Abstract
Background:
18β-Glycyrrhetinic acid (18β-GA), a major bioactive triterpenoid from licorice, has demonstrated anti-cancer potential; however, its mechanisms against lung adenocarcinoma (LUAD) remain unclear.
Methods:
This study employed an integrated approach combining network pharmacology, bioinformatics, and experimental validation to investigate the anti-LUAD efficacy and underlying molecular targets of 18β-GA.
Results:
In vitro and in vivo assays demonstrated that 18β-GA dose-dependently inhibited LUAD cell proliferation, migration, and colony formation, while significantly suppressing tumor growth in a Lewis lung carcinoma (LLC) allograft model. By integrating network pharmacology with TCGA transcriptomic data, MET was identified as a core upstream target, and the PI3K/AKT signaling pathway was revealed as a significantly enriched pathway. Molecular docking and molecular dynamics simulations revealed that 18β-GA forms stable, high-affinity complexes in the catalytic pockets of both MET and AKT. Subsequent biological validations confirmed that 18β-GA attenuated the phosphorylation of critical signaling components, including MET, PI3K, AKT, and mTOR, without altering their mRNA or total protein expression. Crucially, functional rescue assays revealed that exogenous MET activation via its specific ligand HGF simultaneously reversed the 18β-GA-induced suppression of both p-MET and p-AKT. In contrast, the AKT agonist SC79 effectively restored p-AKT levels and reversed the anti-tumor efficacy of 18β-GA, but it failed to rescue the inhibition of upstream p-MET. This unidirectional reversal rigorously validates MET as the upstream regulator of AKT.
Conclusion:
18β-GA suppresses LUAD by targeting upstream MET to drive a top-down cascade blockade of the PI3K/AKT/mTOR signaling pathway, providing a pharmacological rationale for its application in LUAD targeted therapy.
1 Introduction
LUAD is the predominant histological subtype of lung cancer, accounting for approximately 40% of cases globally (; ). Lung cancer remains the leading cause of cancer-related mortality worldwide (GLOBOCAN, 2020), accounting for approximately 1.76 million deaths annually (). The incidence of LUAD has increased significantly, particularly in developing countries, due to increased exposure to environmental carcinogens (). Current treatments, including platinum-based chemotherapy, EGFR tyrosine kinase inhibitors, and PD-1/PD-L1 immune checkpoint inhibitors, improve patient outcomes but are hampered by a limited duration of response and frequent toxicities (Xia et al., 2017; ; Zhang et al., 2023). These toxicities are common, with approximately 60%–70% of patients experiencing grade ≥3 adverse events such as chemotherapy-induced myelosuppression, radiation pneumonitis, and immune-related endocrinopathies, which limits long-term treatment use (; ).
The PI3K/AKT signaling pathway is central to lung cancer initiation and progression, making it a crucial target for therapeutic interventions (). Dysregulated activation of this pathway is significantly associated with a higher clinical stage, poor differentiation, and lymph node metastasis, and notably, significantly shorter overall survival in LUAD patients, highlighting its value as a prognostic biomarker (; ; ). Beyond PI3K/AKT, aberrant activation of the MET receptor tyrosine kinase is another key therapeutic target in lung cancer. Upon binding to its ligand hepatocyte growth factor (HGF), MET activates multiple downstream pathways, including the PI3K/AKT pathway, to regulate cell proliferation, migration, invasion, and angiogenesis, thereby driving tumorigenesis and progression (; ; ). Consequently, targeting MET not only directly inhibits its kinase activity but also blocks this critical downstream pathway, offering a more comprehensive approach to suppress tumor growth and survival (). Therefore, developing agents that co-target both MET and PI3K/AKT presents a promising strategy for treating cancers driven by this interconnected signaling network.
Botanical drugs play a crucial role in the development of natural anti-cancer therapies (). Many studies have shown that plant-derived bioactive compounds can selectively inhibit malignant tumor cell proliferation while sparing normal tissues (). 18β-GA, the primary triterpenoid in licorice, exhibits a wide range of pharmacological effects (). 18β-GA confers anti-inflammatory effects by inhibiting the NF-κB pathway and demonstrates antioxidant activity by activating the Nrf2 pathway (; ; Wang et al., 2024). Furthermore, it exerts broad-spectrum antitumor effects by inducing endoplasmic reticulum stress, promoting mitochondrial apoptosis via Bcl-2/Bax modulation, and polarizing tumor-associated macrophages toward the M1 phenotype (Wang et al., 2016; ). Its efficacy has been demonstrated in various tumor models: inducing apoptosis in MCF-7 breast cancer cells, downregulating cyclin D1 in hepatocellular carcinoma, and inhibiting the Wnt/β-catenin pathway in colorectal cancer (; Wang et al., 2017; ). In LUAD, 18β-GA induces apoptosis by modulating related proteins—increasing pro-apoptotic Bax and Bim, decreasing anti-apoptotic Bcl-2, and activating caspases-9 and -3. It also triggers G2/M cell cycle arrest via p21 and p27 upregulation, thereby suppressing proliferation, and inhibits the MAPK pathway by reducing ROS levels (; Zhu et al., 2015; ). Despite these findings, the interactions between 18β-GA and key oncogenic drivers, particularly MET and the PI3K/AKT signaling pathway, remains poorly understood. Systematic investigation is essential to elucidate the regulatory mechanisms and functional impact of 18β-GA on this critical signaling axis.
