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Article

Quercetin Inhibits the Progression of Gastric Cancer Through the AKT/MAPK Signaling Pathway

by
Guorong Yang
1,2,†,
Juwu Chen
3,†,
Xiangdi Yang
1,2,
Jifeng Tan
1,
Chengbin Liu
4,
Lingyu Li
5,
Xihua Yang
6,* and
Jianfu Zhao
1,*
1
Research Center of Cancer Diagnosis and Therapy, Department of Oncology, The First Affiliated Hospital of Jinan University, Guangzhou 510632, China
2
Department of Oncology, The First People’s Hospital of Chenzhou, Xiangnan University, Chenzhou 423000, China
3
Department of General Surgery, The First Affiliated Hospital of Jinan University, Guangzhou 510632, China
4
Luoding Hospital of Traditional Chinese Medicine, Luoding 527200, China
5
Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, College of Pharmacy, Cancer Research Institute, Jinan University, Guangzhou 510632, China
6
Department of Surgical Oncology, The First People’s Hospital of Chenzhou, Xiangnan University, Chenzhou 423000, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cancers 2026, 18(4), 603; https://doi.org/10.3390/cancers18040603
Submission received: 9 January 2026 / Revised: 6 February 2026 / Accepted: 10 February 2026 / Published: 12 February 2026
(This article belongs to the Special Issue Advances in Drug Delivery for Cancer Therapy)
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Simple Summary

Gastric cancer (GC) remains a leading cause of cancer-related death, and treatment options for advanced GC are limited. In this study, in vitro and in vivo experiments were performed to evaluate the effects of quercetin on AGS/MGC803 cells. The in vivo efficacy of quercetin was assessed using a BALB/c xenograft model. We identified 137 common targets, with SERPINE1 being the core target. Quercetin inhibits the AKT/MAPK signaling pathway and reverses epithelial–mesenchymal transition by upregulating E-cadherin and downregulating N-cadherin, thus inhibiting cell proliferation and migration. It also induces apoptosis by upregulating Bax expression and downregulating Bcl-2 expression. The in vivo experiments showed that quercetin treatment reduced the tumor volume by 78.4% without systemic toxicity. This study elucidates the multifaceted antitumor mechanism of quercetin derived from Epimedium, providing a promising therapeutic strategy for cancer treatment through precise targeting of SERPINE1.

Abstract

Background: Gastric cancer (GC) remains a leading cause of cancer-related mortality, with limited treatment options for advanced stages. This study systematically investigated the molecular mechanisms of quercetin, an active compound from Epimedium, against GC using network pharmacology and experimental validation. Methods: Active compounds were screened from Epimedium. GC targets from GeneCards and Epimedium targets were analyzed for overlap. Molecular docking was conducted using AlphaFold-predicted structures. Serpin family E member 1 (SERPINE1) expression, prognostic value, and immune correlations were analyzed using The Cancer Genome Atlas data. In vitro assays were performed to evaluate quercetin’s effects on AGS/MGC803 cells. BALB/c xenograft models were used to assess in vivo efficacy. Results: We identified 137 shared targets, with SERPINE1 as the core target. SERPINE1 was overexpressed in GC and correlated with poor prognosis and M2 macrophage infiltration. In vitro, quercetin dose-dependently inhibited cell proliferation, suppressed migration, and induced apoptosis through increasing Bax while decreasing Bcl-2 expression. It also inhibited AKT/MAPK signaling and reversed epithelial–mesenchymal transition by upregulating E-cadherin and downregulating N-cadherin. In vivo, quercetin treatment led to a 78.4% reduction in tumor volume without causing systemic toxicity. Conclusion: This study elucidated the multifaceted antitumor mechanisms of Epimedium-derived quercetin, which orchestrates dual suppression of tumor proliferation and immune microenvironment regulation through precise targeting of SERPINE1, offering a promising therapeutic strategy for cancer treatment.

