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Introduction

Gastrointestinal cancer is a common cause of cancer-related mortality, of which gastric cancer exhibits the highest mortality rate of all the gastrointestinal cancers, largely due to the elusive nature of its early symptoms that lead to delays in treatment (1). Risk factors for gastric cancer include Helicobacter pylori infection, advancing age, excessive salt consumption and dietary imbalances (2). In early-stage gastric cancer, tumor infiltration typically remains confined to the mucosal or submucosal layers, irrespective of lesion size or lymph node metastasis (3,4). Timely detection and close monitoring of tumor progression are required to alleviate the disease burden and mitigate the mortality rates associated with gastric cancer (5). Presently, the increasing use of semi-invasive endoscopic and radiological techniques is expanding the number of treatable cases (6), and emerging studies aimed at discerning differentially expressed molecules are gaining traction in research (7,8). Initial attempts at employing dual therapy with first-line platinum drugs and the chemotherapeutic agent fluoropyrimidine yielded suboptimal outcomes in terms of patient survival, with median survival rates still being <1 year (2). By contrast, subsequent targeted therapeutic modalities approved for gastric cancer treatment, including trastuzumab, ramucizumab (an anti-angiogenic agent) and PD-1 monoclonal antibody, have shown promise (9). However, the prognosis for the disease remains poor, with the 5-year overall survival rate being ~25% across all stages and reducing to <5% for the late metastatic form of this type of cancer (10). Consequently, there is a need for enhanced therapeutic interventions for gastric cancer.

Lenvatinib, a multi-target tyrosine kinase inhibitor, exerts its inhibitory effects on vascular endothelial growth factor receptor 1-3, fibroblast growth factor receptor 1-4, platelet-derived growth factor receptors α and β (PDGFRB), and RET (11–13). Extensive research has indicated that lenvatinib possesses tumor-suppressive mechanisms (14). For example, it has been discovered to inhibit the proliferation of liver cancer cells both in vivo and in vitro, to curb the proliferation, invasion and migration of gallbladder cancer cells, and to promote apoptosis via AKT signaling (15). Furthermore, lenvatinib has demonstrated efficacy in inducing apoptosis and autophagy in human papillary thyroid cancer cells (16). However, investigations (17,18) into the effects of lenvatinib on gastric cancer remain scarce. Notably, a preclinical study has indicated its potential to impede the growth of xenografts sourced from patients with gastric cancer (17). In addition, a finding from a phase II clinical trial in Japan suggested favorable outcomes when lenvatinib was combined with pembrolizumab, an immune checkpoint inhibitor, in the treatment of advanced gastric cancer (18). These observations indicate a plausible role for lenvatinib in suppressing the malignant progression of gastric cancer cells.

The present study aimed to further investigate the effects of lenvatinib on gastric cancer cells, elucidating underlying mechanisms through a combination of bioinformatics analyses and experimental validation. The present study aimed to establish a theoretical basis for the clinical application of lenvatinib in gastric cancer treatment.

Materials and methods

Cell culture and treatment

Gastric adenocarcinoma MKN45 and gastric carcinoma HGC27 cells (Ningbo Mingzhou Biotechnology Co., Ltd.) were cultured in RPMI 1640 medium (Gibco; Thermo Fisher Scientific, Inc.) supplemented with 15% fetal bovine serum (Gibco; Thermo Fisher Scientific, Inc.) and 1% penicillin-streptomycin solution (Ningbo Mingzhou Biotechnology Co., Ltd.). The culture environment was maintained at 37°C with 5% CO2.

The cells were seeded into a 6-well plate at a density of 2×105 cells/well and were transfected with 2 µg pcDNA3.1 PDGFRB overexpression vector or an empty vector (NovoPro Bioscience, Inc.) using RFect reagent (Baidai; Changzhou EMI Biotechnology Co., Ltd.) at 37°C. After transfection for 48 h at 37°C, the cells were treated with lenvatinib (40 µM; Selleck Chemicals) for 24 h at 37°C.

Cell Counting Kit-8 (CCK-8) assay

MKN45 and HGC27 cells were seeded into 96-well plates at a density of 3×103 cells/well, and were incubated in the presence of gradient concentrations of lenvatinib (0–100 µM) for 24 h at 37°C. Subsequently, 10 µl CCK-8 reagent was added to the wells. The optical density was measured at a wavelength of 450 nm using a microplate reader (Tecan Group, Ltd.) after 1 h of incubation. The percentage cell inhibition rate (%) was calculated using the following formula: Cell inhibition=(OD value of control group-OD value of experimental group)/(OD value of control group-OD value of blank group) ×100.