Traditional network pharmacology is limited by its inability to capture tissue-specific gene expression, correlate with clinical prognosis, or resolve complex signaling pathway crosstalk. To overcome these limitations, we adopted an integrative multi-omics approach that combines clinically guided bioinformatic analysis of the TCGA-LUAD transcriptome with rigorous in vitro and in vivo validation. This strategy improves the accuracy of 18β-GA target identification and directly links pathway modulation to clinical outcomes. Elucidating 18β-GA’s action on the MET/AKT axis enables new therapeutic strategies for MET-driven and PI3K/AKT-pathway activated LUAD. By integrating botanical medicine with molecular oncology, this study provides a robust theoretical and experimental foundation for developing 18β-GA as a targeted therapy for LUAD.
2 Materials and methods
2.1 Cell lines and cell culture
The following cell lines were used: human LUAD A549 (CL-0016, Procell, China), NCL-H1299 (H1299, C6348, BDBIO, China), murine Lewis lung carcinoma (LLC, CL-0140, Procell, China), and human bronchial epithelial BEAS-2B (C5382, BDBIO, China). A549 and H1299 cells were cultured in RPMI-1640 medium (C22400500BT, Gibco, United States) supplemented with 10% heat-inactivated fetal bovine serum (FBS, 164,210, Procell, China). LLC and BEAS-2B cells were maintained in DMEM (C11995500BT, Gibco, United States) with 10% FBS.
2.2 Cell viability assays
18β-GA (HY-N0180, MedChemExpress, United States) was dissolved in DMSO. Cells were seeded, allowed to adhere, and treated with 18β-GA (0, 20, 40, 60, 80, and 100 μM) for 24 h. Cell viability was assessed using the Cell Counting Kit-8 (CCK-8; CK04, Dojindo, Japan) per the manufacturer’s protocol. The IC50 value for A549 cells was determined, based on which a high (IC50) and a low (0.5 × IC50) dose were selected for subsequent experiments.
2.3 Cell migration assays
Cell migration was assessed using Transwell chambers. Briefly, 7.0 × 104 serum-starved cells were seeded in the upper chamber with serum-free medium, while the lower chamber contained medium with 10% FBS as a chemoattractant. Both chambers were treated with high or low doses of 18β-GA. After 24 h, non-migrated cells were removed; migrated cells were fixed, stained with 0.1% crystal viole. The stained cells were then imaged and counted using a light microscope (DM1000, Leica, Germany).
2.4 Cell clone formation assays
Cells were trypsinized, counted, and seeded at 1 × 103 cells per well in 6-well plates. After 7 days of culture with medium refreshed every 2–3 days, colonies were fixed with 4% paraformaldehyde, stained with 0.1% crystal violet and photographed.
2.5 Immunofluorescence staining
Cell proliferation activity was evaluated by Ki67 immunofluorescence staining. Cells seeded on coverslips were treated with 18β-GA at low and high concentrations for 24 h. After treatment, cells were fixed with 4% paraformaldehyde (G1101, Servicebio, China) for 15 min at room temperature, permeabilized with 0.5% Triton X-100 (GC204003, Servicebio, China) for 10 min, and blocked with 5% bovine serum albumin (BSA, GC305010, Servicebio, China) for 1 h. Samples were incubated with anti-Ki67 primary antibody (27309-1-AP, Proteintech, China) at 4 °C overnight, followed by incubation with fluorescent secondary antibody (RGAR002, Proteintech, China) for 1 h at room temperature protected from light. The coverslips were mounted with an antifade mounting medium containing DAPI (S2110, Solarbio, China), and images were acquired using a fluorescence microscope.
2.6 Establishment of the lewis lung cancer model and drug administration
Twenty male C57BL/6 mice (4–6 weeks old, 16–18 g) were purchased from Charles River (China) and housed under specific pathogen-free (SPF) conditions. An LLC model was established by subcutaneous inoculation of each mouse with 1 × 107 LLC cells (in 0.2 mL of PBS) into the right flank (Figure 2A). Tumor size was measured every 48 h using digital calipers, and volume was calculated using the formula (length × width2)/2. On day 5 post-inoculation, when palpable tumors had formed, the mice were randomly divided into five groups (n = 4) using a random number table (Figure 2B): Model: saline (0.2 mL, ip, daily); 18β-GA low-dose (LD): 18β-GA (30 mg/kg, ip, daily); 18β-GA high-dose (HD): 18β-GA (60 mg/kg, ip, daily); DDP: cisplatin (3 mg/kg, ip, every 3 days); Combination: 18β-GA (60 mg/kg, ip, daily) + cisplatin (3 mg/kg, ip, every 3 days). The treatment period lasted for 14 days. Subsequently, all mice were deeply anesthetized by ip injection of pentobarbital sodium (50 mg/kg) and euthanized. Tumor tissues were excised, weighed, photographed, and processed for further analysis.
2.7 Network pharmacology and bioinformatics
2.7.1 Acquisition of drug and disease targets
Potential targets 18β-GA were retrieved from four databases: Traditional Chinese Medicine Systems Pharmacology (TCMSP; https://www.tcmsp-e.com/tcmsp.php), ETCM2.0 (http://www.tcmip.cn/ETCM2/front/#/), SwissTargetPrediction (http://www.swisstargetprediction.ch/), and PharmMapper (https://www.lilab-ecust.cn/pharmmapper/). Gene nomenclature was subsequently standardized using the UniProt database (https://www.uniprot.org/).
Disease targets associated with LUAD were acquired from three databases: GeneCards (https://www.genecards.org/), OMIM (https://omim.org/), and DisGeNET (https://disgenet.com/).
