The Encyclopedia of Traditional Chinese Medicine (ETCM) database [26] (http://www.tcmip.cn/ETCM/), HERB database [27] (http://herb.ac.cn/), and relevant literature [28, 29] were used to identify the active components of Dendrobium. The structures and SMILES identifiers were retrieved from the PubChem database [30] (https://pubchem.ncbi.nlm.nih.gov/). Active compounds were further filtered using the SwissADME database [31] (https://www.swissadme.ch/) by applying the criteria of high gastrointestinal absorption (GI absorption) and drug-likeness, defined by satisfying at least two of the five established rules (Lipinski, Ghose, Veber, Egan, and Muegge) to obtain relevant pharmacologically active compounds [32, 33].

The potential Dendrobium bioactive compound targets were identified through consensus predictions across multiple databases, including SwissTargetPrediction [34] (https://www.swisstargetprediction.ch/), TargetNet [35] (http://targetnet.scbdd.com/), and Super-PRED [36] (https://prediction.charite.de/). Targets with probability scores exceeding 50% were further validated using the UniProt database [37] (https://www.uniprot.org/), retaining only human protein-coding genes. After deduplication, a curated set of potential Dendrobium targets was established for subsequent mechanistic analyses.

Relevant targets were obtained from the GeneCards database [38] (https://www.genecards.org/Search/) by searching for the keywords “Dry Eye” and “keratoconjunctivitis sicca”. Genes with a score of $>$10 were standardized using the UniProt database [37] and selected as potential therapeutic targets for DED. The intersection of these targets with the potential targets of Dendrobium’s active compounds was analyzed to identify possible Dendrobium targets for treating DED.

A high-confidence interaction score threshold (“High Confidence $>$0.7”) was established, and the target protein interaction data were imported into Cytoscape software (version 3.10.3; Cytoscape Consortium, San Diego, CA, USA) to construct the Dendrobium active compound–potential therapeutic target network. The CytoNCA plugin was used to identify key active compounds and critical targets based on node degree and betweenness centrality parameters. The top ten results from both the degree and betweenness centrality analyses were intersected to determine the most important active compounds and targets.

We used the Enrichr platform (https://maayanlab.cloud/Enrichr/) [39] to perform a biological function analysis of the targets involved in Dendrobium treatment of DED using Gene Ontology Biological Process (GO_BP) terms. Pathway analysis was conducted using the MSigDB Hallmark gene sets.

Molecular docking between the protein and small-molecule compounds was performed using the Discovery Studio software (version 2019; Dassault Systèmes Biovia, San Diego, CA, USA). The protein-ligand complex then underwent a 100-nanosecond molecular dynamics simulation using GROMACS 2022. CHARMM36 and GAFF2 force fields were used to parameterize the protein and ligand, respectively. The system was solvated with explicit TIP3P water molecules in a cubic periodic box with a 1.2 nm boundary, using the Particle Mesh Ewald method for long-range electrostatics and the Verlet cutoff scheme. Energy minimization was conducted prior to the production runs under NPT ensemble conditions. The system was equilibrated with 100,000 steps each of NVT and NPT simulations ($\tau$ = 0.1 ps, duration = 100 ps per equilibration). Van der Waals and Coulomb interactions were treated with a 1.0 nm cutoff. Production molecular dynamics simulations were performed for 100 ns using GROMACS software (version 2022; Science for Life Laboratory, Stockholm, Sweden), followed by MM/PBSA binding free-energy calculations at equilibrium.

SV40-immortalized human corneal epithelial cells (HCECs, obtained from ATCC CRL-11135) were cultured in DMEM supplemented with 10% FBS (AusGeneX, Molendinar, QLD, Australia; Lot No. FBS500-S) and 1% penicillin/streptomycin (Thermo Fisher Scientific, Inc., Waltham, MA, USA; Cat No. 15140-122) at 37 °C under 5% CO₂. This cell line was validated using STR profiling and tested negative for Mycoplasma sp. To simulate hyperosmotic conditions, cells were treated with NaCl to achieve an osmolarity between 312 and 550 mOsm. In some experiments, cells were incubated with 3-O-methylgigantol (0–10 µM) (EvitaChem, Linyi, Shandong, China; Cat No. EVT-8342849) and Erlotinib (0–10 µM) (Selleck Chemicals, Houston, TX, USA; Cat No. S7786).

Cell viability was measured using a CCK-8 kit (Biosharp Life Sciences, Hefei, Anhui, China; Cat No. BS350A). Cells were seeded at 1 $\times$ 10⁴ cells per well in a 96-well plate and incubated under the respective treatment conditions. Following incubation, CCK-8 solution was added to the wells and incubated at 37 °C for 2 h before measuring absorbance at 450 nm using a Varioskan LUX microplate reader (Thermo Fisher Scientific, Inc., Waltham, MA, USA).