Graphical Abstract

1. Introduction

Gastric cancer (GC) is a common malignancy worldwide, accounting for 4.9% of all cancer diagnoses and 6.8% of total cancer deaths [1]. Notably, epidemiological data reveal a sustained upward trend in early-onset GC (diagnosed before age 50) in recent years [2]. Beyond Helicobacter pylori infection, GC development is associated with hereditary predisposition and modifiable lifestyle factors, including tobacco use and alcohol consumption [3]. Despite the high incidence of GC, most patients are diagnosed at advanced stages due to subtle early symptoms and low rates of regular screening, resulting in poor prognosis [4]. Advanced GC treatments include surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy. Despite advancements in therapy, issues such as drug resistance, high rates of adverse reactions, and severe side effects continue to lead to unfavorable outcomes [5]. For instance, over 60% of Human Epidermal Growth Factor Receptor 2 (HER2)-positive patients develop resistance to trastuzumab within 12 months, underscoring the urgent need for novel agents [6]. Emerging evidence highlights the critical role of dysregulated signaling pathways in GC pathogenesis, particularly the PI3K/AKT/mTOR axis, which drives tumor proliferation and chemotherapy resistance [7]. Current research should focus on developing novel therapeutic agents with optimized efficacy–toxicity profiles through biomarker-guided precision strategies aiming to significantly improve clinical outcomes for advanced GC patients.
Given their distinct pharmacodynamic profiles, superior safety profiles, and low toxicity demonstrated in oncology and infectious disease management, natural products have garnered sustained scientific attention, emerging as promising alternatives to synthetic drugs and forming the cornerstone of diversified therapeutic strategies [8]. Natural products have resurged as a treasure trove for anticancer drug discovery, with approximately 60% of FDA-approved chemotherapeutics originating from botanical sources [9]. Epimedium is one of the largest species in the genus Epimedium, widely distributed in temperate mountainous regions from East Asia to Northwest Africa [10]. Epimedium contains diverse bioactive flavonoids, with icariin being the principal phytochemical marker and dominant constituent. These secondary metabolites exhibit a multidimensional bioactivity spectrum, including endocrine modulation via hormonal regulation, skeletal homeostasis maintenance against osteoporosis, neuropsychiatric regulation for depression alleviation, immune equilibrium preservation, and broad-spectrum antibacterial and antiviral capacities, coupled with antioxidant and anti-inflammatory cytoprotective effects [11,12,13]. Recent studies have increasingly highlighted the anticancer potential of Epimedium and its extracts, providing new directions and opportunities for further development of this herbal medicine [14,15]. Modern pharmacological studies reveal that Epimedium flavonoids, particularly icariin and quercetin, exhibit pleiotropic anticancer effects through epigenetic modulation, immune regulation, and metabolic reprogramming [16]. However, the specific pharmacological mechanisms and therapeutic potential of Epimedium in gastric cancer remain incompletely elucidated. Further research is required to clarify the interactions between its bioactive components and gastric cancer-related regulatory pathways.
Network pharmacology, as a systems biology approach integrating multi-omics data, demonstrates methodological resonance with Traditional Chinese Medicine (TCM) holism. This paradigm provides critical technical support for deciphering the synergistic therapeutic mechanisms of herbal medicines through multi-component, multi-target, and multi-pathway analyses. In this context, this study systematically revealed the molecular mechanisms of Epimedium against GC by integrating network pharmacology and experimental validation strategies. First, an active ingredient–target–disease interaction network for Epimedium was constructed using the TCMSP and GeneCards databases, identifying key GC targets such as SERPINE1 and TP53. Subsequent TCGA data analysis confirmed significant correlations between these targets and patient prognosis or the tumor immune microenvironment. Finally, in vitro and in vivo experiments validated that Epimedium’s primary active components targeting SERPINE1 synergistically regulate cellular proliferation and apoptosis through multi-target interactions, thereby elucidating its multi-pathway anti-GC mechanism.

2. Materials and Methods

2.1. The Bioactive Compounds and Target Compendium of Epimedium

The phytochemical constituents of Epimedium were systematically extracted from the TCMSP (https://www.tcmsp-e.com/, accessed on 10 March 2025) by employing stringent pharmacokinetic parameters, including an oral bioavailability (OB) threshold of ≥30% and drug-likeness (DL) score of ≥0.18 [17]. Target acquisition involved a multi-database retrieval strategy: (1) protein targets obtained from UniProt (https://www.uniprot.org/, accessed on 15 March 2025) were cross-referenced with official gene symbols from the HUGO Gene Nomenclature Committee (HGNC) [18]; (2) potential targets were predicted via the TCMSP and SwissTargetPrediction platforms (http://swisstargetprediction.ch/, accessed on 20 March 2025) [19].

2.2. Systematic Identification and Curation of Therapeutic Targets in GC

We searched for GC-related targets using the term “STAD” in the GeneCards database (www.genecards.org/, accessed on 22 March 2025), which contains comprehensive annotated human gene data. The program “Venny 2.1.0” (https://bioinfogp.cnb.csic.es/tools/venny/index.html/, accessed on 30 March 2025) was used to identify overlaps between the putative target genes of Epimedium and GC-related targets. These overlapping targets are considered potential therapeutic targets in Epimedium for GC intervention.

2.3. GO and KEGG Functional Enrichment Analysis

To investigate the shared targets, the Metascape platform was employed for pathway enrichment analysis to reveal the twenty most significantly enriched biological processes and signaling pathways, including Gene Ontology terms and KEGG pathways, in accordance with established protocols [20].

2.4. Drug-Target-Disease and PPI Network Construction

The active components and targets of Epimedium were imported into Cytoscape 3.10.0 software to construct an “Epimedium active components–targets–disease” network [21]. For PPI analysis, the overlapping targets were submitted to the STRING database (https://string-db.org/, accessed on 4 April 2025) with species restriction to Homo sapiens and the interaction confidence score set at 0.9 [21].

2.5. Key Gene Identification and Expression Profiling via TCGA Database

TCGA database supplied raw mRNA expression data comprising 448 gastric specimens (36 normal tissues vs. 412 carcinomas). The systematic analysis included (1) differential mRNA expression profiling of core genes between histologically normal and malignant gastric tissues, with (2) prognostic stratification via survival analysis (Cox proportional hazards regression) and diagnostic validation through ROC curve analysis, both conditioned on high/low expression thresholds of target genes.