2.7.2 Identification of overlapping targets and functional enrichment analysis
The putative targets of 18β-GA and LUAD-associated targets were imported into the Xiantao Academic platform (www.xiantao.love) to identify common targets and construct a Venn diagram. Subsequently, these overlapping targets were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses using the DAVID tool (https://david.ncifcrf.gov) and the REACTOME database (https://reactome.org). Homo sapiens was set as the background species, with a significance threshold of p < 0.05. The enrichment results were plotted by https://www.bioinformatics.com.cn, an online platform for data analysis and visualization.
2.7.3 PPI network construction and hub genes identification
Overlapping genes between 18β-GA and LUAD targets were imported into the STRING database (https://cn.string-db.org/) to construct a protein-protein interaction (PPI) network using a high confidence interaction score (0.900). The network was visualized using Cytoscape (v3.10.1) software, with nodes ranked according to Degree centrality. Hub genes were further analyzed and screened using the CytoHubba plugin within Cytoscape (v3.10.1). To enhance result reliability, 11 topological analysis methods were comprehensively applied: Betweenness, Bottleneck, Closeness, Degree, DMNC, Eccentricity, EPC, MCC, MNC, Radiality, and Stress.
2.7.4 Analysis of TCGA-LUAD transcriptomic data
Transcriptomic and clinical data for LUAD were retrieved from the TCGA database (https://www.cancer.gov/ccg/research/genome-sequencing/tcga). This dataset includes tumor and normal tissue from LUAD patients. Differential expression analysis was performed using the DESeq2 package in R (v4.3.2) to identify differentially expressed genes (DEGs). Genes with an adjusted p-value <0.05 and absolute log2 fold change >1 were considered statistically significant.
2.7.5 Data screening and analysis of key genes
The overlapping genes between DEGs and hub genes were identified and visualized in a Venn diagram using the Xiantao Academic platform (https://www.xiantao.love/). The expression of these overlapping genes was then validated at the transcriptomic level using the GEPIA2 database (http://gepia2.cancer-pku.cn/#index), which integrates data from TCGA and the GTEx project to improve reliability. Genes with a statistically significant differential expression (p < 0.05) were subjected to overall survival (OS) analysis using the Kaplan-Meier Plotter platform (https://kmplot.com/analysis/). Genes demonstrating a significant correlation with poor prognosis (logrank p < 0.05) were defined as key genes. Finally, to further corroborate the differential expression of these key genes, a paired analysis was conducted by comparing tumor tissues with normal tissues from the TCGA-LUAD cohort using the “limma” and “ggpubr” R packages (v4.3.2).
2.7.6 Clinical Correlation analysis of key genes
Patients from the TCGA-LUAD cohort were stratified into high and low key gene expression groups based on the median expression value. This stratification was visualized using the “ComplexHeatmap” package in R (v4.3.2). The associations between these expression groups and clinicopathological characteristics (e.g., age, sex, overall stage, and TNM stage) were then assessed using the Chi-square test.
2.7.7 Molecular docking
The two-dimensional (2D) structure of 18β-GA was retrieved from the PubChem database (http://pubchem.ncbi.nlm.nih.gov/) and converted into its three-dimensional (3D) conformation using ChemOffice software. The resulting 3D structure was energy-minimized and saved in MOL2 format for docking studies. High-resolution crystal structures of target proteins were downloaded from the RCSB Protein Data Bank (PDB; http://www.rcsb.org/) to serve as receptor templates. These structures were preprocessed using PyMOL (v2.5) to remove water molecules, ions, and any co-crystallized ligands, retaining only the protein chains of interest. The cleaned structures were then saved in PDB format. These structures were preprocessed using PyMOL (v2.5) to remove water molecules, ions, and any co-crystallized ligands, retaining only the protein chains of interest. The cleaned structures were then saved in PDB format.
2.7.8 Molecular dynamics simulation
Molecular dynamics (MD) simulations were performed using the GROMACS 2022 software package. Topologies and force field parameters for the protein receptor were generated using the pdb2gmx tool and assigned according to the CHARMM36 force field. Parameters for the ligand were obtained from the CGenFF program. The receptor protein was parameterized with the CHARMM36 force field, while the ligand was described using CGenFF (). The solvated system was built by embedding the protein-ligand complex in a cubic TIP3P water box, with a minimum distance of 1.0 nm between the solute and the box boundary. The system was neutralized by adding appropriate ions using the gmx genion tool (). Long-range electrostatic interactions were treated using the Particle Mesh Ewald (PME) method with a Fourier spacing of 0.12 nm and a direct space cutoff of 1.0 nm. Covalent bonds involving hydrogen atoms were constrained using the LINCS algorithm. Simulations were integrated using the Verlet leapfrog algorithm with a time step of 2 fs. Energy minimization was conducted in two stages. First, the system was minimized for 3,000 steps using the steepest descent algorithm with positional restraints applied in a stepwise manner: initially, only water molecules were energy-minimized while restraining the solute atoms, followed by minimization of the entire system while restraining counterions. Second, an additional 2,000 steps of conjugate gradient minimization were performed without any restraints. Following minimization, production molecular dynamics simulations were carried out for 100 ns in the NPT ensemble at 310 K. The resulting trajectories were analyzed to evaluate the system’s stability and energetics. The following properties were calculated using the specified GROMACS tools: Root-mean-square deviation (RMSD) using gmx_rmsd; Root-mean-square fluctuation (RMSF) using gmx_rmsf; Hydrogen bonds (H-bonds) using gmx_hbond; Radius of gyration (Rg) using gmx_gyrate; Solvent-accessible surface area (SASA) using gmx_sasa; Free energy landscape (FEL) using gmx_sham.