5-Ethynyl-2′-deoxyuridine (EdU) staining

MKN45 and HGC27 cells were seeded into 96-well plates, and cell proliferation was assessed using the EdU staining kit (Beyotime Institute of Biotechnology). As aforementioned, MKN45 and HGC27 cells were treated with lenvatinib, transfected with Ov-PDGFRB or Ov-NC, and were then incubated with 10 µM EdU reagent for 2 h at 37°C. The cells were washed twice with phosphate-buffered saline (PBS), fixed with 4% paraformaldehyde for 15 min at room temperature and the nuclei were stained with DAPI. Images of the stained cells were captured under a fluorescence microscope (Olympus Corporation).

Colony formation assay

MKN45 and HGC27 cells treated with lenvatinib and transfected with Ov-PDGFRB or Ov-NC were inoculated into culture dishes (500 cells/dish) and evenly dispersed. After culturing the cells for 14 days, they were fixed with 4% paraformaldehyde for 30 min at room temperature and stained with 0.1% crystal violet (Selleck Chemicals) for 30 min at room temperature. Images of visible colonies (≥50 cells) were captured under a light microscope and colonies were counted using ImageJ software (version 1.8; National Institutes of Health).

Flow cytometry

Cell apoptosis was analyzed using an Annexin V-FITC Apoptosis Detection kit (BD Biosciences). After being treated with lenvatinib and transfected with Ov-PDGFRB or Ov-NC, the MKN45 and HGC27 cells were digested with 0.25% trypsin and washed twice with PBS. The cells were then suspended in binding buffer, and were incubated with 5 µl Annexin V-FITC for 30 min, followed by incubation with 5 µl propidium iodide for 5 min at room temperature in the dark. Apoptosis was analyzed using a FACSCalibur flow cytometer (BD Biosciences) and FlowJo software (version 10.7.2; FlowJo LLC).

Western blotting

After MKN45 and HGC27 cells were treated with lenvatinib and transfected with Ov-PDGFRB or Ov-NC, total proteins were extracted using RIPA lysis buffer (Beyotime Institute of Biotechnology) and were quantified using a Nanodrop spectrophotometer (Thermo Fisher Scientific, Inc.). Protein samples (30 µg) were separated by SDS-PAGE (10% gel; Bio-Rad Laboratories, Inc.). PVDF membranes carrying transferred proteins were then blocked with 5% skimmed milk for 1 h at room temperature, followed by an overnight incubation with primary antibodies against Bax (cat. no. 50599-2-Ig; 1:8,000 dilution; Wuhan Sanying Biotechnology), Bcl-2 (cat. no. 12789-1-AP; 1:9,000 dilution; Wuhan Sanying Biotechnology), PDGFRB (cat. no. 3169S; 1,000 dilution; Cell Signaling Technology, Inc.) and GAPDH (cat. no. 10494-1-AP; 1:20,000 dilution; Wuhan Sanying Biotechnology) at 4°C. Thereafter, the membranes were incubated with an HRP-conjugated goat anti-rabbit antibody (cat. no. SA00001-2; 1:5,000 dilution; Wuhan Sanying Biotechnology) at 37°C for 1 h. Signals were visualized using an ECL Western Blotting substrate kit (BioVision, Inc.) and were analyzed using ImageJ software (version 1.8; National Institutes of Health).

Molecular docking analysis

The structure of Lenvatinib was drawn in the ChemDraw software (version 18.0) and then imported into OpenBabel software (version 2.3.1) for hydrogenation, and converted into a mol2 format file. Subsequently, the structure of PDGFRB (PDB ID: AF-P09619-F1) was obtained from the RCSB PDB (https://www.rcsb.org/). Thereafter, the protein PDGFRB file was opened in PyMOL software (version 2.2.0) to remove the excess water molecules, delete any irrelevant small ligands originally carried and to keep only the protein structure. As the downloaded protein structure had ligands, the original ligands were deleted and the original ligand positions were set as the docking sites. AutoDock (version 1.5.6) (14) was used to display the specific docking energy value after running. Finally, the results were analyzed with the adoption of Protein-Ligand Interaction Profiler (PLIP; http://plip-tool.biotec.tu-dresden.de/plip-web).

Scratch assay

After being treated with lenvatinib and transfected with Ov-PDGFRB or Ov-NC, serum-starved MKN45 and HGC27 cells were grown until cells reached 90% confluence, and the central cells on the monolayer were scraped away using a 200-µl pipette tip. The distance of migration within 24 h was analyzed using ImageJ software (version 1.8) to calculate the migration rate under a light microscope.