2.6. Immune Infiltration Analysis

Utilizing GC datasets from TCGA, we executed a multi-modal analysis to delineate tumor–immune interactions through (1) immune cell profiling using Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) deconvolution for immune cell fraction quantification and the ssGSEA algorithm for pathway activity scoring; (2) correlation analysis between key gene expression (RNA-seq TPM values) and immune infiltration levels via linear regression (stats package) with multicollinearity diagnostics (car package); and (3) the identification of immune marker associations using alluvial plot visualizations (ggalluvial) and multivariate regression heatmaps (ggplot2). This workflow integrated bulk transcriptome deconvolution (CIBERSORT) with gene set enrichment (ssGSEA) to map gene–immune axis relationships in gastric adenocarcinoma.

2.7. Molecular Docking

The three-dimensional protein structures of essential genes were obtained from the AlphaFold Protein Structure Database (https://alphafold.com/, accessed on 5 April 2025), selecting entries with predicted local distance difference test (pLDDT) scores exceeding 90 to ensure structural integrity. Drug molecules in SDF format were downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov/, accessed on 6 April 2025). For molecular docking simulations, AutoDock 4.2 was implemented with Lamarckian genetic algorithm configurations including a population size of 150, maximum energy evaluations set at 2.5 million, and 50 separate docking experiments [22]. The visualization of molecular interactions in PyMOL 2.5.7 focused on the most stable conformation exhibiting minimal binding energy (ΔG), with particular emphasis on key residues located within a 4 Å radius of ligand–protein interactions.

2.8. Cells and Animals

The AGS and MGC803 cell lines were acquired from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). Female BALB/c nude mice aged 4 weeks were procured from Zhuhai Bestest Biotechnology Co., Ltd. (Zhuhai, China). All experimental procedures involving animals strictly adhered to institutional regulations and were ethically approved by the Laboratory Animal Welfare Ethics Committee of Jinan University, complying with the standards established in the “Guidelines for the Welfare and Use of Laboratory Animals”.

2.9. CCK-8 Assay

Cell proliferation was assessed with a CCK-8 assay kit (Meilunbio, China). AGS and MGC803 cells were plated into 96-well plates (2000 cells/well) and maintained overnight for adhesion. Following adhesion, cells received 24 h exposure to quercetin (TargetMol, Shanghai, China) at concentrations ranging from 0 to 80 μM (0, 0.3175, 0.625, 1.25, 2.5, 5, 10, 20, 40, 80 μM). Then, the instructions in the kit were followed to perform the corresponding steps.

2.10. EdU Assay

An evaluation of cell proliferation was carried out with the EdU detection kit (Beyotime Biotechnology, Shanghai, China). AGS and MGC803 cells were plated into 96-well plates at a density of 4 × 103 cells/well and maintained overnight to allow attachment. Post-adhesion, AGS cultures received quercetin treatments at 5 and 10 μM concentrations, whereas MGC803 cells were exposed to quercetin at 4 and 8 μM doses for 24 h periods. Staining procedures followed the kit’s standardized protocol.

2.11. Colony Formation Assay

AGS and MGC803 cells were plated into 6-well culture dishes at 2 × 103 cells per well and allowed to adhere under standard culture conditions for 24 h. Post-adhesion, AGS cultures received 5 and 10 μM quercetin treatments, whereas MGC803 cells were administered 4 and 8 μM concentrations of the compound, with subsequent maintenance for 5–7 days. The experiment concluded when the control group colonies developed from single cells attained sizes of approximately 80 cells per colony. The cellular monolayers underwent PBS washing (2 × 5 min), fixation with 4% paraformaldehyde solution (15 min duration), and staining with 0.1% crystal violet dye (20 min exposure).

2.12. Transwell Migration Assay

AGS and MGC803 cells underwent trypsin digestion, followed by centrifugation and reconstitution in serum-deprived media supplemented with varying doses of quercetin (5 and 2 μM for AGS cells; 4 and 2 μM for MGC803 cells). A 200 μL aliquot of cellular suspension (2 × 105 cells/mL density) was seeded into the upper compartment of a Transwell (Corning, 8 μm pores), with the lower chamber receiving 600 μL of FBS-enriched complete medium containing 10% serum. Following 24 h of culture at 37 °C, stationary cells remaining on the upper membrane surfaces were mechanically cleared using cotton applicators. The cell samples underwent fixation through 15 min immersion in 4% paraformaldehyde solution and were subsequently treated with 0.1% crystal violet staining solution for 20 min.

2.13. Flow Cytometric Analysis of Apoptosis

Apoptotic cells were assessed with an Annexin V-FITC/PI dual-labeling assay kit (Beyotime Biotechnology, China). Following quercetin exposure, AGS and MGC803 cell suspensions were prepared through meticulous adherence to the kit manufacturer’s standardized procedures.