2.8 Histological analysis of tumor tissue
Tumor tissue samples were fixed in 4% paraformaldehyde for 48 h at room temperature, dehydrated through a graded ethanol series, cleared with eco-friendly deparaffinization solution (G1128, Servicebio, China), and embedded in paraffin. Serial sections of 5 μm thickness were prepared, followed by deparaffinization and rehydration. The sections were stained with Hematoxylin and Eosin (H&E, G1005, Servicebio, China) according to standard protocols, followed by dehydration, clearing, and mounting with a synthetic resinous medium. Subsequently, the slides were imaged using a light microscope (DM1000, Leica, Germany).
2.9 Tissue immunohistochemistry (IHC)
Tumor sections (5 μm thickness) were deparaffinized using an eco-friendly clearing agent and rehydrated through a graded ethanol series. Antigen retrieval was performed in citrate buffer. Endogenous peroxidase activity was blocked by incubating sections with 3% H2O2 for 10 min. Sections were then incubated with a primary anti-Ki67 antibody overnight at 4 °C, followed by incubation with an HRP-conjugated secondary antibody. Color development was carried out using a DAB chromogen kit (E-IR-R101, Elabscience, China). Finally, sections were counterstained with hematoxylin, dehydrated, mounted, and imaged using a light microscope (DM1000, Leica, Germany).
2.10 Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR)
Total RNA was isolated with the Total RNA Isolation Kit (RC112, Vazyme, China). The concentration and purity of RNA were measured using a NanoDrop spectrophotometer. cDNA was synthesized from 1 μg of RNA with RT SuperMix containing gDNA Remover (R202, EnzyArtisan, China) under the following conditions: 37 °C for 15 min, and 85 °C for 5 s for enzyme inactivation. Quantitative PCR was performed using ChamQ Universal SYBR Master Mix (Q711, Vazyme, China). Each 20 μL reaction mixture consisted of 10 μL of Master Mix, 0.8 μL of forward primer, 0.8 μL of reverse primer, 2 μL of diluted cDNA, and 6.4 μL of nuclease-free H2O. The PCR protocol included an initial denaturation at 95 °C for 30 s, followed by 40 cycles of 95 °C for 10 s and 60 °C for 30 s. A melting curve analysis was conducted to verify amplification specificity. All samples were run in triplicate. The primer sequences are provided in Table 1. Gene expression levels were normalized to β-actin and analyzed via the 2−ΔΔCT method.
TABLE 1
| Gene name | Forward | Reverse |
|---|---|---|
| MET | AGCAATGGGGAGTGTAAAGAGG | CCCAGTCTTGTACTCAGCAAC |
| GAPDH | GTCAAGGCTGAGAACGGGAA | AAATGAGCCCCAGCCTTCTC |
Primer sequences used for RT-qPCR.
2.11 Western blot
Total protein was extracted from A549 cells, H1299 cells, and tissue samples using lysis buffer (PC101, Epizyme, China) supplemented with protease and phosphatase inhibitors (GRF101/GRF102, Epizyme, China). Protein concentration was determined by NanoDrop, and 30 μg of protein per sample was denatured with loading buffer (LT101S, Epizyme, China) at 100 °C for 5 min. Proteins were separated by SDS-PAGE on 7.5% gels (PG211, Epizyme, China) alongside protein markers (26,616, Thermo Fisher, United States; PL00003, Proteintech, China) at 80–120 V and subsequently transferred to PVDF membranes (IPVH00010, Millipore, United States; 0.45 μm) using a wet transfer system at 300 mA for 40–90 min at 4 °C. Membranes were blocked with 5% skim milk (PS112, Epizyme, China) in TBST for 1 h at room temperature, followed by incubation with primary antibodies overnight at 4 °C.
The primary antibodies used were as follows: AKT (60203-2-Ig, Proteintech, China), p-AKT (4060T, CST, United States), PI3K (60225-1-I, Proteintech, China), p-PI3K (AP0854, ABclonal, China), mTOR (T55306, Abmart, China), p-mTOR (T56571, Abmart, China), MET (25869-1-AP, Proteintech, China), p-MET (30737-1-AP, Proteintech, China), β-actin (66009-1-Ig, Proteintech, China).
After washing, membranes were incubated with HRP-conjugated secondary antibodies (SA00001-1/SA00001-2, Proteintech, China). Protein bands were visualized using an ECL substrate (PK10003, Proteintech, China), and β-actin was used as the loading control for normalization with Image Studio software.
2.12 In vitro HGF rescue assay
To verify the dependence of 18β-GA-mediated AKT inhibition on MET, HGF rescue experiments were performed. LUAD cells were seeded in culture dishes, grown to 70%–80% confluence, and then serum-starved for 24 h to quiesce basal kinase activity. The cells were divided into four groups. (1) Control group: cells were incubated with serum-free medium containing an equivalent volume of vehicle (0.1% DMSO) for 24 h (2) 18β-GA group: cells were pretreated with 18β-GA at the high concentration determined in Section 2.2 in serum-free medium for 24 h (3) HGF group: cells were incubated with serum-free medium containing vehicle (0.1% DMSO) for 24 h; 15 min before protein extraction, recombinant human HGF (rp175908; Aladdin, China) was added to a final concentration of 50 ng/mL, and the cells were stimulated for 15 min at 37 °C (Yano et al., 2008; ; ). (4) Combination group: cells were pretreated with 18β-GA as in the 18β-GA group for 24 h, and then stimulated with HGF (50 ng/mL, 15 min) in the same manner. After the respective treatments, cells were placed on ice, washed with cold PBS, and lysed for immunoblotting.
