EGCG promotes apoptosis in BT-549 triple-negative breast cancer cells by targeting STAT3

Abstract

Background

Triple-negative breast cancer (TNBC), defined by the lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression, is an aggressive subtype of breast cancer (BC) with limited therapeutic options. Signal transducer and activator of transcription 3 (STAT3) plays a critical oncogenic role in TNBC by promoting tumor progression and conferring resistance to apoptosis. (-)-epigallocatechin-3-gallate (EGCG), a bioactive green tea polyphenol, has been reported to inhibit STAT3 signaling and exert anti-cancer effects. However, its mechanistic effect on STAT3-mediated pathways in TNBC has yet to be fully elucidated. This study aims to investigate the effects of EGCG on TNBC cell proliferation, migration, and apoptosis, and to elucidate the underlying mechanisms involving STAT3-associated signaling and hydrogen sulfide-related pathways.

Methods

BT-549 TNBC cells were treated with EGCG to assess its effects on cell viability, migration, and apoptosis. STAT3 expression was assessed at both the transcript and protein levels. Apoptotic regulatory proteins, including Bax, Bcl-2, caspase-3, and caspase-8, were quantified to examine pathway involvement. Molecular docking and molecular dynamics simulations were conducted to evaluate the interaction between EGCG and STAT3, and to explore the potential inhibition of the JAK/STAT3/ERK signaling cascade.

Results

EGCG significantly reduced the viability, impaired the migratory capacity, and significantly enhanced the apoptosis of BT-549 cells. The treatment significantly downregulated STAT3 at both the messenger RNA (mRNA) and protein levels. EGCG further modulated apoptotic regulators by increasing the Bax/Bcl-2 ratio and promoting the activation of caspase-3 and caspase-8. Computational analyses revealed a stable binding interaction between EGCG and STAT3, which supporting the inhibitory effect on the JAK/STAT3/ERK signaling axis.

Conclusions

EGCG effectively suppresses STAT3-driven oncogenic signaling in TNBC by inhibiting cell proliferation, reducing migration, and inducing apoptosis through the modulation of key apoptotic pathways. Thus, EGCG could be a promising therapeutic candidate for targeting STAT3-mediated mechanisms in TNBC. Further research should be conducted to examine the clinical application of EGCG.

Highlight box.

Key findings

• (-)-epigallocatechin-3-gallate (EGCG) significantly inhibits BT-549 triple-negative breast cancer (TNBC) cell viability and migration, and induces apoptosis.

• EGCG downregulates signal transducer and activator of transcription 3 (STAT3) expression at both the messenger RNA (mRNA) and protein levels.

• EGCG modulates apoptotic regulators, including Bax, Bcl-2, caspase-3, and caspase-8, promoting apoptosis.

What is known, and what is new?

• Previous research has shown that STAT3 plays a key oncogenic role in TNBC, driving tumor progression and resistance to apoptosis.

• This study provides novel insights into the mechanism by which EGCG inhibits STAT3 signaling, promoting apoptosis and suppressing TNBC progression.

What is the implication, and what should change now?

• EGCG represents a promising therapeutic agent for targeting STAT3-driven pathways in TNBC.

• Further clinical studies need to be conducted to explore the potential use of EGCG in TNBC therapy and its effect on STAT3-mediated mechanisms.

Introduction

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer (BC), affecting younger women aged 20–49 years and women from racial minorities (1). The name “triple-negative” refers to the absence of the three most common biomarkers for BC: progesterone receptor (PR), estrogen receptor (ER), and human epidermal growth factor receptor 2 (HER2) (2-4). TNBC was so named because it tests negative for the three most common biomarkers used in BC classification (5,6). Due to a lack of targets and inter-tumor heterogeneity, TNBC is aggressive and difficult to treat (7,8). A major factor in BC mortality is metastasis, which is driven by genetic and phenotypic alterations in epithelial-mesenchymal transition, stemness, survival, migration, and invasion (9,10).