2.14. Western Blot

Cellular disruption was performed with RIPA lysis buffer supplemented with PMSF protease inhibitor, followed by protein quantification through a BCA methodology employing a commercial assay kit (Vazyme, Nanjing, China). The immunoblotting procedures involved overnight incubation at 4 °C with Cell Signaling Technology (Danvers, MA, USA) antibodies targeting various proteins: AKT (4685), Bax (41162), Bcl-2 (4223), GAPDH (5174), MAPK (4695), p21 (2947), p27 (3686), p53 (2527), p-AKT (4060), p-MAPK (9101), E-cadherin (3195), N-cadherin (13116), Vimentin (5741), and p-p53 (9284). Subsequently, the membranes were incubated with HRP-conjugated secondary antibody (Cat:7074, Cell Signaling Technology, USA) for 1 h at room temperature.

2.15. Mouse Xenograft Model

A 100 μL solution containing 3 × 106 AGS cells was administered subcutaneously into the upper limb region of each mouse. Tumor dimensions were monitored weekly, with volumes calculated using the equation Volume = (Longest dimension × Shortest dimension squared)/2. Following a 10-day post-inoculation period, the animals were randomly divided into two cohorts—one receiving alternate-day intraperitoneal quercetin administration and the other receiving PBS injections, with both regimens maintained for 21 consecutive days. Biometric parameters, including neoplastic growth and animal mass, were tracked at three-day intervals throughout the study. Terminal euthanasia was performed at the 4-week endpoint, followed by comprehensive tumor specimen collection for subsequent experimental evaluations.

2.16. Statistical Analysis

All experiments were conducted in triplicate, and the processed data were evaluated using GraphPad Prism 8 software. The findings are presented as mean values ± standard deviations. To assess statistical significance, intergroup differences were examined through either Student’s t-test or one-way ANOVA methodologies.

3. Results

3.1. Study on the Anti-GC Effects of Epimedium and Screening of Hub Genes

In this study, 7014 GC-related targets were first extracted from the GeneCards database. Using the TCMSP database, a total of 23 bioactive components of the traditional Chinese medicine Epimedium were identified based on the predefined screening criteria (Table 1), yielding 176 drug-specific targets. The interaction network between bioactive drug components, diseases, and targets was visualized using Cytoscape (Figure 1A). Subsequently, the intersection between these drug targets and the aforementioned 7014 disease targets was analyzed, identifying 137 overlapping targets (Figure 1B). We further conducted pathway analysis on the overlapping targets. Metascape-based enrichment analysis revealed that these targets were primarily enriched in pathways including “Pathways in cancer”, “Lipid and atherosclerosis”, and “response to xenobiotic stimulus” (Figure 1C,D). Subsequently, the 137 overlapping targets were used to construct a PPI network via the STRING database, with only 82 targets exhibiting interaction relationships. Further analysis using MCODE identified 31 hub genes (Figure 1E,F).

3.2. Identification of Core Gene SERPINE1, Molecular Docking, and Its Expression and Clinical Significance in GC

A survival analysis based on high/low expression levels of the 31 hub genes demonstrated that SERPINE1 and TP53 were statistically significant (p < 0.05) (Figure 2A,B). Subsequently, we investigated the interactions between 18 active components of Epimedium and the target proteins SERPINE1 (P05121) and TP53 (P04637) through molecular docking. The results demonstrate that quercetin exhibited relatively low binding energies with both proteins: −4.70 kcal/mol for the TP53–quercetin complex and −6.20 kcal/mol for the SERPINE1–quercetin complex (Figure 2C,D). According to molecular docking principles, a lower binding energy indicates greater stability in protein–ligand interactions, suggesting that quercetin may possess potential binding affinity for these two targets. To investigate the expression pattern of SERPINE1 in GC, we analyzed TCGA database and found that SERPINE1 expression was significantly upregulated in GC tissues compared with normal controls (p < 0.05) (Figure 2E). In further diagnostic ROC curve analysis, SERPINE1 exhibited high predictive accuracy for GC (AUC = 0.875, 95%CI: 0.817–0.933), suggesting its potential as a diagnostic biomarker (Figure 2F).

3.3. Immune Infiltration

The tumor microenvironment (TME) is primarily composed of fibroblasts, immune cells, extracellular matrix, various growth factors, inflammatory cytokines, and unique physicochemical properties, which collectively influence disease diagnosis, prognosis, and therapeutic sensitivity. We presented the distribution of immune infiltration levels and correlations between immune cells in various formats before further analysis (Figure 3A,B). Immune infiltration analysis revealed that compared with normal tissues, GC samples exhibited significantly elevated levels of naive B cells, M0 macrophages, M1 macrophages, and activated CD4 memory T cells, whereas the levels of dendritic cells, resting mast cells, and plasma cells were markedly reduced (Figure 3C). These alterations in immune cell composition may contribute to GC pathogenesis. To further investigate the interplay between key genes and the immune microenvironment, we analyzed TCGA GC data and found that SERPINE1 exhibited broad correlations with the infiltration levels of 24 immune cell types (Figure 3E). A heatmap analysis further demonstrated strong positive associations between SERPINE1 and macrophage M2, B cell, and T cell subsets, suggesting that quercetin may exert antitumor effects by modulating SERPINE1-linked immune cells (Figure 3D). Patients were divided into high- and low-SERPINE1 expression groups, and the results showed that macrophage infiltration was significantly increased in the high-expression group (p < 0.05) (Figure 4A,B). Further analysis revealed that SERPINE1 expression was significantly positively correlated with the M2 macrophage markers CD163 and MRC1 (Figure 4C–E). These findings suggest that quercetin may inhibit the progression of gastric cancer (GC) by targeting SERPINE1 to influence macrophage polarization.