2.13 In vivo SC79 rescue assay
To investigate whether the antitumor effect of 18β-GA was mediated through inhibition of the AKT pathway, a rescue experiment was conducted using SC79 (T2274, TargetMol, United States), a cell-permeable AKT activator (Wen et al., 2020). To Sixteen tumor-bearing mice were randomly assigned to four treatment groups (n = 4; Figure 9A): Model: saline (0.2 mL, ip, daily); 18β-GA HD: 18β-GA (60 mg/kg, ip, daily); SC79: SC79 (5 mg/kg, ip, daily); Combination: SC79 (5 mg/kg, ip) administered 1 h prior to 18β-GA (60 mg/kg, ip, daily).
After 14 days of treatment, mice were euthanized, and tumor tissues were harvested 24 h after the final dose. The tumor tissues were then homogenized, and total protein was extracted for Western blot analysis. The phosphorylation levels of AKT and MET were assessed, and the ratios of p-AKT/AKT and p-MET/MET were quantified to evaluate pathway activity.
2.14 Statistical analysis
The data were analyzed and visualized using GraphPad Prism (v10.2.0) software. All experiments were independently repeated at least three times. The experimental results were expressed as mean ± standard deviation (Mean ± SD). Independent samples t-test was used for comparisons between two groups, and one-way ANOVA was used for comparisons among multiple groups. A p value <0.05 was considered statistically significant.
3 Results
3.1 18β-GA inhibits the proliferation of LUAD cells
18β-GA exhibited dose-dependent cytotoxicity in A549, H1299, and LLC cells, with IC50 values of 65.84, 62.80, and 64.47 μM, respectively (Figures 1A–C). Based on these results, high (65 μM) and low (32.5 μM) doses were selected for A549 and H1299 cells. 18β-GA significantly inhibited cell migration (Transwell; Figures 1D,E), reduced Ki67 expression (immunofluorescence; Figures 1F,G), and decreased clonogenic survival (colony formation; Figure 1H), demonstrating potent anti-migratory and anti-proliferative effects.
FIGURE 1
3.2 18β-GA inhibited LLC allograft tumor growth in vivo with a favorable safety profile
Following the 2-week treatment, mice were euthanized and tumors collected (Figure 2C). 18β-GA dose-dependently reduced final tumor weight, most notably in the high-dose (HD) and combination (18β-GA HD + DDP) groups (Figure 2D). Tumor volume measurements throughout the study confirmed significant growth inhibition in the 18β-GA HD, DDP, and combination groups versus the model group (p < 0.05; Figure 2E). Notably, 18β-GA’s antitumor efficacy was accompanied by minimal systemic toxicity. Body weight remained stable in 18β-GA–treated groups (within ±5% of baseline), similar to the model group. In contrast, DDP monotherapy caused marked weight loss from day 9 (Figure 2F), highlighting 18β-GA’s superior safety profile.
FIGURE 2
Histopathology further corroborated 18β-GA’s efficacy. H&E staining showed extensive tumor damage, featuring nuclear pyknosis and tissue architecture loss (Figure 2G). IHC analysis revealed a marked decrease in Ki-67-positive cells (Figures 2H,I), confirming potent suppression of proliferation. Collectively, 18β-GA exerts potent and well-tolerated antitumor activity against LLC allografts in vivo.
3.3 Prediction of key pathways and hub genes for 18β-GA-mediated LUAD suppression by network pharmacology
A comprehensive screening approach identified 378 potential binding targets of 18β-GA from pharmacological databases (TCMSP, ETCM 2.0, SwissTargetPrediction, PharmMapper) and 1,840 LUAD-related genes from disease databases (GeneCards, OMIM, DisGeNET). Their intersection revealed 109 overlapping genes (Figure 3A), representing candidate therapeutic targets of 18β-GA in LUAD. A drug-target-disease network constructed in Cytoscape (v3.10.1, Figure 3B) illustrated the multi-target mechanism of 18β-GA and highlighted central hub genes potentially critical to its inhibitory effects.
FIGURE 3
GO enrichment analysis of these 109 genes identified 1,576 biological processes (BP), 74 cellular components (CC), and 136 molecular function (MF) terms. The top five terms per category are shown in Figure 3C. BP results were primarily associated with transcriptional regulation by RNA polymerase II. CC terms indicated alterations in subcellular localization, including the plasma membrane, cytoplasm, and nucleoplasm. MF analysis implied suppression of proliferation pathways and reversal of apoptosis inhibition.
KEGG analysis revealed 185 enriched pathways, with the top 10 shown in Figure 3D. The PI3K/AKT signaling pathway was identified as a key regulatory axis. REACTOME analysis confirmed this finding, showing significant enrichment in “Negative Regulation of the PI3K/AKT Network” and “PI3K/AKT Signaling in Cancer” (Figure 3E), suggesting that 18β-GA likely suppresses LUAD proliferation via the PI3K/AKT pathway.
A PPI network of the 109 overlapping genes was constructed with STRING (109 nodes, 238 edges; Supplementary Figure S1) and analyzed in Cytoscape. Node sizes were proportional to degree centrality (Figure 3F). Hub genes identified by CytoHubba (Supplementary Table S1) were integrated across algorithms. After duplicate removal, 28 core hub genes were selected for further validation (Supplementary Table S2).
3.4 Analysis of TCGA-LUAD transcriptome data and hub genes validation
Analysis of TCGA-LUAD data identified 9,088 differentially expressed genes (DEGs) (|log2FC| > 1, adjusted p < 0.05), comprising 6,504 upregulated and 2,584 downregulated genes (Figures 4A,B). Intersection of these DEGs with the 28 hub genes revealed six overlapping candidates: IL6, FGFR2, ERBB4, JAK3, MET, and MMP9 (Figure 4C; Supplementary Table S3). These genes, at the intersection of network pharmacology predictions and LUAD transcriptomic alterations, were selected for further investigation of 18β-GA’s mechanism.