TNBC is a subtype of BC with a poor prognosis and a lack of approved targeted therapies (11). Epidermal growth factor receptor (EGFR) is overexpressed in more than 50% of TNBC patients and is thought to be a driver of TNBC progression (12); however, antibody-based therapies that block EGFR dimerization and activation have shown little clinical benefit in the treatment of TNBC (13). EGFR monomers have been shown to activate signal transducer and activator of transcription 3 (STAT3), and a strong negative correlation between ZFAS1 and STAT3 gene expression has been observed (14). Many drugs targeting STAT3 have also been shown to inhibit the development of TNBC (15-18). STAT3 also plays a key role in the resistance of various drugs to epithelial-to-mesenchymal transition (19). However, STAT3 is also a key target of apoptosis (20). Thus, STAT3 inhibitors, or negative drugs that increase BC cell apoptosis, urgently need to be identified and developed to aid in the treatment of TNBC (21).

(-)-epigallocatechin-3-gallate (EGCG) (Figure 1) is a natural phenolic compound found in foods and beverages, especially tea, with a wide range of biological activities, including anti-oxidant, anti-microbial, anti-obesity, anti-inflammatory, and anti-cancer properties (22,23). Its potential in cardiovascular and brain health has attracted much attention (24). The anti-cancer potential of EGCG is unquestioned, and the induction of EGCG drug analogs has been shown to inhibit STAT3-mediated JAK gene expression and to inhibit the development of liver cancer (25,26). The direct use of EGCG has also been shown to inhibit colorectal cancer cell proliferation and migration, Further, research has shown that EGCG enhances the therapeutic potential of gemcitabine and CP690550 by inhibiting the STAT3 signaling pathway in human pancreatic cancer (27,28).

Figure 1.

The structural formula of EGCG. EGCG, (-)-epigallocatechin-3-gallate.

In this study, we evaluated the anti-TNBC potential of EGCG and assessed its effect on STAT3 to examine the relationship between EGCG and STAT3. We also conducted a preliminary study on the mechanism of EGCG-induced apoptosis, providing evidence to support its clinical use. We present this article in accordance with the MDAR reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2883/rc).

Methods

mRNA extraction and qPCR

The BT-549 cells in this experiment were purchased from Procell Company, with the item number CL-0041.

Total RNA was extracted from the cell samples using the FastPure Complex Tissue/Cell Total RNA Isolation Kit (Vazyme, Wuhan, China; RC113-01), and genomic DNA contamination was eliminated using the Vazyme HiScript IV All-in-One Ultra RT SuperMix for quantitative polymerase chain reaction (qPCR) Kit (Vazyme, R433-01). RNA concentration and purity were assessed spectrophotometrically. A total of 1 µg RNA was reverse transcribed into complementary DNA (cDNA) in accordance with the manufacturer’s instructions. The reaction mixture contained reverse transcriptase, reverse transcription buffer, deoxy-ribonucleoside triphosphate (dNTPs), and primers. Reverse transcription was performed at 25 ℃ for 10 min, 42 ℃ for 50 min, followed by enzyme inactivation at 85 ℃ for 5 min, and rapid cooling to 4 ℃. qPCR was conducted using Vazyme AceQ Universal SYBR qPCR Master Mix (Vazyme, Q511-02) in a sterile environment. Each 20 µL reaction contained 10 µL of qPCR Master Mix, 1 µL of specific primer pair, 2 µL of cDNA (typically diluted 1:10), and RNase-free water. Reactions were performed in triplicate. The qPCR cycling conditions included an initial denaturation at 95 ℃ for 2 min, followed by 40 cycles of 95 ℃ for 15 s and 60 ℃ for 30 s. A melting curve analysis was conducted to confirm specificity. Relative gene expression levels were calculated using the cycle threshold (Ct) method and normalized to an internal reference gene. All reagents and consumables were RNase-free.

Cell protein extraction and Western blotting

Protein extraction from cultured cells was performed by washing the cells twice with cold PBS buffer, followed by cell scraping and centrifugation at 500 g for 5 min to collect the cell pellet. The pellet was re-suspended in pre-cooled Radioimmunoprecipitation Assay buffer (RIPA) buffer (Thermo Fisher Scientific, Massachusetts, USA; Catalog No. 89900), supplemented with protease inhibitors (Roche, Basel, Switzerland; Catalog No. 11836170001) and phosphatase inhibitors (Sigma-Aldrich, Missouri, USA; Catalog No. P5726). The cells were lysed on ice for 30 min with intermittent mixing every 10 min. The lysate was centrifuged at 12,000 g for 15 min at 4 ℃, and the supernatant was collected as the total protein extract.

