diagram of the intersection between the GEO and TCGA DEGs.

3.2. WGCNA Results for Disease Genes

We performed a WGCNA on the gene expression profile data collected from GSE121248 and identified that the modules most positively and negatively correlated with the normal and disease phenotypes were MEbrown (0.82) and MEturquoise (−0.72), which contained 691 and 33 genes, respectively. Conversely, the WGCNA performed on TCGA data revealed that the modules most positively and negatively correlated with the normal and disease phenotypes were MEturquoise (0.4) and MEblue (−0.76), which contained 4381 and 3538 genes, respectively (Figures 3(a)–3(d)).

(a)

(b)

(c)

(d)

(e)

(f)

Figure 3: (a) Dynamic tree cutting-based GEO gene module identification results. (b) Correlation between different GEO gene modules and phenotypes. (c) Dynamic tree cutting-based GEO gene module identification results. (d) Correlation between different TCGA gene modules and phenotypes. (e) Intersection between the positively correlated module collected from the GEO-based WGCNA (i.e., MEbrown) and that collected from TCGA-based WGCNA (i.e., MEturquoise). (f) Intersection between the negatively correlated module collected from the GEO-based WGCNA (i.e., MEturquoise) and that collected from TCGA-based WGCNA (i.e., MEblue).

We obtained the intersection between the positively correlated module collected from the GEO-based WGCNA (i.e., MEbrown) and that collected from TCGA-based WGCNA (i.e., MEturquoise), identifying 63 common genes. Subsequently, we obtained the intersection between the negatively correlated module collected from the GEO-based WGCNA (i.e., MEturquoise) and that collected from TCGA-based WGCNA (i.e., MEblue), identifying 150 common genes. We then combined the two sets of common genes (in total, 213 common genes) to form the WGCNA-based HCC gene set (Figures 3(e) and 3(f)).

3.3. HCC PPI Network Analysis Results

We combined the 493 genes (in gene set ) obtained from the DEG analysis with the 213 genes (in gene set ) obtained from the WGCNA to form a liver cancer disease gene set , which contained 670 genes. Subsequently, we imported the 670 genes into the STRING database to generate the HCC PPI network. We then applied the MCC algorithm for analysis, identifying 25 genes in the PPI network with much higher scores compared with those of the other genes. The 25 genes were thus determined to be core gene set in the HCC PPI network (Figures 4(a) and 4(b)).

Figure 4: (a) HCC PPI network. (b) The 25 crucial targets in the PPI network obtained using the MCC algorithm. In the figure, the degree value of the node is presented as a gradient from red to green (large to small) according to the degree of the target in the entire PPI network. The darker the color is, the greater the degree value of the node is.

3.4. Discovery of Key HCC Genes Based on Survival Analysis

We obtained the intersection between the DEG set and WGCNA gene set , obtaining 36 common genes. We then combined these genes with 25 core genes collected from the PPI network-based analyses, resulting in a total of 61 key genes (Gk) on which we performed a survival analysis that revealed 26 genes related to survival in Gk (Figure 5). These genes were ACADS, BUB1B, CCNA2, CCNB1, CDC20, CDK1, CENPE, CEP55, DLGAP5, FAM149A, KIF11, KIF20A, KIF4A, NDC80, NUF2, NUSAP1, PALM3, PBK, PRC1, RBP7, RRM2, SFN, SPINK1, TOP2A, TPX2, and TTK, as illustrated in Figure 6.

Figure 5: Flowchart of the key gene screening process.

Figure 6: Survival curves of the 26 survival-related genes.

3.5. ADMET Assessment Results for Effective YCHD Ingredients

We conducted a literature analysis and find that of the 41 chemical components entered into blood, 5 components could not be retrieved from PubChem CID information. We derived data on 36 small YCHD molecules and subsequently performed ADMET assessments on the effective ingredients by using ACD/Labs and SwissADME. The corresponding structure of each small-molecule compound and its corresponding canonical SMILES were obtained from the PubChem database and then imported into the ACD/LABS software and the SwissADME online prediction system to evaluate these active ingredients.