3.4. Quercetin Inhibits the Proliferation of GC Cells

To evaluate the inhibitory effect of quercetin on GC cells, we first assessed cell viability using CCK-8 assays on the AGS and MGC803 cell lines. The results demonstrated that quercetin dose-dependently inhibited cell proliferation, with IC50 values of 12.16 μM (AGS) and 8.548 μM (MGC803), respectively (Figure 5A). Colony formation assays further confirmed the antitumor efficacy of quercetin, showing a significant reduction in colony numbers with increasing drug concentration (p < 0.05) (Figure 5B,C). EdU incorporation assays revealed that quercetin treatment markedly decreased the proliferation rate compared with that for the control group (p < 0.05) (Figure 5D). WB analysis indicated that quercetin significantly downregulated the expression of the proliferation-related proteins p-AKT and p-MAPK (p < 0.05) (Figure 5E and Figure S1A). These findings collectively demonstrate that quercetin effectively inhibits GC cell proliferation by suppressing the AKT/MAPK signaling pathway.

3.5. Quercetin Inhibits the Growth of GC In Vivo

To evaluate the antitumor efficacy of quercetin in vivo, we established a subcutaneous xenograft model using AGS cells in mice. The results demonstrated that quercetin treatment (50 mg/kg) significantly inhibited tumor growth as compared with the control group: after 21 days of treatment, the tumor volume was reduced by 78.4% (p < 0.05), and the final tumor weight decreased by 78.4% (p < 0.05) (Figure 5F,G,I). Notably, the body weights in both groups remained stable during the treatment period, indicating the absence of significant systemic toxicity from quercetin at its effective dose (Figure 5H). These findings confirm the potent anti-GC activity of quercetin in vivo.

3.6. Quercetin Inhibits the Migration of GC Cells

To further investigate the effect of quercetin on GC cell migration, we performed Transwell assays using AGS and MGC803 cells. The results demonstrated that quercetin significantly inhibited cell migration in a dose-dependent manner (p < 0.05), with high-dose treatments reducing migration rates to 22.2% (AGS) and 10.4% (MGC803) of the control levels (Figure 6A). WB analysis revealed significant alterations in EMT markers: the expression of pro-migratory proteins Vimentin and N-cadherin decreased, while expression of the epithelial marker E-cadherin increased (Figure 6B and Figure S1B). These findings confirm that quercetin inhibits GC cell migration by modulating the EMT process.

3.7. Quercetin Promotes Apoptosis in GC Cells

To investigate the effect of quercetin on GC cell apoptosis, this study employed Annexin V/PI double staining to assess apoptosis in AGS and MGC803 cells treated with quercetin. The results demonstrated that quercetin significantly induced apoptosis (p < 0.05) in a dose-dependent manner (Figure 6C and Figure S1C). WB analysis revealed significant alterations in apoptosis-related protein expression: the pro-apoptotic proteins p21, p27, p-p53, and Bax were significantly upregulated, while the anti-apoptotic proteins Bcl-2 were significantly downregulated (Figure 6D). These findings suggest that quercetin promotes apoptosis in GC cells.