Protein concentration was determined using the bicinchoninic acid (BCA) protein quantification kit (Thermo Fisher Scientific, Catalog No. 23227). Equal amounts of protein (20–30 µg per well) were loaded onto a SDS-PAGE gel (SDS-polyacrylamide gel electrophoresis gel) (Bio-Rad, California, USA; Cat. No. 1610173) and electrophoresed at 100–120V until the dye front reached the bottom of the separation gel. The proteins were then transferred onto PVDF (PVDF membrane) membranes (Millipore, Massachusetts, USA; Cat. No. IPVH00010) at 100V for 1–2 h at 4 ℃. The membranes were blocked with 5% skim milk (Bio-Rad, Cat. No. 1706404) at room temperature for 1 h to prevent non-specific binding, and then incubated with primary antibodies at an appropriate dilution overnight at 4 ℃. After washing, the membranes were incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies at room temperature for 1 h, and then washed again.

Protein bands were visualized using an enhanced chemiluminescence detection reagent (Thermo Fisher Scientific, Cat. No. 32106) and incubated for 1–2 min at room temperature. The signals were captured using a chemiluminescence imaging system. All the primary antibodies were purchased from Abcam. The apoptotic inhibitor Necrostatin 1 (MCE, New Jersey, USA; HY-121954) and apoptotic agonist Bafetinib (MCE, HY-50868) were purchased from MCE.

Antibody information: STAT3 (ABclonal, Wuhan, China; A19566), IRF9 (CST, Massachusetts, USA; 76684), caspase-3 (CST, 9662), cleaved caspase-3 (CST,9661), caspase-8 (CST, 4790), cleaved caspase-8 (CST, 9496), ERK (CST, 4695), p-ERK (CST, 5726), JAK (CST, 3332), p-JAK (CST,3331), GAPDH (CST, 2118).

Flow cytometry for detecting apoptotic cells

Flow cytometry was used to analyze apoptosis using an Annexin V-FITC/PI (Annexin V-FITC/PI) apoptosis detection kit (Keygen, KGA1101). The cells were seeded in 6-well plates at a density of 1×10⁶ cells per well and incubated overnight at 37 ℃ with 5% carbon dioxide (CO₂) before exposure to the experimental conditions. After treatment, the cells were washed twice with cold PBS, detached using trypsin-ethylenediaminetetraacetic acid (EDTA), and collected by centrifugation at 1,000 rpm for 5 min at 4 ℃. The pellet was re-suspended in 100 µL of 1× binding buffer, followed by the addition of 5 µL of Annexin V-FITC and 5 µL of propidium iodide (PI) solution. The mixture was incubated in the dark at room temperature for 15 min before 400 µL of 1× binding buffer was added. The samples were analyzed using a flow cytometer equipped with a 488-nm laser, and at least 10,000 events per sample were collected. Apoptosis was quantified based on the percentage of Annexin V-positive/PI-negative cells (early apoptosis) and Annexin V-positive/PI-positive cells (late apoptosis/necrosis) using FlowJo software.

Transwell migration/invasion assay

Cells were seeded into the upper chambers of Transwell inserts in serum-free medium, while medium containing 10% fetal bovine serum (FBS) was added to the lower chambers as a chemoattractant. After incubation, non-migrated cells on the upper surface were removed with a cotton swab. Cells that migrated to the lower surface were fixed and stained with 0.1% crystal violet, and images were captured under a microscope.

Cell scratch assays

The experimental groups included a blank control group, low-drug group (50 µM), medium-drug group (100 µM), and high-drug group (200 µM). A marker was used to draw three horizontal lines on the bottom of the 6-well plate, with each line spaced 0.5–1 cm apart. Next, 6×10⁵ cells were added to each well and cultured for 24 h. A 100-µL pipette tip was used to create vertical cell scratches perpendicular to the horizontal lines at the bottom of the wells. The cells were washed three times with PBS to remove any detached cells. Roswell Park Memorial Institute-1640 (RPMI-1640) was used in the control group, while 2 mL of different concentrations of EGCG (50, 100, or 200 µM) were added to the wells of the different experimental groups. The cells were incubated at 37 ℃ with 5% CO₂, and the scratch areas were photographed under a microscope at 4× magnification at 0 and 48 h.