Intracellular ROS levels were measured using 2^′,7^′-dichlorofluorescin diacetate (DCFH-DA) (Beyotime Institute of Biotechnology, Shanghai, China; Cat No. S0035S). The cells were incubated with 10 µM DCFH-DA at 37 °C for 30 min and observed under a Varioskan LUX microplate reader (Thermo Fisher Scientific, Inc., Waltham, MA, USA). The fluorescence intensity of each cell type was then measured.

Apoptosis was assessed using calcein-AM and propidium iodide (PI) staining. The calcein-AM (Aladdin Biochemical Technology Co., Ltd., Shanghai, China; Cat No. C273362) stock solution contained 1 mM calcein-AM in dimethyl sulfoxide (DMSO). The stock solution was diluted to 1–50 µM with PBS. The stock solution of PI (Solarbio Science & Technology Co., Ltd., Beijing, China; Cat No. C0080) was diluted with PBS to 10–50 µM. The cells were incubated with calcein-AM and PI at room temperature for 15 min and observed under a fluorescence microscope. Calcein-positive cells were considered live, and PI-positive cells were considered apoptotic.

The concentrations of pro-inflammatory cytokines, including interleukin-6 (IL-6), interleukin-1 beta (IL-1$\beta$), tumor necrosis factor-alpha (TNF-$\alpha$), in the cell culture supernatants were determined using commercial Enzyme-Linked Immunosorbent Assay (ELISA) kits (R&D Systems; Bio-Techne, Minneapolis, MN, USA). Specifically, the following kits were employed: IL-6 (Cat No. D6050), IL-1$\beta$ (Cat No. DLB50), TNF-$\alpha$ (Cat No. DTA00C), and IL-8 (Cat No. D8000C). All procedures were performed strictly according to the manufacturer’s instructions. The absorbance was measured at 450 nm using a Varioskan LUX microplate reader (Thermo Fisher Scientific, Inc., Waltham, MA, USA).

Biotinylation of 3-O-methylgigantol was achieved using a two-step click chemistry strategy. First, the alkynyl intermediate was prepared by dissolving 3-O-methylgigantol (1.0 eq) in anhydrous acetone, followed by the addition of anhydrous K₂CO₃ (2.0 eq) as a base. Propargyl bromide (1.2 eq) was added dropwise with stirring, and the mixture was refluxed for 2–4 h. Upon completion, the inorganic salts were filtered, and the filtrate was concentrated under reduced pressure and purified using silica gel column chromatography to yield alkynyl-3-O-methylgigantol. Subsequently, copper-catalyzed azide-alkyne cycloaddition (CuAAC) was performed by co-dissolving the obtained alkynyl intermediate and biotin-azide (1.1 eq) in a DMSO/water mixture (1:1, v/v). CuSO₄ (0.1 eq) and sodium ascorbate (0.5 eq) aqueous solutions were added sequentially to generate the catalytic Cu(I) species in situ. The reaction mixture was stirred at room temperature in the dark for 1–2 hours. After confirming reaction completion by TLC, the mixture was extracted with ethyl acetate. The crude product was purified by preparative HPLC or column chromatography, then dried under vacuum to yield the final 3-O-methylgigantol-biotin conjugate.

A biotin-streptavidin pull-down assay was performed to verify the direct interaction between 3-O-methylgigantol and EGFR. HCECs lysates were prepared in a non-denaturing lysis buffer supplemented with protease and phosphatase inhibitors. Lysates (1 mg protein) were incubated with 20 µM biotin-3-O-methylgigantol overnight at 4 °C. Lysates were pre-incubated with a 10-fold excess (200 µM) of unlabeled 3-O-methylgigantol for 2 h prior to probe addition for competitive inhibition. Biotinylated complexes were captured using Pierce Streptavidin Magnetic Beads (Thermo Fisher Scientific, Inc., Waltham, MA, USA; Cat No. 88817) for 2 h at 4 °C. After five washes with lysis buffer, the bound proteins were eluted by heating in sodium dodecyl sulfate (SDS) loading buffer and analyzed by western blotting using a specific anti-EGFR antibody (Cell Signaling Technology, Inc., Danvers, MA, USA; Cat No. 4267; 1:1000 dilution).