Transwell assay

After being treated with lenvatinib and transfected with Ov-PDGFRB or Ov-NC, 1×105 MKN45 or HGC27 cells suspended in fresh serum-free RPMI 1640 medium were seeded into the upper chamber of Transwell plates (8-µm pore size; Costar; Corning, Inc.) precoated with Matrigel at 37°C for 30 min, and RPMI 1640 medium containing 20% FBS was added to the lower chamber. After a 24-h incubation at 37°C, cells that passed through the Matrigel were stained with 0.5% crystal violet at room temperature for 10 min and were captured under a light microscope. The results were analyzed using ImageJ software (version 1.8).

Bioinformatics analysis

Differentially expressed genes between gastric cancer tissue and paired normal tissue obtained from the GSE79973 (19) and GSE118916 (20) datasets from the GEO database (ncbi.nlm.nih.gov/gds) were determined using the Limma package in R software (version 4.1.2; http://www.bioconductor.org/packages/release/bioc/html/limma.html). The differentially expressed genes in gastric cancer were acquired from The Cancer Genome Atlas (https://www.cancer.gov/ccg/research/genome-sequencing/tcga). The target genes of lenvatinib were analyzed through TargetNet (http://targetnet.scbdd.com/) and SuperPred (https://prediction.charite.de/) databases. A Venn diagram was generated to display intersection genes, and the Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia.cancer-pku.cn/) was used to display the association between intersection genes and overall survival.

Statistical analysis

Statistical analysis was performed using GraphPad Prism software (version 9.5; Dotmatics) and quantitative data are presented as the mean ± SD of three independent experiments. One-way ANOVA and Tukey's post hoc test were used to determine statistical differences between multiple groups. P<0.05 was considered to indicate a statistically significant difference.

Results

Effects of lenvatinib on gastric cancer cell proliferation and colony formation

Gradient concentrations of lenvatinib (0–100 µM) were used to treat MKN45 and HGC27 cells, and their cell viability was measured at 24 h. According to the results of the CCK-8 assay, lenvatinib inhibited the viability of both cells in a concentration-dependent manner (Fig. 1A and B). Given that the IC50 values of lenvatinib in MKN45 and HGC27 cells were 30.75 and 29.03 µM at the 24-h time point, concentrations of lenvatinib at 0, 10, 30 and 40 µM were selected for subsequent assays. The effects of lenvatinib on cell proliferation were determined by EdU staining. Proliferation levels were quantified by assessing the proportion of EdU-positive cells. It was discovered that Lenvatinib markedly suppressed the proliferation of MKN45 and HGC27 cells in a concentration-dependent manner (Fig. 1C and D). In addition, lenvatinib inhibited colony formation and the number of colonies visible to the naked eye was reduced (Fig. 1E and F).

Figure 1. Effects of lenvatinib on gastric cancer cell proliferation and colony formation. Gradient concentrations of lenvatinib were applied to treat (A) MKN45 and (B) HGC27 cells, and their viability was measured at 24 h. Effects of lenvatinib on (C) MKN45 and (D) HGC27 cell proliferation were determined by EdU staining; scale bar, 50 µm. Effects of lenvatinib on colony formation of (E) MKN45 and (F) HGC27 cells were assessed. *P<0.05, **P<0.01, ***P<0.001. EdU, 5-ethynyl-2′-deoxyuridine.

Effects of lenvatinib on gastric cancer cell apoptosis and invasion

The effect of lenvatinib on MKN45 and HGC27 cell apoptosis was evaluated by flow cytometry. Lenvatinib increased the early and late apoptosis of both cells in a concentration-dependent manner (Fig. 2A and B). According to the results of western blot analysis, lenvatinib reduced Bcl-2 and increased Bax protein expression levels in cells, supporting the effect of lenvatinib on apoptosis (Fig. 2C and D). Scratch and Transwell assays were applied to evaluate the migration and invasion of the two cell lines, respectively. Lenvatinib significantly reduced the migration rate and the number of cells that invaded the matrix membrane, in both MKN45 (Fig. 3A and B) and HGC27 cells (Fig. 3C and D).

Figure 2. Effects of lenvatinib on gastric cancer cell apoptosis. Effects of lenvatinib on (A) MKN45 and (B) HGC27 cell apoptosis were evaluated by flow cytometry; comp-FL6-A and comp-FL7-A are the names of the channels. Western blot analysis revealed the effects of lenvatinib on the expression levels of apoptosis-related proteins in (C) MKN45 and (D) HGC27 cells. *P<0.05, **P<0.01, ***P<0.001.