Cell cloning

The experimental groups included a blank control group, low-drug group (50 µM), medium-drug group (100 µM), and high-drug group (200 µM). For each experimental group, 400 cells were seeded per well in a 6-well culture plate, after which, 2 mL of room temperature pre-warmed complete medium (containing 10% FBS) was added. After cell attachment, the medium was removed, and 2 mL of different concentrations of EGCG (50, 100, or 200 µM) were added to the wells, with the blank control group receiving an equal amount of RPMI-1640. The cells were incubated in a cell culture incubator for 144 h to terminate the culture, and the colony formation areas were then photographed.

Cell Counting Kit 8 (CCK-8) assays

The cells were seeded into 96-well plates at 10,000 cells per well; the volume of each well was generally 100 µL. The cells were incubated in a 37 ℃, 5% CO₂ incubator for 24 h to allow the cells to adhere to the wall. According to the experimental design, each well was treated with 10, 20, or 40 µM EGCG. A blank control group (containing culture medium only), a negative control group (PBS), and a positive control group (Triton X-100) were also included. Cells were then cultured in a 37 ℃, 5% CO₂ incubator for 48 h. After the treatment, 10 µL of CCK-8 solution was added to each well and mixed gently. The 96-well plate was gently shaken to evenly distribute the CCK-8 reagent. The 96-well plate was returned to a 37 ℃, 5% CO₂ incubator, and the cells were incubated for 2 h.

After incubation, the absorbance [optical density (OD) value] of each well was measured on an enzyme-linked immunosorbent assay (ELISA) reader at a wavelength of 450 nm. The OD value of the treated group was compared with that of the control group to calculate the cell viability or proliferation rate. The relative cell viability was calculated using the following formula:

Cell viability = (treatment group OD value – blank control group OD value)/(negative control group OD value – blank control group OD value) × 100%.

Network pharmacology analysis

The chemical components and targets were collected. The PubChem database (https://pubchem.ncbi.nlm.nih.gov) was used to obtain the effective components and their Simplified Molecular Input Line Entry System (SMILES), and SwissTargetPrediction and SEA were used to predict their effective targets.

The prediction of disease targets was performed by entering keywords in the GeneCards (https://www.genecards.org/) and OMIM (https://www.omim.org/) databases for retrieval, integrating all targets in excel, removing duplicate genes, and merging them to obtain the disease targets of this study. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

The intersection targets were obtained using Venn (https://bioinfogp.cnb.csic.es/tools/venny/) software.

For the component-target network analysis, Cytoscape3.9.2 software was used to import relevant files and perform the network topology analysis.

For the protein-protein interaction (PPI) network construction and network topology analysis, the drug-intersection genes were imported using the String (https://string-db.org/) platform, with the object set to homo sapiens and the minimum interaction score set to 0.9.

For the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, the bioinformatics open source software Bioconductor (http://www.bioconductor.org/) was used. The GO analysis was divided into three categories to annotate the roles of the drug treatment targets in gene function: biological process (BP), cellular component (CC), and molecular function (MF).

Dynamics simulation and docking

Molecular dynamics (MD) simulations were carried out with the GPU-enabled GROMACS 2022 package using the CHARMM36 force field. The STAT3–EGCG complex was solvated in a cubic periodic box (80 Å per side) and neutralized by adding Na⁺ and Cl^- ions. Prior to production runs, the system was relaxed by steepest-descent energy minimization until the maximum force fell below 10 kJ·mol^-1·nm^-1. The system was subsequently equilibrated under NVT and NPT conditions for 5 ns to maintain the temperature at 310 K and the pressure at 1 bar. A 100 ns MD production simulation was then performed.

Trajectory analyses included the root mean square deviation (RMSD) of the ligand and the complex, as well as the root mean square fluctuation (RMSF) of protein residues. The radius of gyration (Rg) was also computed and, together with RMSD, used as descriptors for principal component analysis (PCA). The free-energy surface along the principal components was estimated and visualized using a random hill-climbing approach to characterize the conformational landscape (including stable and transition states) of the STAT3–EGCG complex and to identify representative stable conformations.