In the assessments, we evaluated indicators such as gastrointestinal absorption, bioavailability (%), and dose (), as presented in Table 1. Among the 36 molecules, 24 (66.7%) could be sufficiently absorbed by the stomach and intestines, and 22 (61.1%) had a bioavailability greater than 70%.

Table 1: ADMET assessment results and data on the 36 small YCHD molecules.

Number
Ingredient name
Molecular formula
PubChem CID
GI absorption
Bioavailability (%)

MOL01
Gallic acid
C7H6O5
370
High
7.73

MOL02
2,5-Dimethyl-7-hydroxy chromone
C11H10O3
5316891
High
99.36

MOL03
Cacticin
C22H22O12
5318644
Low
6.98

MOL04
Physcion
C16H12O5
10639
High
75.28

MOL05
Chimaphylin
C12H10O2
101211
High
89.68

MOL06
Chrysophanol
C15H10O4
10208
High
72.78

MOL07
Rhein
C15H8O6
10168
High
97.68

MOL08
Kaempferide
C16H12O6
5281666
High
98.78

MOL09
Villosolside
C16H26O9
127454
Low
3.69

MOL10
Scopoletin
C10H8O4
5280460
High
99.43

MOL11
4-Hydroxyacetophenone
C8H8O2
7469
High
99.52

MOL12
7-Methoxycoumarin
C10H8O3
10748
High
99.57

MOL13
Safflor yellow A
C27H30O15
6448299
Low
0.02

MOL14
5,6-Dimethoxy-7-hydroxy coumarin
C11H10O5
5316862
High
99.5

MOL15
Isofraxidin
C11H15O5
5318565
High
99.41

MOL16
Isoquercitrin
C21H20O12
5280804
Low
2.58

MOL17
Salicylic acid
C7H6O3
338
High
83.24

MOL18
Isorhamnetin-3-glucoside
C22H22O12
14704554
Low
7.84

MOL19
Scoparone
C11H10O4
8417
High
99.61

MOL20
Azelaic acid
C9H16O4
2266
High
10.58

MOL21
6-Demethoxycapillarisin
C15H10O6
5316511
High
99.2

MOL22
Capillarisin
C16H12O7
5281342
High
97.83

MOL23
Cirsimaritin
C17H14O6
188323
High
99.37

MOL24
Capillartemisin
C19H24O4
6439717
High
98.77

MOL25
Rhamnocitrin
C16H12O6
5320946
High
99.2

MOL26
Gardenoside B
C17H24O11
24721095
Low
1.68

MOL27
Picrocrocinic acid
C16H26O8
5320582
Low
1.6

MOL28
Genipingentiobioside
C23H34O15
3082301
Low
0.35

MOL29
Geniposide
C17H24O10
107848
Low
5.72

MOL30
2,4-Decadienal
C10H16O
5283349
High
99.66

MOL31
2-Ethyl-2-hexenal
C8H14O
5354264
High
99.69

MOL32
Isosyringinoside
C23H34O14
131752947
Low
0.22

MOL33
Naringenin
C15H12O5
667495
High
98.38

MOL34
Neochlorogenic acid
C16H18O9
7067335
Low
0.48

MOL35
Chlorogenic acid
C16H18O9
1794427
Low
0.48

MOL36
6-Hydroxy-7-methoxy-2H-chromen-2-one
C10H8O4
69894
High
99.36

3.6. Results of the Prediction and Identification of Small YCHD Molecular Targets

We imported all small YCHD molecules into four online prediction systems (i.e., HitPick, SEA, SwissTargetPrediction, and STITCH) by using the method described in Section 2.8 to predict molecular targets. We then used thresholds to perform screening, obtaining 24 small YCHD molecules and 105 potential targets. Information on the targets is presented in Table 2.

Table 2: YCHD molecular target information.