4. Discussion

GC, ranking as the fifth most common malignancy globally, with over 1 million new cases annually, exhibits high mortality rates among cancer-related deaths. Although surgery combined with chemoradiotherapy improves prognosis in locally advanced patients, the five-year survival rate for advanced GC remains below 30%, with chemoresistance and tumor heterogeneity leading to recurrence or metastasis in approximately 60% of patients [23]. This clinical challenge underscores the urgent need for novel therapeutic strategies. Our study systematically elucidated, for the first time, the molecular mechanism by which Epimedium’s active component quercetin inhibits GC progression via targeted regulation of SERPINE1, by integrating network pharmacology with experimental validation to provide new insights into natural product-based tumor interventions.
Among the 137 Epimedium–GC cross-targets identified through network pharmacology, SERPINE1 was identified as a core regulatory target. TCGA data analysis revealed significant SERPINE1 overexpression in GC tissues (p < 0.05), with high-expression patients exhibiting a reduction in overall survival (HR = 1.93, 95% CI: 1.38–2.71), consistent with its pro-metastatic role in pan-cancer studies [24]. SERPINE1 overexpression, observed in 78% of TCGA-STAD samples, correlates with advanced TNM stages and vascular invasion, likely through its dual role in extracellular matrix remodeling and TGF-β-mediated immune suppression [25]. Molecular docking demonstrated had a good binding affinity between quercetin and SERPINE1 (ΔG = −6.20 kcal/mol), superior to that reported for natural compounds such as baicalein (ΔG = −5.80 kcal/mol) [26]. This binding affinity surpasses that of synthetic inhibitors such as tiplaxtinin, suggesting quercetin’s potential as a lead compound for SERPINE1-targeted therapy [27].
In vitro experiments confirmed that quercetin dose-dependently inhibited GC cell proliferation (IC50 = 12.16 μM in AGS cells, 8.55 μM in MGC803 cells) and reduced colony formation (p < 0.05). Transwell assays further showed that high-dose quercetin decreased cell migration rates to 22.2% (AGS) and 10.4% (MGC803) of control levels. WB analysis revealed apoptosis induction via Bcl-2 downregulation and Bax/p21 upregulation, a mechanism analogous to that of curcumin’s pro-apoptotic effects [28]. While our study focused on quercetin, Epimedium’s multi-component synergy—for instance, icariin’s anti-angiogenic effects combined with quercetin’s pro-apoptotic activity—warrants investigation as a combinatorial strategy to overcome monotherapy’s limitations [29]. In animal studies, quercetin reduced the subcutaneous xenograft tumor volume by 78.4% without significant weight loss, aligning with quercetin’s recent toxicological profiles [30]. Notably, quercetin treatment significantly suppressed AKT/MAPK pathway activation and reversed EMT, a dual mechanism complementary to HIF-1α pathway modulation by other Epimedium components [31]. The inhibitory effects of quercetin on AKT/MAPK signaling align with previous findings that demonstrate its potential to synergistically enhance docetaxel’s anticancer efficacy through modulation of the AKT/MAPK signaling pathway in breast cancer cell lines [32].
Immune infiltration analysis indicated a strong correlation between SERPINE1 overexpression and M2 macrophage polarization, suggesting its role in fostering an immunosuppressive TME. The SERPINE1–macrophage axis uncovered here adds mechanistic depth to prior observations of Epimedium extracts polarizing macrophages toward antitumor M1 phenotypes [33]. This finding complements the limited clinical efficacy of programmed cell death protein 1 (PD-1) inhibitors (20–30% response rates in GC), as quercetin-mediated SERPINE1 inhibition may reverse immune evasion [34,35]. Although this study did not dissect specific immune cell subsets, prior evidence suggests that SERPINE1 modulates macrophage polarization via TGF-β signaling, implying quercetin’s potential to remodel the immunosuppressive TME [36,37,38].
This study has several limitations that warrant attention. The influence of SERPINE1 post-translational modifications on quercetin sensitivity remains uncharacterized, and the use of an immunocompromised xenograft model precluded any assessment of quercetin’s immunomodulatory effects observed in a syngeneic model. Future work should employ patient-derived organoids to model GC’s heterogeneity and evaluate Epimedium extracts (rather than isolated compounds) to better reflect clinical TCM practice. Additionally, quercetin’s limited bioavailability requires optimization via advanced nano-delivery systems [39]. The plan also includes conducting corresponding randomization, blinding, and toxicity assessment experiments. Future investigations should integrate single-cell RNA sequencing to dissect SERPINE1-specific immune cell subpopulations and explore combinatorial therapeutic strategies with PD-1 inhibitors to enhance clinical translation.

5. Conclusions

This study revealed the multifaceted antitumor mechanisms of Epimedium-derived quercetin via SERPINE1 targeting, demonstrating the dual effects of tumor proliferation suppression and immune microenvironment regulation. These findings not only advance the scientific basis for modernizing traditional Chinese medicine but also offer a novel strategy to overcome GC treatment resistance.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers18040603/s1, The raw bands from the Western blot are shown in the supplementary materials, and the quantitative analysis results are shown in Supplementary Figure S1.

Author Contributions

Conceptualization, Data curation, Formal analysis: G.Y., J.C. and X.Y. (Xiangdi Yang); Funding acquisition: C.L., X.Y. (Xiangdi Yang) and J.Z.; Investigation and Methodology: G.Y., J.C., X.Y. (Xiangdi Yang), L.L., C.L. and X.Y. (Xihua Yang); Project administration, Resources, Software, Supervision, Validation and Visualization: J.T., L.L., C.L., X.Y. (Xihua Yang) and J.Z.; Writing—original draft and Writing—review and editing: All authors; All authors have read and agreed to the published version of the manuscript.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Natural Science Foundation of Hunan Province (Grant No. 2025JJ70576, 2025JJ70537 and 2026JJ80578). This research was supported by the annual School-Level Scientific Research Project of Xiangnan University (No. 2020XJ132, 2025XJ87 and 2025XJ94). This research is supported by the Scientific Research Project of Guangdong Provincial Bureau of Traditional Chinese Medicine (20231419).