Replicates

All cell-based experiments were independently performed at least three times using biological replicates (n=3) unless otherwise stated. For assays involving multi-well measurements, including qPCR and CCK-8 assays, technical replicates were included within each biological replicate.

Statistical analysis

The data are presented as the mean ± standard deviation. The Student’s t-test was used for comparisons between two groups. One-way analysis of variance followed by Tukey’s post-hoc test was used for comparisons among multiple groups. A P value of less than 0.05 was considered statistically significant.

Results

Network pharmacology analysis confirms the association between EGCG and TNBC

The Venn diagram analysis revealed 82 common targets between EGCG and TNBC, including several important inflammation, fibrosis, and cancer targets (Figure 2A). After importing the common targets of the intersection of drug targets and TNBC targets into the database, the relevant PPI network metadata were downloaded to obtain the PPI diagram of the Intersection target, and the top 10 targets were obtained from these PPI analysis results (Figure 2B,2C). After excluding the internal reference proteins, the proteins with higher PPI frequencies included STAT3, Raf-1 proto-oncogene, serine/threonine kinase (RAF1), and programmed cell death 1 (PD1) (Figure 2B-2E). Most of these proteins are related to cancer and fibrosis. Thus, the results suggest that EGCG exerts anti-inflammatory, antioxidant, and anti-fibrotic effects, and promotes cancer cell apoptosis.

Figure 2.

Network pharmacology analysis of EGCG and TNBC. (A) Venn diagram of disease targets and drug targets; (B) PPI network; (C,D) select the top ten proteins from the PPI network for further protein-protein interaction analysis; (E) drug-target-disease interaction network; (F) GO enrichment analysis; (G) KEGG enrichment analysis. BP, biological process; CC, cellular component; EGCG, (-)-epigallocatechin-3-gallate; GO, Gene Ontology; MF, molecular function; PPI, protein-protein interaction; TNBC, triple-negative breast cancer.

The GO functional enrichment analysis of the cross-linked genes between EGCG and TNBC showed that the target genes were mainly enriched in the top 10 terms of each category—BPs, CCs, and MFs—which were ranked from highest to lowest based on the number of associated genes (Figure 2F). In terms of the BPs, EGCG is mainly involved in enzymatic glycosylation, nucleoside metabolism regulation, purine nucleoside monophosphate biosynthesis, cell metabolism compounds, cell secretion regulation, etc.; in terms of the CCs, EGCG is mainly involved in vesicle cavity, membrane raft, membrane microdomain, membrane region and other processes; in terms of the MFs, EGCG is mainly involved in metallopeptidase activity, protein tyrosine kinase activity, oligosaccharide binding, endopeptidase activity, carbohydrate derivative transmembrane transporter activity, etc.

A KEGG pathway enrichment analysis was performed on the cross-linked genes, and the top 13 signal pathways were identified (Figure 2G), including cancer development, the EGFR signaling pathway, the VEGF (Vascular Endothelial Growth Factor) signaling pathway, the HIF-1 (Hypoxia-Inducible Factor-1) signaling pathway, and epithelial-to-mesenchymal transition. These findings indicate that STAT3 is a potential therapeutic target for TNBC, and EGCG is a potential therapeutic drug for TNBC, is related to multiple important signaling pathways such as cancer, fibrosis, and inflammation, and has important pharmacological analysis significance.

Molecular docking and MD simulations elucidated the interaction between EGCG and STAT3

STAT3 and EGCG also formed four hydrogen bonds during the simulation, with a binding free energy of –9.5 kcal/mol (Figure 3A). The molecule as a whole binds to the hydrophobic pocket formed by the protein residues IRP187, ARG197, CAL185, and HIS139 through hydrophobic interactions. Notably, TYR170 forms hydrogen bonds with carbonyl oxygen to form π-π bond interactions, contributing to the stable binding of compounds to proteins (Figure 3B).

Figure 3.

Molecular docking and molecular dynamics simulations elucidated the interaction between EGCG and STAT3. (A,B) Molecular docking simulation of EGCG with Stat3; (C-G) molecular dynamics simulations of EGCG and STAT3. EGCG, (-)-epigallocatechin-3-gallate; RMSD, root mean square deviation; RMSF, root mean square fluctuation.