FIGURE 4
Validation using GEPIA2 and GTEx databases confirmed significant differential expression of IL6, JAK3, MMP9, and MET in LUAD (Figure 4D). Subsequent Kaplan–Meier survival analysis showed that only high MET expression significantly correlated with poor prognosis (log-rank p < 0.05; Figure 4E), identifying MET as a key candidate for further study.
Analysis of paired tumor and normal tissues from TCGA-LUAD confirmed MET overexpression in tumors (Figure 4F). Clinically, the overview is shown in Figure 4G. MET expression showed no association with patient age (Supplementary Figure S2) but was significantly higher in females (p = 0.049; Figure 4H). No correlation was observed with M, N, or T stage (Supplementary Figures S3-S5); however, MET expression was elevated in advanced overall disease stages (Stage III vs. Stage I, p < 0.05; Figure 4I).
These results suggest that MET detection in early-stage (Stage I/II) LUAD could serve as a biomarker for identifying high-risk patients who might benefit from intensified monitoring or targeted therapy, potentially improving prognostic stratification and clinical outcomes.
3.5 Molecular docking revealed key interactions between 18β-GA and target proteins
AKT: Val90, Gly10, Glu9, Glu91, Pro24, and Glu98 were involved in van der Waals interactions; His13 participated in hydrophobic interactions; Trp99, Glu95, and Lys8 formed hydrogen bonds; Trp11 displayed Pi-Sigma stacking (Figure 5A; Supplementary Table S4).
FIGURE 5
MET: Glu1120, Lys244, Asp1228, His1088, and Phe1089 were involved in van der Waals interactions; Met1229, Pro1246, Tyr1235, Arg1203, Arg1227, and Ala1243 participated in hydrophobic interactions; Asp1204 and Asp1222 formed hydrogen bonds (Figure 5B; Supplementary Table S4).
3.6 Molecular dynamics simulations demonstrated system stability
RMSD analysis indicated that the AKT complex stabilized after 85 ns at ∼2.7 Å, while the MET complex reached stability after 90 ns with lower fluctuations, maintaining a value near 1.8 Å (Figure 5C). The Rg exhibited minor variations, indicating consistent compactness during simulation (Figure 5D). SASA changes suggested that ligand binding altered protein surface topology and hydrophobicity (Figure 5E). Hydrogen bond analysis confirmed an average of two bonds in both complexes, with dynamic fluctuations (AKT: 0–8; MET: 0–4), indicating stable binding (Figure 5F). RMSF values (Figures 5G,H) remained low (0.5–2.8 Å), supporting the rigidity of the 18β-GA-MET complex, which occupied a single deep energy well, indicating high stability. In contrast, the 18β-GA-AKT complex sampled multiple low-energy basins, suggesting conformational plasticity during binding (Figures 5I,J).
3.7 18β-GA inhibits MET phosphorylation
To validate the potential significance of MET as a target in LUAD as suggested by our transcriptomic analysis, we first assessed its expression and activation status. Interestingly, while bioinformatic predictions indicated an upregulation of MET mRNA in tumor tissues, our experimental data revealed that the total MET protein levels were comparable between the normal bronchial epithelial cell line BEAS-2B and the LUAD cell lines A549 and H1299 (Figures 6A,B). Strikingly, the phosphorylation level of MET (p-MET) was significantly elevated in both cancer cell lines compared to the BEAS-2B cells (Figures 6A,B). We next investigated whether the anti-tumor effect of 18β-GA was mediated through modulation of MET signaling. Treatment with 18β-GA did not alter the mRNA level of MET in either A549 or H1299 cells (Figures 6C,D), effectively ruling out transcriptional suppression as its mechanism of action. However, immunoblotting analysis demonstrated a potent and significant reduction in p-MET levels upon 18β-GA treatment in both cell lines (Figures 6E–H). These results suggest that 18β-GA may directly inhibit the kinase activity of MET or target its upstream regulators to suppress phosphorylation.
FIGURE 6
3.8 18β-GA suppresses LUAD proliferation through inhibition of the PI3K/AKT signaling pathway
To experimentally validate the key pathway identified by our network pharmacology analysis, we examined the effect of 18β-GA on the PI3K/AKT signaling pathway. Consistent with the predictions from our network pharmacology analysis, Western blot analysis demonstrated that 18β-GA treatment potently suppressed the phosphorylation of core pathway components. Specifically, the levels of phosphorylated PI3K (p-PI3K), AKT (p-AKT), and its key downstream target mTOR (p-mTOR) were significantly diminished in both A549 and H1299 cell lines (Figures 7A–D). These data provide strong experimental evidence that inhibition of the PI3K/AKT/mTOR cascade is a central mechanism through which 18β-GA exerts its anti-tumor effects.
FIGURE 7
3.9 Exogenous MET activation reverses 18β-GA-induced inhibition of AKT phosphorylation
To further elucidate whether the inhibitory effect of 18β-GA on AKT is directly dependent on the inactivation of upstream MET, we performed an in vitro functional rescue assay using the MET-specific ligand HGF. Western blot analysis revealed that, compared with the control group, single-agent treatment with 18β-GA significantly decreased the protein levels of p-MET and downstream p-AKT. However, in the combined treatment group, the exogenous addition of HGF not only successfully reactivated MET but also significantly counteracted the 18β-GA-induced inhibition of AKT phosphorylation (p < 0.05, Figures 8A,B). These results clearly demonstrate that exogenous MET activation can effectively reverse the 18β-GA-induced inactivation of AKT, thereby directly confirming that 18β-GA blocks downstream AKT signal transduction by inhibiting MET.