Figure 3. Effects of lenvatinib on gastric cancer cell invasion. (A) Scratch and (B) Transwell assays evaluated the migration and invasion of MKN45 cells. (C) Scratch and (D) Transwell assays evaluated the migration and invasion of HGC27 cells. Scale bar, 100 µm for scratch assay; scale bar, 50 µm for Transwell assay. ***P<0.001.

PDGFRB is a potential target of lenvatinib

Volcano plots displaying the differentially expressed genes in gastric cancer are shown in Fig. 4A and B. The Venn diagrams exhibit the intersection of the predicted targets of lenvatinib and the differentially upregulated (Fig. 4C) or downregulated (Fig. 4D) genes in gastric cancer, suggesting that only 8 upregulated genes in gastric cancer can act as potential targets of lenvatinib. As depicted in Fig. 4E, the hazard ratios of these eight intersection genes were exhibited in the heatmap, and PDGFRB, which had the highest hazard ratio in gastric cancer, was screened out. Based on GEPIA, low- and high-expression PDGFRB groups were classified based on the median expression of PDGFRB, and it was shown that high PDGFRB expression was associated with poor survival (Fig. 4F); therefore, the effect of lenvatinib on PDGFRB was subsequently studied. Molecular docking analysis revealed that lenvatinib formed multiple hydrogen bonds with PDGFRB (Fig. 4G), indicating that lenvatinib bound well to amino acids in the protein pocket. Western blot analysis indicated that the expression levels of PDGFRB in MKN45 and HGC27 cells were reduced in a concentration-dependent manner upon lenvatinib treatment (Fig. 4H). Furthermore, PDGFRB was successfully overexpressed by transfection of MKN45 and HGC27 cells with a pcDNA3.1 PDGFRB overexpression vector, which was confirmed by western blotting (Fig. 4I).

Figure 4. PDGFRB is a potential target of lenvatinib. Volcano plots of differentially expressed genes in gastric cancer, as determined using data from (A) GSE79973 and (B) GSE118916 datasets. Venn diagrams exhibiting the intersection of differentially expressed genes in gastric cancer and target genes of lenvatinib, as shown for (C) upregulated and (D) downregulated genes. (E) Correlation between eight intersection genes and patient overall survival rate was analyzed, with a deep color indicating a close correlation. (F) Gene Expression Profiling Interactive Analysis showed the association between PDGFRB levels and overall survival. (G) Molecular docking revealed that lenvatinib bound to amino acids in the PDGFRB protein. (H) Western blotting indicated the expression levels of PDGFRB upon lenvatinib treatment in MKN45 and HGC27 cells. (I) Western blotting indicated the overexpression levels of PDGFRB after transfection in MKN45 and HGC27 cells. **P<0.01, ***P<0.001. NC, negative control; Ov, overexpression vector; PDGFRB, platelet-derived growth factor receptor β; STAD, stomach adenocarcinoma.

PDGFRB overexpression reverses the regulatory effects of lenvatinib

To explore the regulatory effects of lenvatinib on PDGFRB, cells were induced to overexpress PDGFRB, and the proliferation and colony formation of the cells were evaluated. The results demonstrated that PDGFRB overexpression promoted MKN45 and HGC27 cell proliferation (Fig. 5A and B) and colony formation (Fig. 5C and D), and reversed the inhibitory effects of lenvatinib. Flow cytometry (Fig. 6A and B) and apoptosis-related protein analysis (Fig. 6C and D) also revealed that PDGFRB overexpression reduced the proportion of apoptotic cells and the protein expression levels of Bax, and increased the protein expression levels of Bcl-2 compared with the lenvatinib 40 µM + Ov-NC group. The migration and invasion of MKN45 (Fig. 7A and B) and HGC27 cells (Fig. 7C and D) were also enhanced in response to PDGFRB overexpression, compared with the negative control group, upon treatment with lenvatinib.

Figure 5. PDGFRB overexpression reverses the regulatory effects of lenvatinib on cell proliferation. Proliferation of (A) MKN45 and (B) HGC27 and colony formation of (C) MKN45 and (D) HGC27 cells were evaluated upon PDGFRB overexpression. Scale bar, 50 µm. **P<0.01, ***P<0.001. NC, negative control; Ov, overexpression vector; PDGFRB, platelet-derived growth factor receptor β.