The RMSD results showed that protein B undergoes large conformational changes during the simulation, but small molecule compounds always bind stably to the binding site (Figure 3C). The results revealed that amino acid residues 200–350 of the protein exhibit RMSFs (Figure 3D). Combined with the protein crystal structure, it can be concluded that the 200–300 part belongs to the flexible region, which contributed most of the RMSD and RMSF during the simulation. During the simulation, the radius of gyration (Rg) and solvent accessible surface (SASA) of the protein continued to decrease, while the hydrogen bonds became more stable (Figure 3E), which also shows that the 200–300 flexible region of the protein gradually stabilized during the simulation.

To further examine the stable binding of the protein and the small molecule compound, the distance between the small molecule compound and the protein was calculated. The results showed that the stable distance between the two always remained at about 4 (Figure 3F). The Gibbs free energy topography showed that the ideal stability results were obtained in the simulation of the two molecules (Figure 3G). Figure 3 shows the molecular docking and dynamics simulation of EGCG and STAT3.

The in vitro anti-TNBC effect of EGCG was evaluated using BT-549 cells

CCK8 was used to evaluate the in vitro killing effect of EGCG on the BT-549 cells (Procell System, CL-0041). As the dose increased, the cell survival rate decreased to less than 20% at 320 µM (Figure 4A), and the IC50 (Half maximal inhibitory concentration) statistics showed that the IC50 of EGCG for BT-549 was 127.3 µM (Figure 4B). After 144 h of culture, EGCG had a significant inhibitory effect on the proliferation of the BT-549 cells, and the number of cell clones formed decreased as the dose increased (Figure 4C). The cell scratch experiment showed that EGCG had a notable effect on BT-549 migration at 50 µM (Figure 4D). Collectively, these results indicate that different concentrations of EGCG exert a killing effect on BT-549 cells, inhibiting proliferation and migration.

Figure 4.

Cytotoxicity, IC50, and scratch clones of EGCG. (A) Cytotoxicity of EGCG on BT-549 cells; (B) IC50 assay for EGCG; (C) the effect of EGCG on the invasion of BT-549 cells; (D) the effect of EGCG on BT-549 cell migration. Images were captured under a microscope using a 4× objective. For the Transwell assay (C), cells on the lower surface were fixed and stained with 0.1% crystal violet before imaging. EGCG, (-)-epigallocatechin-3-gallate; IC50, half-maximal inhibitory concentration.

EGCG reduces cellular H2S levels and STAT3 expression at the gene and protein levels.

To verify the targeting effect of EGCG, this study examined the effect of EGCG on the H₂S (Hydrogen sulfide) level in vitro and measured it by ELISA. After adding EGCG, the concentration of H₂S continued to increase as the concentration of EGCG increased (Figure 5A). For the H₂S level, the half maximal effective concentration (EC₅₀) was 88.2 µM (Figure 5B). H₂S is a key substance of apoptosis, and as the concentration of EGCG increased, the level of H₂S increased, as did the level of apoptosis.

Figure 5.

EGCG reduces cellular H₂S levels and p-STAT3 expression at the gene and protein levels. (A) The effect of EGCG on H₂S production; (B) EC₅₀ curve of EGCG for H₂S production; (C) the effect of EGCG on STAT3 gene expression levels; (D,E) the effect of EGCG on STAT3 protein expression levels in BT-549 cells under the influence of apoptosis inhibitors and activators; (F) flow cytometry validation of EGCG’s effect on apoptosis in BT-549 cells. ns, no significance; ***, P<0.001; ****, P<0.0001. EC₅₀, half maximal effective concentration; EGCG, (-)-epigallocatechin-3-gallate.