FIGURE 8
3.10 SC79-mediated reactivation of AKT rescues the anti-tumor effect of 18β-GA in vivo
To determine if the antitumor effect of 18β-GA depends on AKT signaling, mice received the AKT agonist SC79. After 2 weeks, all mice were weighed, euthanized, and tumors were excised and photographed (Figure 9B). Tumor weights were significantly smaller in the 18β-GA HD group than in the Model, SC79 monotherapy, and combination (18β-GA HD + SC79) groups (p < 0.05), which did not differ from each other (Figure 9C). Body weight was comparable across all groups (Figure 9D).
FIGURE 9
SC79-mediated AKT reactivation markedly reversed 18β-GA’s antitumor effect, as tumor volumes in the combination group increased to approximately 80% of the model group. Accordingly, p-AKT levels suppressed by 18β-GA were restored to baseline (p > 0.05) with SC79 co-treatment (Figure 9E). In contrast, 18β-GA–induced suppression of p-MET remained significant (p < 0.05) despite AKT reactivation, indicating that MET inhibition is independent of AKT signaling (Figures 9F–H). These results confirm that the PI3K/AKT pathway is essential for 18β-GA’s antitumor activity, while MET inhibition represents a concurrent, independent mechanism.
4 Discussion
By integrating bioinformatic analyses with in vitro and in vivo functional assays, this study systematically elucidated the molecular mechanisms by which 18β-GA inhibits LUAD progression via a top-down cascade blockade of the MET/AKT signaling axis (Figure 10). In both cellular and xenograft models, 18β-GA exhibited pronounced anti-proliferative and signaling blockade effects. These findings provide a robust pharmacodynamic basis for developing 18β-GA as a candidate molecule targeting aberrant MET activation, and further highlight the broader potential of natural pentacyclic triterpenoids for precise intervention against tumor-driving kinases.
FIGURE 10
We initially established its robust efficacy, demonstrating that 18β-GA potently inhibits tumor growth and metastasis both in vitro and in vivo, while exhibiting a more favorable safety profile than cisplatin. To elucidate the underlying mechanism, we employed an integrated computational approach. Network pharmacology and transcriptomic analysis of LUAD tissues predicted the PI3K/AKT signaling pathway as a central target, with MET emerging as a key upstream regulator. Molecular docking and dynamics simulations further substantiated this finding, revealing that 18β-GA could form stable, high-affinity complexes with both AKT and MET.
Guided by these predictions, we embarked on experimental validation. Intriguingly, while bioinformatics suggested MET mRNA overexpression, our protein-level analysis revealed a more nuanced reality: the oncogenic driver in our LUAD models was not an increase in total MET protein, but rather its marked hyperphosphorylation. We subsequently confirmed that 18β-GA directly targets this activated state, significantly suppressing MET phosphorylation without affecting its mRNA or total protein levels. This cascade of inhibition extended downstream, as 18β-GA treatment also dose-dependently reduced the phosphorylation of PI3K, AKT, and mTOR.
We performed dual rescue experiments using the MET-specific ligand HGF and the AKT agonist SC79 to define the hierarchical regulatory order between MET and AKT. We found that activation of MET by exogenous HGF not only successfully reactivated MET but also significantly reversed the 18β-GA-induced inhibition of AKT phosphorylation and the anti-tumor efficacy of 18β-GA. By contrast, co-treatment with SC79 effectively reversed the tumor-suppressive effects of 18β-GA and restored p-AKT levels, yet failed to abrogate the 18β-GA-induced suppression of p-MET in vivo. This unidirectional rescue outcome provides rigorous molecular evidence that MET functions as a critical upstream regulator of AKT.
Notably, aberrant activation of the MET signaling axis is not only a key factor driving the malignant progression of LUAD but also a major mechanism mediating acquired resistance to targeted therapies (). Clinically, although EGFR-TKIs, represented by gefitinib, have significantly improved the prognosis for a subset of patients, the emergence of acquired resistance remains the primary cause of treatment failure (). Previous studies have shown that following the inhibition of EGFR signaling, MET gene amplification or its aberrant kinase activation can activate bypass signaling (Westover et al., 2018), thereby circumventing TKI-blocked pathways via compensatory activation of critical downstream cascades, such as PI3K/AKT and RAS/RAF/MEK/ERK, and ultimately sustaining tumor cell survival and proliferation (; ).
This study demonstrated that 18β-GA can block the downstream PI3K/AKT/mTOR signaling cascade by targeting and inhibiting the activation of upstream MET. This intervention modality, which cuts off core survival signals at the source, suggests that 18β-GA holds promise for counteracting resistance signals driven by MET bypass activation, thereby restoring tumor cell sensitivity to EGFR-TKIs. Therefore, validating whether 18β-GA delays or reverses this acquired drug resistance represents a highly translatable follow-up of our present findings. To this end, future studies should establish MET-driven EGFR-TKI-resistant tumor models to systematically assess the synergistic antitumor efficacy of 18β-GA combined with clinically available targeted therapies (e.g., gefitinib, osimertinib) both in vitro and in vivo. Such follow-up work would not only facilitate the development of innovative combinatorial regimens to overcome bypass resistance triggered by single-agent kinase inhibitors, but also expand the translational prospects of natural small-molecule agents for precision LUAD therapy.