Numbers
Targets
Protein names
UniProt ID

Tar001
ABCB1
ATP-dependent translocase ABCB1
P08183

Tar002
ABCC1
Multidrug resistance-associated protein 1
P33527

Tar003
ABCG2
Broad substrate specificity ATP-binding cassette transporter ABCG2
Q9UNQ0

Tar004
ACHE
Acetylcholinesterase (AChE)
P22303

Tar005
ADORA1
Adenosine receptor A1
P30542

Tar006
ADORA2A
Adenosine receptor A2a
P29274

Tar007
ADORA3
Adenosine receptor A3
P0DMS8

Tar008
ADRA2C
Alpha-2C adrenergic receptor
P18825

Tar009
AKR1B1
Aldo-keto reductase family 1 member B1
P15121

Tar010
AKR1B10
Aldo-keto reductase family 1 member B10
O60218

Tar011
AKR1C2
Aldo-keto reductase family 1 member C2
P52895

Tar012
AKR1D1
Aldo-keto reductase family 1 member D1
P51857

Tar013
ALB
Albumin
P02768

Tar014
ALOXE3
Hydroperoxide isomerase ALOXE3
Q9BYJ1

Tar015
ALPI
Intestinal-type alkaline phosphatase
P09923

Tar016
APOB
Apolipoprotein B-100
P04114

Tar017
ATM
Serine-protein kinase ATM
Q13315

Tar018
BDNF
Brain-derived neurotrophic factor
P23560

Tar019
CA1
Carbonic anhydrase 1
P00915

Tar020
CA12
Carbonic anhydrase 12
O43570

Tar021
CA13
Carbonic anhydrase 13
Q8N1Q1

Tar022
CA14
Carbonic anhydrase 14
Q9ULX7

Tar023
CA2
Carbonic anhydrase 2
P00918

Tar024
CA3
Carbonic anhydrase 3
P07451

Tar025
CA4
Carbonic anhydrase 4
P22748

Tar026
CA5A
Carbonic anhydrase 5A
P35218

Tar027
CA5B
Carbonic anhydrase 5B
Q9Y2D0

Tar028
CA6
Carbonic anhydrase 6
P23280

Tar029
CA7
Carbonic anhydrase 7
P43166

Tar030
CA9
Carbonic anhydrase 9
Q16790

Tar031
CASP3
Caspase-3
P42574

Tar032
CAT
Catalase
P04040

Tar033
CBR1
Carbonyl reductase [NADPH] 1
P16152

Tar034
CCL2
C-C motif chemokine 2
P13500

Tar035
CES4A
Carboxylesterase 4A
Q5XG92

Tar036
CES5A
Carboxylesterase 5A
Q6NT32

Tar037
CSNK2A1
Casein kinase II subunit alpha
P68400

Tar038
CTDSP1
Carboxy-terminal domain RNA polymerase II polypeptide A small phosphatase 1
Q9GZU7