The effect of EGCG on STAT3 was studied at the gene and protein levels in vitro. The qPCR results showed that as the EGCG concentration increased, the STAT3 concentration gradually decreased. STAT3 decreased approximately 20-fold at low concentrations and approximately 80-fold at 100 µM (Figure 5C). The statistical gray-value analysis showed that EGCG at 100 µM significantly decreased the STAT3 protein gray value (P<0.0001) (Figure 5D). However, when EGCG and apoptosis inhibitors were added, the level of STAT3 returned to the control level. After adding the agonists, the level of STAT3 decreased. After adding the inhibitors, the level of STAT3 increased. The first two sets of results proved that STAT3 and EGCG had a binding effect. EGCG acts as a target of STAT3. However, after adding inhibitors and agonists of apoptosis, the expression of STAT3 was affected. A strong correlation was found between STAT3 and apoptosis, and EGCG promoted apoptosis (Figure 5E). To further explore the pro-apoptotic effect of EGCG on the BT-549 cells, the BT-549 cells were treated with 25, 50, or 100 µM of EGCG, and flow cytometry was used to detect apoptotic cells. The results showed that treatment with 50 and 100 µM of EGCG significantly increased the apoptosis rate of the BT-549 cells (Figure 5F), which further confirmed that EGCG promoted apoptosis in BT-549 cells.

EGCG exerts anti-apoptotic effects through caspase family proteins and the ERK signaling pathway

Apoptosis is a key cellular process in anti-cancer damage. Suppressing cancer by promoting the apoptosis of cancer cells is a popular area of anti-cancer research. This study explored the pro-apoptotic effect of EGCG on TNBC cells. The results showed that after adding EGCG, the level of Bax/bcl2 gene in the TNBC cells increased significantly, providing strong evidence of pro-apoptosis (Figure 6A). The results related to the inhibitors and agonists further confirmed the effect of EGCG.

Figure 6.

EGCG promotes apoptosis through caspase family and the MAPK signaling pathway. (A-C,E,I) Effects of different concentrations of EGCG on gene expression levels in BT-549 Cells; (D,F-H,J,K) effect of EGCG on STAT3 protein expression levels in BT-549 cells under the influence of apoptosis inhibitors and activators) *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. EGCG, (-)-epigallocatechin-3-gallate.

Caspase-3 is a cysteine-aspartic protease that plays a key role in the apoptosis process and is widely used as a biomarker of apoptosis. After drug treatment, the gene content of caspase-3 and caspase-8 increased significantly in this study. The increases in the high-concentration drug group were approximately 6- and 10-fold, respectively (Figure 6B,6C). IRF9 encodes a member of the interferon regulatory factor (IRF) family, a class of transcription factors with multiple functions, including the virus-mediated activation of interferons and the regulation of cell growth, differentiation, apoptosis, and immune system activity. This study showed that after drug treatment, IRF9 increased in a dose-dependent manner, ultimately increasing by about 10-fold (Figure 6D-6F). The changes in the protein levels were consistent with those observed in the gene levels. At the protein level, as the drug concentration increased, the levels of apoptosis-related proteins also increased, and the effects of the inhibitors and agonists were consistent with those observed in the caspase family proteins (Figure 6G,6H). Cleaved caspase-3 is a commonly used marker for studying cell apoptosis, and its expression can be used to assess whether cells are in an apoptotic state. This study found that the levels of uncleaved caspase-3 were unchanged, while the cleaved caspase-3 levels increased in a concentration-dependent manner, increasing up to 3-fold (Figure 6G). Further, the level of claved-caspase-3 decreased following treatment with apoptosis inhibitors. After adding the apoptosis promoter, the level of claved-caspase-3 was similar to that of the group treated with EGCG alone, indicating that EGCG had a significant inducing effect on the level of caspase cleavage, further illustrating the pro-apoptotic effect of EGCG.

JAK mRNA levels were significantly increased, showing a trend consistent with changes in caspase family gene expression (Figure 6I).

As key proteins in the MAPK, JAK and ERK pathways, the phosphorylation levels of these proteins further illustrate the activation of these signaling pathways. We found that the MAPK pathway was activated in the cells following the EGCG treatment. Compared with the EGCG group, treatment with the inhibitor significantly reduced protein levels, and the effect of the agonist was consistent with the trend observed in the EGCG group (Figure 6J,6K).

Discussion

BC is the most common cancer and the second leading cause of cancer-related death among women in the United States (29). TNBC is a highly aggressive form of BC, characterized by short survival, high mortality, and increased rates of recurrence and metastasis compared with other BC subtypes (30).