Institutional Review Board Statement

The animal study was conducted in accordance with standard ethical guidelines and under the supervision of the Animal Welfare Ethics Committee of Jinan University (approval number: 20250428-06, approval date: 28 April 2025). The study complied with local legislation and institutional requirements.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

AKT, Protein Kinase B; Bax, BCL2-Associated X;Bcl-2, B-cell lymphoma-2; CNVs, Copy Number Variations; DL, Drug-Likeness; FDA, Food and Drug Administration; GC, Gastric cancer; GO, Gene Ontology terms; GSEA, Gene Set Enrichment Analysis;HER2, Human Epidermal Growth Factor Receptor 2; HGNC, Human Genome Organization Gene Nomenclature Committee; KEGG, Kyoto Encyclopedia of Genes and Genomes; MAPK, Mitogen-Activated Protein Kinase; OB, Oral Bioavailability; PD-1, Programmed cell Death protein 1.pLDDT, predicted Local Distance Difference Test; PPI, Protein–Protein Interaction Networks; ROC, Receiver Operating Characteristic; SERPINE1, Serpin family E member 1;SNPs, Single Nucleotide Polymorphisms; SPF, Specific Pathogen-Free; TCGA, The Cancer Genome Atlas; TCM, Traditional Chinese Medicine; TCMSP, Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform; TGF-β, Transforming growth factor beta; TME, Tumor Microenvironment; TP53, Tumor Protein P53; WB, Western blot.