Despite these encouraging findings, several limitations of the present study warrant further investigation. First, although we used the TCGA-LUAD cohort and the GEPIA2 database to confirm the expression and prognostic significance of key targets, the mechanistic scope remains to be expanded. Future work should incorporate transcriptomic profiling of 18β-GA-treated versus untreated LUAD cells, followed by Gene Set Enrichment Analysis (GSEA). This would clarify whether the observed antitumor effects extend beyond the MET/AKT cascade to encompass broader biological processes such as cytokine production, extracellular matrix (ECM) remodeling, TNF signaling, and angiogenesis (). Additionally, validating MET upregulation and its association with poor survival in further independent datasets would strengthen the clinical relevance of our findings (; Zengin and Önal-Süzek, 2020). Second, with respect to correlation versus causal inference, the HGF rescue experiments provided critical functional support for targeted signaling; however, these results primarily constitute observational evidence derived from biological models. To establish definitive causality while controlling for confounding factors, future studies should integrate transcriptome-wide association studies (TWAS) with Mendelian randomization (MR). By leveraging eQTL and GWAS data, this strategy can systematically evaluate the causal roles of MET and AKT in LUAD prognosis from a genetic perspective. Such analyses should then be complemented by CRISPR-based gene knockout or knock-in functional assays (Zhang et al., 2025). Third, from a statistical modeling perspective, relying on MET and AKT as single-gene biomarkers inherently limits robustness and predictive performance. Future studies could address this by developing multi-gene prognostic signatures, particularly immune-related gene pair models that can effectively mitigate batch effects and platform differences (Xie et al., 2022). This methodology has been shown to yield robust prognostic performance across multiple independent cancer cohorts. Finally, this study did not explore the potential impact of 18β-GA on the tumor immune microenvironment or on the response to immunotherapy. Given the central role of immunotherapy in the clinical management of LUAD, subsequent research should employ single-cell sequencing technologies to characterize how 18β-GA reshapes the immune microenvironment. Moreover, it will be crucial to identify immune subtypes that predict differential responses to 18β-GA combined with immune checkpoint blockade (ICB) and to construct corresponding predictive models for immunotherapeutic efficacy (Zhang et al., 2024; ).
In conclusion, this study identifies 18β-GA as a highly promising therapeutic agent against LUAD, exhibiting potent in vitro and in vivo anti-tumor efficacy coupled with an excellent systemic safety profile. Mechanistically, 18β-GA docks into the core binding pockets of both MET and AKT with stable, low-energy conformations, significantly suppressing their active phosphorylation without altering total protein expression. Crucially, our functional rescue models revealed that 18β-GA halts tumor survival and proliferation by specifically inhibiting the upstream driver MET, thereby driving a “top-down” cascade blockade of the downstream PI3K/AKT/mTOR signaling pathway. Ultimately, the elucidation of this MET/AKT axis-targeted mechanism establishes 18β-GA as a robust and safe signaling interceptor, providing a solid theoretical foundation and a compelling pharmacological rationale for novel LUAD therapeutic strategies, including potential applications in overcoming MET-driven bypass resistance.
Statements
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: Direct link: https://portal.gdc.cancer.gov/; Repository: The Cancer Genome Atlas (TCGA); Accession number: TCGA-LUAD.
Ethics statement
Ethical approval was not required for the studies on humans in accordance with the local legislation and institutional requirements because only commercially available established cell lines were used. The animal study was approved by the Laboratory Animal Welfare and Ethics Committee of Mudanjiang Medical University. The study was conducted in accordance with the local legislation and institutional requirements.
Author contributions
GW: Validation, Conceptualization, Methodology, Data curation, Writing – original draft, Investigation, Formal Analysis, Software, Visualization. HG: Validation, Formal Analysis, Resources, Supervision, Investigation, Writing – original draft. ML: Formal Analysis, Validation, Writing – original draft, Investigation. ZC: Validation, Writing – original draft. CG: Validation, Writing – original draft. WS: Validation, Writing – original draft. HF: Resources, Data curation, Funding acquisition, Project administration, Conceptualization, Methodology, Writing – review and editing, Formal Analysis, Supervision.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This work was supported in part by the Doctoral Research Startup Fund of Mudanjiang Medical University (Grant No. 2021-MYBSKY-051), the Scientific Research Project of the Education Department of Heilongjiang Province (Grant No. 2020-KYYWFMY-0053), and the Scientific Research Project of the Health Commission of Heilongjiang Province (Grant No. 2020-421).
Acknowledgments
We sincerely thank HF for her invaluable guidance throughout this study. We are also grateful to all our colleagues and collaborators for their contributions. Additionally, we acknowledge the use of BioRender (https://www.biorender.com/) for creating the scientific figures.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcell.2026.1752632/full#supplementary-material
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Summary
Keywords
18β-glycyrrhetinic acid, bioinformatics, lung adenocarcinoma, MET, PI3K/AKT signaling pathway
Citation
Wu G, Guan H, Lv M, Chen Z, Gao C, Su W and Fu H (2026) 18β-glycyrrhetinic acid inhibits lung adenocarcinoma progression by targeting the MET/AKT signaling axis. Front. Cell Dev. Biol. 14:1752632. doi: 10.3389/fcell.2026.1752632
Received
23 November 2025
Revised
21 June 2026
Accepted
08 July 2026
Published
21 August 2026
Volume
14 - 2026
Edited by
Md. Imtaiyaz Hassan, Jamia Millia Islamia, India
Reviewed by
Guichuan Lai, Chongqing Medical University, China
Siladitya Khan, University of Rochester, United States
Updates
Copyright
© 2026 Wu, Guan, Lv, Chen, Gao, Su and Fu.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Hui Fu, fuhui@mdjmu.edu.cn
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.