Tar039
CYP19A1
Aromatase
P11511

Tar040
CYP1A1
Cytochrome P450 1A1
P04798

Tar041
CYP1A2
Cytochrome P450 1A2
P05177

Tar042
CYP1B1
Cytochrome P450 1B1
Q16678

Tar043
CYP2C9
Cytochrome P450 2C9
P11712

Tar044
CYP2E1
Cytochrome P450 2E1
P05181

Tar045
CYP3A4
Cytochrome P450 3A4
P08684

Tar046
DECR1
2,4-Dienoyl-CoA reductase
Q16698

Tar047
DNM1L
Dynamin-1-like protein
O00429

Tar048
EIF2AK3
Eukaryotic translation initiation factor 2-alpha kinase 3
Q9NZJ5

Tar049
ELANE
Neutrophil elastase
P08246

Tar050
ERAP1
Endoplasmic reticulum aminopeptidase 1
Q9NZ08

Tar051
FTO
Alpha-ketoglutarate-dependent dioxygenase FTO
Q9C0B1

Tar052
FUT7
Alpha-(1,3)-fucosyltransferase 7
Q11130

Tar053
GATA3
Trans-acting T-cell-specific transcription factor GATA-3
P23771

Tar054
GFER
FAD-linked sulfhydryl oxidase ALR
P55789

Tar055
GGCX
Vitamin K-dependent gamma-carboxylase
P38435

Tar056
GLP1R
Glucagon-like peptide 1 receptor
P43220

Tar057
GSK3B
Glycogen synthase kinase-3 beta
P49841

Tar058
HMOX1
Heme oxygenase 1
P09601

Tar059
HSD17B1
Estradiol 17-beta-dehydrogenase 1
P14061

Tar060
IKBKB
Inhibitor of nuclear factor kappa-B kinase subunit beta
O14920

Tar061
IL6
Interleukin-6
P05231

Tar062
JUN
Transcription factor AP-1
P05412

Tar063
LDLR
Low-density lipoprotein receptor
P01130

Tar064
MITF
Microphthalmia-associated transcription factor
O75030

Tar065
MMP2
Matrix metalloproteinase-2
P08253

Tar066
MMP7
Matrix metalloproteinase-7
P09237

Tar067
MPO
Myeloperoxidase
P05164

Tar068
NFKB1
Nuclear factor NF-kappa-B p105 subunit
P19838

Tar069
NOS2
Nitric oxide synthase
P35228

Tar070
NOX4
NADPH oxidase 4
Q9NPH5

Tar071
NPR1
Atrial natriuretic peptide receptor 1
P16066

Tar072
P4HB
Protein disulfide-isomerase
P07237

Tar073
PPARA
Peroxisome proliferator-activated receptor alpha
Q07869

Tar074
PPARG
Peroxisome proliferator-activated receptor gamma
P37231

Tar075
PTGS1
Prostaglandin G/H synthase 1
P23219

Tar076
PTGS2
Prostaglandin G/H synthase 2
P35354

Tar077
RAPGEF1
Rap guanine nucleotide exchange factor 1
Q13905

Tar078
RELA
Transcription factor p65
Q04206

Tar079
RXRA
Retinoic acid receptor RXR-alpha
P19793

Tar080
SCD
Acyl-CoA desaturase
O00767

Tar081
SELE
E-selectin
P16581

Tar082
SERPINE1
Plasminogen activator inhibitor 1
P05121

Tar083
SHBG
Sex hormone-binding globulin
P04278

Tar084
SLC16A1
Monocarboxylate transporter 1
P53985

Tar085
SLC22A10
Solute carrier family 22 member 10
Q63ZE4

Tar086
SLC22A11
Solute carrier family 22 member 11
Q9NSA0

Tar087
SLC22A6
Solute carrier family 22 member 6
Q4U2R8

Tar088
SLC22A7
Solute carrier family 22 member 7
Q9Y694

Tar089
SLC22A8
Solute carrier family 22 member 8
Q8TCC7

Tar090
SLCO2B1
Solute carrier organic anion transporter family member 2B1
O94956

Tar091
TTR
Transthyretin
P02766

Tar092
TYR
Tyrosinase
P14679

Tar093
UCP1
Mitochondrial brown fat uncoupling protein 1
P25874

Tar094
UGT1A1
UDP-glucuronosyltransferase 1A1
P22309

Tar095
UGT1A10
UDP-glucuronosyltransferase 1A10
Q9HAW8

Tar096
UGT1A3
UDP-glucuronosyltransferase 1A3
P35503

Tar097
UGT1A6
UDP-glucuronosyltransferase 1-6
Q64435

Tar098
UGT1A7
UDP-glucuronosyltransferase 1A7
Q9HAW7

Tar099
UGT1A8
UDP-glucuronosyltransferase 1A8
Q9HAW9

Tar100
UGT1A9
UDP-glucuronosyltransferase 1A9
Q62452

Tar101
UGT2B15
UDP-glucuronosyltransferase 2B15
P54855

Tar102
UGT2B17
UDP-glucuronosyltransferase 2B17
O75795

Tar103
VCAM1
Vascular cell adhesion protein 1
P19320

Tar104
VEGFA
Vascular endothelial growth factor A
P15692

Tar105
XDH
Xanthine dehydrogenase/oxidase
P47989

We obtained the intersection between the 105 potential targets and the HCC disease gene set , which revealed that YCHD directly interfered with 17 HCC-related disease targets: AKR1B10, AKR1D1, CA2, CA5A, CCL2, CYP1A2, CYP2C9, CYP2E1, CYP3A4, PPARG, PTGS2, SELE, SERPINE1, SHBG, SLC22A7, UGT1A6, and XDH. A survival analysis performed on the 17 targets revealed that AKR1D1, CYP2C9, CYP2E1, CYP3A4, and SLC22A7 were related to the survival of patients with HCC (Figure 7(b)). The YCHD ingredient–target effect network is illustrated in Figure 7(a).