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Cancers 18 00603 g001
Figure 1. Study on the anti-GC effects of epimedium and screening of hub genes. (A) Epimedium active components-targets-disease network. (B) Intersection targets of drugs and diseases. (C,D) Functional enrichment analysis of intersection targets. (E,F) Screening of hub genes.
Figure 1. Study on the anti-GC effects of epimedium and screening of hub genes. (A) Epimedium active components-targets-disease network. (B) Intersection targets of drugs and diseases. (C,D) Functional enrichment analysis of intersection targets. (E,F) Screening of hub genes.
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Figure 2. Identification of core gene SERPINE1, molecular docking, and its expression and clinical significance in GC. (A) Survival analysis of hub genes (TP53). (B) Survival analysis of hub genes (SERPINE1). (C) Molecular docking (TP53). (D) Molecular docking (SERPINE1). (E) High expression of SERPINE1 in GC tissues. Violin shapes show the distribution density of SERPINE1 expression, with dots marking the median values for each tissue. (F) ROC curve analysis of SERPINE1 for GC diagnosis. The blue solid line represents the ROC curve of the proposed model, demonstrating its classification performance; the diagonal dotted line indicates the random guess benchmark (AUC = 0.5). ***, p < 0.001.
Figure 2. Identification of core gene SERPINE1, molecular docking, and its expression and clinical significance in GC. (A) Survival analysis of hub genes (TP53). (B) Survival analysis of hub genes (SERPINE1). (C) Molecular docking (TP53). (D) Molecular docking (SERPINE1). (E) High expression of SERPINE1 in GC tissues. Violin shapes show the distribution density of SERPINE1 expression, with dots marking the median values for each tissue. (F) ROC curve analysis of SERPINE1 for GC diagnosis. The blue solid line represents the ROC curve of the proposed model, demonstrating its classification performance; the diagonal dotted line indicates the random guess benchmark (AUC = 0.5). ***, p < 0.001.
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Figure 3. Relationship between SERPINE1 expression and immune infiltration in GC tissues. (A,B) Analysis of immune infiltration level distribution and immune cell correlation. (C) Characterization of immune cell infiltration in GC tissues. (D) Differences in immune cell infiltration between high and low SERPINE1 expression groups (Stacked Bar Plot). (E) Correlation analysis between SERPINE1 expression levels and immune cell infiltration in GC (Lollipop Plot). ns, p > 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Figure 3. Relationship between SERPINE1 expression and immune infiltration in GC tissues. (A,B) Analysis of immune infiltration level distribution and immune cell correlation. (C) Characterization of immune cell infiltration in GC tissues. (D) Differences in immune cell infiltration between high and low SERPINE1 expression groups (Stacked Bar Plot). (E) Correlation analysis between SERPINE1 expression levels and immune cell infiltration in GC (Lollipop Plot). ns, p > 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001.
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Figure 4. Relationship between SERPINE1 expression and macrophage infiltration in GC tissues. (A) Correlation analysis between SERPINE1 and macrophage infiltration in GC. (B) Relationship between SERPINE1 expression and macrophage infiltration in GC. (C) Heatmap of co-expression between SERPINE1 and M2 macrophage markers. (D) Co-expression analysis of SERPINE1 and CD163. (E) Co-expression analysis of SERPINE1 and MRC1. ***, p < 0.001.
Figure 4. Relationship between SERPINE1 expression and macrophage infiltration in GC tissues. (A) Correlation analysis between SERPINE1 and macrophage infiltration in GC. (B) Relationship between SERPINE1 expression and macrophage infiltration in GC. (C) Heatmap of co-expression between SERPINE1 and M2 macrophage markers. (D) Co-expression analysis of SERPINE1 and CD163. (E) Co-expression analysis of SERPINE1 and MRC1. ***, p < 0.001.
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Figure 5. Quercetin inhibits the proliferation of GC. (A) Quercetin inhibits the viability of GC Cells (IC50 = 12.16 μM for AGS, IC50 = 8.54 μM for MGC803). (B,C) Inhibition of GC cell colony formation by Quercetin. (D) EdU assay confirms SERPINE1 inhibits GC cell proliferation. Scale bar: 400 μm. (E) Quercetin inhibits the expression of proliferation-related pathway proteins in GC cells. (F) Quercetin inhibits GC in vivo. (G) Growth curve of Quercetin inhibiting GC in vivo. (H) Quercetin does not affect mouse body weight. (I) Tumor weight demonstrating Quercetin inhibits GC in vivo. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Figure 5. Quercetin inhibits the proliferation of GC. (A) Quercetin inhibits the viability of GC Cells (IC50 = 12.16 μM for AGS, IC50 = 8.54 μM for MGC803). (B,C) Inhibition of GC cell colony formation by Quercetin. (D) EdU assay confirms SERPINE1 inhibits GC cell proliferation. Scale bar: 400 μm. (E) Quercetin inhibits the expression of proliferation-related pathway proteins in GC cells. (F) Quercetin inhibits GC in vivo. (G) Growth curve of Quercetin inhibiting GC in vivo. (H) Quercetin does not affect mouse body weight. (I) Tumor weight demonstrating Quercetin inhibits GC in vivo. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
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Figure 6. Quercetin inhibits migration and promotes apoptosis of GC cells. (A) Quercetin inhibits migration of GC cells. Scale bar: 400 μm. (B) Quercetin inhibits the expression of migration-related proteins in GC cells. (C) Quercetin promotes apoptosis of GC cells. (D) Detection of apoptosis-related protein expression by WB. **, p < 0.01; ***, p < 0.001.
Figure 6. Quercetin inhibits migration and promotes apoptosis of GC cells. (A) Quercetin inhibits migration of GC cells. Scale bar: 400 μm. (B) Quercetin inhibits the expression of migration-related proteins in GC cells. (C) Quercetin promotes apoptosis of GC cells. (D) Detection of apoptosis-related protein expression by WB. **, p < 0.01; ***, p < 0.001.
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Table 1. Key active components of epimedium.
Table 1. Key active components of epimedium.
Mol IDMolecule NameOB (%)DL
MOL000006luteolin36.160.25
MOL000098quercetin46.430.28
MOL000359sitosterol36.910.75
MOL000422kaempferol41.880.24
MOL000622Magnograndiolide63.710.19
MOL00151024-epicampesterol37.580.71
MOL001645Linoleyl acetate42.100.20
MOL001771poriferast-5-en-3beta-ol36.910.75
MOL001792DFV32.760.18
MOL003044Chryseriol35.850.27
MOL0035428-Isopentenyl-kaempferol38.040.39
MOL004367olivil62.230.41
MOL004373Anhydroicaritin45.410.44
MOL004380C-Homoerythrinan, 1,6-didehydro-3,15,16-trimethoxy-, (3.beta.)-39.140.49
MOL004382Yinyanghuo A56.960.77
MOL004384Yinyanghuo C45.670.50
MOL004386Yinyanghuo E51.630.55
MOL0043886-hydroxy-11,12-dimethoxy-2,2-dimethyl-1,8-dioxo-2,3,4,8-tetrahydro-1H-isochromeno [3,4-h]isoquinolin-2-ium60.640.66
MOL0043918-(3-methylbut-2-enyl)-2-phenyl-chromone48.540.25
MOL004394Anhydroicaritin-3-O-alpha-L-rhamnoside41.580.61
MOL0043961,2-bis(4-hydroxy-3-methoxyphenyl)propan-1,3-diol52.310.22
MOL004425Icariin41.580.61
MOL004427Icariside A731.910.86
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Yang, G.; Chen, J.; Yang, X.; Tan, J.; Liu, C.; Li, L.; Yang, X.; Zhao, J. Quercetin Inhibits the Progression of Gastric Cancer Through the AKT/MAPK Signaling Pathway. Cancers 2026, 18, 603. https://doi.org/10.3390/cancers18040603

AMA Style

Yang G, Chen J, Yang X, Tan J, Liu C, Li L, Yang X, Zhao J. Quercetin Inhibits the Progression of Gastric Cancer Through the AKT/MAPK Signaling Pathway. Cancers. 2026; 18(4):603. https://doi.org/10.3390/cancers18040603

Chicago/Turabian Style

Yang, Guorong, Juwu Chen, Xiangdi Yang, Jifeng Tan, Chengbin Liu, Lingyu Li, Xihua Yang, and Jianfu Zhao. 2026. "Quercetin Inhibits the Progression of Gastric Cancer Through the AKT/MAPK Signaling Pathway" Cancers 18, no. 4: 603. https://doi.org/10.3390/cancers18040603

APA Style

Yang, G., Chen, J., Yang, X., Tan, J., Liu, C., Li, L., Yang, X., & Zhao, J. (2026). Quercetin Inhibits the Progression of Gastric Cancer Through the AKT/MAPK Signaling Pathway. Cancers, 18(4), 603. https://doi.org/10.3390/cancers18040603

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