STAT3 is an important target for TNBC research. In addition to molecular diagnostic characteristic proteins, STAT3 is currently considered a target with therapeutic significance. Alterations in the JAK/STAT3 signaling pathway are also thought to play an important role in the progression and survival of TNBC (31). Cancer occurs when normal cells progressively transform into malignant cells due to ongoing environmental damage.

EGCG is an important bioactive green tea antioxidant with significant cancer chemopreventive properties (32). EGCG has multiple beneficial effects by controlling the proliferation, invasion, apoptosis, inflammation and DNA demethylation of BC (33). We found that EGCG exerts a significant killing effect on TNBC cells, directly killing cells through toxic effects. This is consistent with the anti-cancer effects of EGCG in ovarian cancer, gastric cancer, lung cancer, and other cancers, The effect is equivalent (34-36). However, the specific anti-cancer molecular mechanism of EGCG in TNBC is still unclear.

Based on the preliminary analysis of the molecular mechanism of EGCG, various PPI analyses were conducted by network pharmacology. Ultimately, STAT3 and IRF9 were identified as the most important connections between EGCG and TNBC. After analyzing the literature, we concluded that these two targets are directly related to the JAK/ERK signaling pathway (37), and speculated that they promote cancer cell apoptosis through the JAK/ERK signaling pathway (38).

After our experimental analysis, we found that the addition of EGCG increased the Bax/Bcl2 ratio, suggesting that EGCG causes apoptosis in TNBC cells. We also found that the content of H₂S, an important marker of apoptosis, was increased, and caspase-3 and caspase-8 of the caspase family were sheared. These experimental results showed that the cells underwent apoptosis, and EGCG plays a significant role in inducing cell apoptosis, which is similar to the role of EGCG in pancreatic cancer (39).

Western blotting and qPCR revealed alterations in the JAK/ERK signaling pathways. Our experimental results using inhibitors and agonists showed that STAT3 is an important target in the process of cell apoptosis. EGCG can bind to STAT3, inducing apoptosis by modulating the ERK and JAK pathways. EGCG has also been reported to activate the MAPK pathway (40,41). Our results showed that in TNBC cells, EGCG clearly induces apoptosis and activates the associated molecular mechanisms.

As a targeted drug for STAT3, EGCG has been widely studied in the fields of obesity, atherosclerosis, and various cancers. However, research on the specific binding mechanism of EGCG and STAT3 is limited. To explore the binding of EGCG and STAT3, we conducted molecular docking and MD simulation experiments, and found that it has a very strong binding force and works through multiple bonding, such as H bonds and Pi-Pi bonds. In the 100 ns dynamics simulation experiment, EGCG and STAT3 were also found to be relatively stable in the 100 ns simulation and had a flexible binding site with the protein binding pocket.

Based on this, we propose that EGCG targets STAT3 and inhibits the JAK/STAT3/ERK signaling pathway to promote the apoptosis of TNBC cells. After phenotypic verification using TNBC cells, we found that EGCG exerts toxic and inhibitory effects on cells, while opposite experimental results were obtained in related experiments following the addition of inhibitors and agonists.

A limitation of this study is that we did not include a non-tumor breast epithelial cell line to directly evaluate whether the effects of EGCG are preferential to tumor cells. Future studies will incorporate appropriate non-tumor controls to assess tumor selectivity and further evaluate the safety profile of EGCG. Although our results are promising, additional limitations should be acknowledged. The present work is based on an in vitro experimental platform, and in vivo tumorigenesis and pathway cross-talk remain to be investigated in future studies.

Conclusions

In this study, we confirmed the anti-TNBC effects of EGCG using experiments such as cytotoxicity assays, cloning formation inhibition, scratch assays, and H₂S level measurements. Western blotting and qPCR were used to examine the apoptotic effects of EGCG via the JAK/ERK pathway, demonstrating its ability to induce cancer cell apoptosis. The relevant target, STAT3, was identified by network pharmacology, and its targeting effect was verified by MD simulation. Our findings showed that EGCG activates the JAK/ERK signaling pathway by targeting STAT3, ultimately inducing cell apoptosis, and exerting cytotoxic and inhibitory effects on cancer cells.

Supplementary

The article’s supplementary files as

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Data Sharing Statement

Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2883/dss

tcr-15-01-61-dss.pdf ^{(93.6KB, pdf)}

DOI: 10.21037/tcr-2025-1-2883