(a)

(b)

Figure 7: (a) Network of the 105 potential targets of 24 small YCHD molecules; the circles and triangles denote the targets and small molecules, respectively, and a greater node degree indicates a larger node size. (b) Five survival-related genes in YCHD targets.

3.7. Network Analysis of YCHD Ingredients Interfering with HCC-Related Disease Targets

3.7.1. Enrichment Analysis of YCHD Ingredients Interfering with HCC-Related Disease Targets

We performed an enrichment analysis on YCHD molecules and 17 HCC-related disease targets. A GO functional enrichment analysis revealed that the mechanisms underlying the effects of YCHD involved 17 biological processes, 7 molecular functions, and 9 cellular components (Figure 8(a)). The main YCHD effects included inhibition of abnormal cell proliferation, strengthening of the immune system, and facilitation of metabolism, possibly explaining the mechanism through which YCHD inhibits HCC. Through a KEGG enrichment analysis, we derived 14 pathways (Figure 8(b)): a cancer overview pathway (chemical carcinogenesis), an inflammatory signaling pathway (tumor necrosis factor (TNF) signaling pathway), and nine metabolic pathways (i.e., steroid hormone biosynthesis, drug metabolism—cytochrome P450, metabolism of xenobiotics by cytochrome P450, linoleic acid metabolism, retinol metabolism, drug metabolism—other enzymes, arachidonic acid metabolism, nitrogen metabolism, and pentose and glucuronate interconversions). Therefore, we deduced that the inflammatory signaling pathway and metabolic pathways were the most critical pathways for the delivery of the antiliver cancer effects of YCHD.

(a)

(b)

(c)

Figure 8: (a) Biological processes through which YCHD treats HCC. (b) KEGG pathways through which YCHD inhibits HCC. (c) YCHD ingredient–target–pathway network diagram. In (c), the triangles, circles, and squares represent small molecules, targets, and pathways, respectively, in which targets were enriched; the relationship between small molecules and targets and that between targets and pathways are represented by solid and dotted lines, respectively, and a greater node degree indicates a larger node size.

3.7.2. Construction and Analysis of the Network of YCHD Ingredients Interfering with HCC-Related Disease Targets

To determine the targets and pathways regulated by YCHD compounds, we constructed a compound–target–pathway network incorporating all compounds, targets, and signaling pathways (Figure 8(c)).

As illustrated in Figure 8(c), salicylic acid (MOL17; ) exhibited effects on five targets, naringenin (MOL33; ) exhibited effects on four targets, and gallic acid (MOL01; ) exhibited effects on three targets; moreover, UGT1A6 (), CYP3A4 (), CYP2E1 (), CYP2C9 (), CYP1A2 (), and CA2 () each contained one to two small molecules that exhibited effects on targets. However, many of these targets were enriched in multiple pathways. Of the 17 targets, 6 were enriched in the chemical carcinogenesis () signaling pathway.

The nitrogen metabolism and bile secretion signaling pathways () were linked to seven chemical molecules; chemical carcinogenesis () was linked to six chemical molecules; steroid hormone biosynthesis, drug metabolism—cytochrome P450, and xenobiotic metabolism by cytochrome P450 () were linked to five chemical molecules; retinol metabolism, drug metabolism—other enzymes, and linoleic acid metabolism () were linked to four chemical molecules; and the AGE-RAGE signaling pathway in diabetes, pentose and glucuronate interconversions, arachidonic acid metabolism, and the TNF signaling pathway () were linked to three chemical molecules. The remaining signaling pathways were linked to at least two chemical molecules.

3.8. Regulatory Network Analysis of YCHD Ingredients Interfering with Key Targets

was used to determine whether small YCHD molecules affected and regulated the key target set in the Gk set. The results indicated that 17 small YCHD molecules interfered with the key target set (Table 3).