Figure 6. PDGFRB overexpression reverses the regulatory effects of lenvatinib on cell apoptosis. Effects of PDGFRB overexpression on (A) MKN45 and (B) HGC27 cell apoptosis were evaluated by flow cytometry; comp-FL6-A and comp-FL7-A are the names of the channels. Western blot analysis revealed the effect of PDGFRB overexpression on the expression levels of apoptosis-related proteins in (C) MKN45 and (D) HGC27 cells. *P<0.05, **P<0.01, ***P<0.001. NC, negative control; Ov, overexpression vector; PDGFRB, platelet-derived growth factor receptor β.

Figure 7. PDGFRB overexpression reverses the regulatory effects of lenvatinib on cell migration and invasion. (A) Scratch and (B) Transwell assays evaluated the effect of PDGFRB overexpression on the migration and invasion of MKN45 cells. (C) Scratch and (D) Transwell assays evaluated the effect of PDGFRB overexpression on the migration and invasion of HGC27 cells. ***P<0.001. Scale bar, 100 µm for scratch assay; scale bar, 50 µm for Transwell assay. NC, negative control; Ov, overexpression vector; PDGFRB, platelet-derived growth factor receptor β.

Discussion

The persistently high rates of mortality associated with gastric cancer underscore the ongoing limitations in treatment options. Patients with advanced gastric cancer often face challenges in pursuing radical surgical interventions (21), leaving combination chemotherapy as the predominant therapeutic option (22,23). Nonetheless, the existing array of chemotherapeutic agents remains limited in efficacy, while being markedly associated with toxicity and side effects (24). By contrast, molecular targeted therapies offer a promising alternative characterized by reduced toxicity and enhanced efficacy (25). Consequently, the aim to identify novel molecular targeted drugs has emerged as a focal point in contemporary gastric cancer research. The advent of small molecule tyrosine kinase inhibitors has heralded advancements in the management of various types of cancer, including gastric cancer (26). Within this area, the present study focused on lenvatinib, aiming to elucidate its potential in impeding the malignancy of gastric cancer cells. The experimental findings indicated the capacity of lenvatinib to inhibit the viability and proliferation of MKN45 and HGC27 cells while enhancing apoptosis. Furthermore, given the pivotal role of metastasis in dictating cancer outcomes, the impact of lenvatinib on the migratory capabilities of these cells was scrutinized. Notably, lenvatinib treatment exhibited a suppressive effect on the migration and invasion of both cell lines, underscoring its potential as a metastasis-inhibiting agent in gastric cancer.

Given the multi-target nature of lenvatinib (27), bioinformatics analyses were performed to determine its principal regulatory targets in gastric cancer. Through an intersectional analysis of lenvatinib-targeted genes and differentially expressed genes in gastric cancer tissues, eight genes of interest were identified. Subsequent analyses implicated PDGFRB as a prominent target, with data from the GEPIA database corroborating its inverse association with overall survival rates. Notably, prior research has underscored the pivotal role of PDGFRB in the metaplasia and dysplasia stages of gastric carcinogenesis (28). Moreover, synergistic interactions between PDGFRB blockade and anti-PD-1 immunotherapy have shown promise in impeding tumor growth, underscoring the interplay between stromal reprogramming and immune modulation in gastric cancer management (29). Another study has shown that PDGFRB is closely related to immune cell infiltration in gastric cancer, especially M2 macrophage infiltration (30). These studies all indicate the beneficial effects of inhibiting PDGFRB levels on disease management, supporting the potential use of lenvatinib for the treatment of gastric cancer. Although this study explored the potential mechanism of lenvatinib in the context of gastric cancer using two cell lines, it still has certain limitations, such as the lack of in vivo experimental data, which will be a part of future studies.

In conclusion, the present study identified the ability of lenvatinib to inhibit the malignant phenotype of MKN45 and HGC27 cells, with PDGFRB emerging as a pivotal mediator of its actions. Coupled with the findings of bioinformatics analyses, these results highlight PDGFRB as a primary target of lenvatinib in gastric cancer management. With more clinical research being performed on lenvatinib, it may have a future role in cancer therapeutics.

Acknowledgements

Not applicable.

Funding

Funding: No funding was received.

Availability of data and materials

The data generated in the present study may be requested from the corresponding author.

Authors' contributions

XT, JD and SC contributed to design, and performed experiments and manuscript drafting. QJ, QW and SZ contributed to methods and data analysis. XT and SC confirm the authenticity of all the raw data. All authors read and approved the final version of the manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References