Table 3: Regulatory network analyses and calculation results of small YCHD molecules interfering with key targets.

MOL

MOL17
2.68
2.09
5.11
-2.09

MOL01
2.59
1.87
4.99
-2.01

MOL33
2.53
2.09
4.94
-1.98

MOL02
2.81
2.19
5.33
-1.95

MOL16
2.58
2.14
5.01
-1.84

MOL10
2.60
2.15
5.03
-1.75

MOL12
2.88
2.22
5.42
-1.71

MOL07
2.40
2.03
4.79
-1.54

MOL29
2.42
1.94
4.78
-1.53

MOL36
2.48
2.21
4.91
-0.98

MOL08
2.59
2.25
5.03
-0.88

MOL23
2.87
2.24
5.44
-0.86

MOL19
2.16
2.04
4.52
-0.64

MOL03
2.64
2.22
5.11
-0.49

MOL34
2.64
2.22
5.11
-0.49

MOL35
2.64
2.22
5.11
-0.49

MOL06
2.48
1.93
4.82
-0.32

The results obtained using three distance formulas revealed that the 17 small molecules had interference values similar to those of the key target set. Among the 17 small molecules, MOL02 (2,5-dimethyl-7-hydroxy chromone), MOL12 (7-methoxycoumarin), MOL17 (salicylic acid), and MOL23 (cirsimaritin) interfered with the target set most profoundly (Figure 9).

Figure 9: Comparison of the interference values of the 17 YCHD small molecules and those of the target set obtained using the three distance formulas.

Among the 34 targets directly acted on by the four small molecules, we discovered six genes, namely, CA2, CA5A, CYP2C9, PTGS2, SLC22A7, and XDH, to be related to HCC. In addition, BCB1, ADORA1, ADORA2a, ADORA3, AKR1B1, AKR1C2, ALB, ALOXE3, CA1, CA12, CA14, CA3, CA4, CA5B, CA6, CA9, CAT, CES4A, FUT7, IKBKB, MPO, NPR1, PTGS1, SLC16A1, SLC22A11, SLC22A6, SLCO2B1, and TTR were non-HCC-related genes (Figure 10). However, the results revealed the inherent complex mechanism of action, that is, these four small molecules could indirectly regulate 25 key genes related to HCC survival by disturbing 208 proteins in the PPI network by acting on these 34 targets.

Figure 10: Targets acted on directly by four YCHD small molecules, core targets related to survival, and targets interfered with. Purple triangles represent the four small YCHD molecules, dark green octagons represent the 34 potential targets that the four small molecules directly acted on, ellipses represent the proteins that interfered with the 34 direct action targets in the human PPI network, and the V shapes represent the 25 key genes associated with HCC survival in Gk.

4. Discussion

HCC is a complex disease whose onset and progression is related to multiple proteins and pathways [17]. TCM compounds contain numerous ingredients that affect multiple targets and exhibit diverse, multipathway pharmacological activities [18] that may assist in HCC treatment. Hanahan et al. proposed 10 theories of tumor hallmarks according to modern tumorigenesis and tumor development mechanisms [19]. Because TCM has a complex composition and affects various targets, it may simultaneously interfere with multiple tumor hallmarks.

TCM compounds contain numerous ingredients and involve complex mechanisms that may be linked to multiple targets and multiple pathways. In TCM, compounds without certain pharmacokinetic properties cannot reach the target organs; thus, they cannot effectively exhibit their biological activity. In a preliminary study, we observed that OB screening and DL assessments were the most favorable predictors of compounds with biological activity; however, the molecule effects were minimal if the molecule content was low or if the molecules metabolized quickly. In oral medication, ingredients that enter the bloodstream contribute the most to medicinal effects. Because of the complex characteristics of TCM, comprehensively studying the internal mechanisms of TCM compounds is difficult. Nevertheless, the use of bioinformatics methods to analyze TCM compound ingredients absorbed into the bloodstream may be a viable approach to determining the properties of such complex mechanisms. Thus, the current study employed such an approach to demonstrate the pharmacological mechanisms through which YCHD interferes with HCC.

YCHD is a traditional decoction commonly used in clinical practice. The current study used the network pharmacology method to study the anti-HCC effects of YCHD. We posit that YCHD demonstrates inhibitory effects on HCC by directly regulating the metabolism, inflammation, and signaling transduction pathways. Among the 36 YCHD ingredients absorbed into the bloodstream, 17 were directly related to liver cancer targets and inhibited HCC through interference with 14 pathways. For example, the inflammation-related signaling pathways have been identified to play a key role in the treatment and prevention of HCC [20]. Our research also revealed that MOL17 (salicylic acid), MOL29 (geniposide), and MOL33 (naringenin) can be used to treat HCC by regulating the TNF signaling pathway. Two studies have reported that geniposide [21] exerts anti-HCC effects by suppressing vascular endothelial growth factor expression and angiogenesis and that naringenin [22] suppresses the invasiveness and metastatic potential of HCC by inhibiting multiple signal transduction pathways. Energy metabolism may also play a critical role in the inhibition of HCC [23]. Our study revealed that 12 YCHD compounds affected 9 metabolism pathways. Five of these compounds can be used to treat HCC by regulating the energy metabolism pathway (i.e., MOL01 (gallic acid), MOL02 (2,5-dimethyl-7-hydroxychromone), MOL12 (7-methoxycoumarin), MOL16 (isoquercitrin), and MOL17 (salicylic acid)).

We identified 17 HCC targets directly affected by YCHD, of which 5 genes (AKR1D1, CYP2C9, CYP2E1, CYP3A4, and SLC22A7) were associated with the prognosis of patients with HCC. AKR1C3 plays crucial roles in multiple cancers, and a low expression level of AKR1D1 predicted poor prognosis and short median survival time [24]. CYP2C9 is involved in the metabolism of many carcinogens and drugs, and CYP2C9 was downregulated in human HCC progression [25]. CYP2E1 is a unique gene expressed in the liver but not expressed in HCC [26]. CYP3A4 expression is significantly low in the liver tumor tissue of patients with HCC [27]. Low SLC22A7 expression indicates a high risk of poor prognosis [28]. These five protein targets may be the prognostic targets for HCC and the targets of YCHD in the treatment of HCC. The network pharmacology analysis results demonstrate that MOL17 (salicylic acid) regulates SLC22A7 and CYP2C9, MOL20 (azelaic acid) regulates AKR1D1, MOL29 (geniposide) regulatesCYP2E1, and MOL33 (naringenin) regulates CYP3A4. In addition, gallic acid [29], physcion [30], rhein [31], isofraxidin [32], geniposide [21], naringenin [22], and chlorogenic acid [33] have been reported to exhibit anti-HCC effects.

The current study investigated how YCHD ingredients regulated and interfered with key targets; the results reveal similar trends in how the 17 small molecules interfered with the key target set. Among the 17 small molecules, MOL02 (2,5-dimethyl-7-hydroxychromone), MOL12 (7-methoxycoumarin), MOL17 (salicylic acid), and MOL23 (cirsimaritin) exhibited the most profound interference with the target set.

The current study adopted the network pharmacology method and used YCHD ingredients absorbed into the bloodstream to predict the targets of YCHD and construct meaningful pathways. In addition, the relationships between YCHD compounds, targets, and diseases were consolidated, and the multi-ingredient and multitarget characteristics of YCHD were analyzed. Finally, the mechanisms through which YCHD treat HCC were revealed.

In the current study, we combined bioinformatics and the network pharmacology method to predict which YCHD ingredients entered the bloodstream as well as the molecular targets and pathways through which YCHD treats HCC; the current results may serve as a basis for subsequent experimental studies. The specific mechanisms governing these ingredients should be examined through experiments in future studies.

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors have no conflicts of interest to declare regarding the publication of this article.

Authors’ Contributions

Jijia Sun and Tao Han contributed equally to this work.

Acknowledgments

This work was supported by the Shanghai Undergraduate Training Program on Innovation and Entrepreneurship (SUTPIE) grant (grant number 201810268032) and the Scientific Research Project of Traditional Chinese Medicine of the Shanghai Municipal Health Commission (grant number 2020JP002).

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