Total protein was extracted from cells using radioimmunoprecipitation assay (RIPA) lysis buffer (Thermo Fisher Scientific, Inc., Waltham, MA, USA; Cat No. 89900) supplemented with protease and phosphatase inhibitor cocktails. The protein concentration was determined using a BCA protein assay kit. Equal amounts of protein (20 µg) were separated by 10% SDS-PAGE and transferred onto PVDF membranes (Millipore). Membranes were blocked with 5% bovine serum albumin (BSA) in TBST for 1 h at room temperature to minimize nonspecific binding. Subsequently, the membranes were incubated overnight at 4 °C with the following primary antibodies from Cell Signaling Technology, Inc. (Danvers, MA, USA): p-EGFR (Tyr1068, #3777; 1:1000), EGFR (#4267; 1:1000), p-AKT (Ser473, #4060; 1:1000), AKT (#4691; 1:1000), and $\beta$-Actin (#4970; 1:1000). After washing three times with TBST, the membranes were incubated with HRP-conjugated secondary antibodies (Cell Signaling Technology, Inc., Danvers, MA, USA; #7074; 1:3000) for 1 h at room temperature. Protein bands were visualized using an enhanced chemiluminescence (ECL) detection kit (Thermo Fisher Scientific, Inc., Waltham, MA, USA; Cat No. 34580). The chemiluminescent signals were captured using a ChemiDoc MP Imaging System (Bio-Rad Laboratories, Inc., Hercules, CA, USA). and analyzed using the ImageJ software (version 1.53; National Institutes of Health, Bethesda, MD, USA).

Study outcomes were quantified using mean $\pm$ standard deviation. The hypothesis testing was conducted in an R computational environment. Comparisons between two groups were conducted using Student’s t-test using the t-test function. Comparisons among multiple groups were performed using one-way analysis of variance (ANOVA) via the aov function, followed by Bonferroni’s post-hoc test using the PostHocTest function from the DescTools package. Statistical significance was set at p 0.05.

A total of 92 compounds from Dendrobium were identified using the ETCM database [26], HERB database [27], and relevant literature [28, 29] (Fig. 1A). After screening based on parameters, such as high gastrointestinal absorption and drug-likeness, requiring at least two positive indicators, and supplementation with Dendrobium compounds reported in the literature to have high content and pharmacological activity, a total of 43 active compounds were selected (Fig. 1A). The basic information on the active compounds is presented in Supplementary Table 1.

Fig. 1.

Network pharmacology analysis of Dendrobium in the treatment of dry eye disease (DED). (A) Databases used for screening Dendrobium spp.-related and DED-related therapeutic targets. (B) Venn diagram illustrating the intersection (76 common targets) between Dendrobium spp.-related targets and DED-related targets, representing potential Dendrobium targets for treating DED. (C) The Protein-Protein Interaction (PPI) network of the 76 common targets. The top 8 key therapeutic targets, identified based on degree values, are highlighted in red. (D) The Dendrobium active compound-target network. The 4 key active compounds, identified based on degree and betweenness centrality, are highlighted in red. (E,F) Functional and pathway enrichment analyses of the potential targets using Gene Ontology Biological Process (GO_BP) and MSigDB_Hallmarks databases.

The Swiss Target Prediction, Super-PRED, and TargetNet databases were used to predict 909 targets for the 43 active compounds in Dendrobium (Fig. 1A). A total of 6787 DED-related therapeutic targets were retrieved from the GeneCards database, and 601 targets with relevance scores greater than 10 were selected as highly associated with DED (Fig. 1A). By intersecting the 909 predicted targets of Dendrobium active compounds with 601 DED-related therapeutic targets, 76 common targets were identified. These were considered as potential targets through which Dendrobium may exert therapeutic effects on DED (Fig. 1B).

The top eight key targets based on degree values were APP, AKT1, SRC, EGFR, ESR1, STAT3, ALB, and TNF, which were identified as the primary targets of Dendrobium in the treatment of DED (Fig. 1C). Detailed information of the protein-protein interaction (PPI) network is presented in Supplementary Table 2. Additionally, a network of Dendrobium active compounds and potential therapeutic targets was constructed, and the key interaction data are provided in Supplementary Table 3. The top 10 compounds based on intermediate and betweenness centrality (BC) values intersected, yielding four key active compounds: Scoparone, Caffeic acid, 2-Hydroxy-4,7-dimethoxy-9,10-dihydrophenanthrene, and 3-O-methylgigantol, which were considered to be the main active compounds in the treatment of DED (Fig. 1D).

Biological functions and pathway predictions for the 76 Dendrobium targets for DED treatment were generated using the Enrichr database. MSigDB_Hallmark analysis revealed that these targets were enriched in the PI3K/AKT/mTOR and IL-6/JAK/STAT3 signaling pathways, as well as in apoptotic processes (Fig. 1E). Similarly, Gene Ontology Biological Process analysis indicated enrichment in the activation of cell surface tyrosine kinase signaling and positive regulation of PI3K signaling (Fig. 1F). These analyses allowed us to identify the key biological pathways and molecular mechanisms through which Dendrobium may exert therapeutic effects in the treatment of DED.

Molecular docking was performed using Discovery Studio to analyze the interactions within the Dendrobium active compound–potential therapeutic target network, and the lowest binding affinities were recorded. The results showed that 3-O-methylgigantol exhibited the lowest docking binding affinity with EGFR, at –32.2651 kcal/mol (Fig. 2A). Additionally, the top eight active compound–therapeutic target pairs with the lowest binding affinities were selected for visualization of the molecular docking results (Fig. 2A–H). The binding affinities of these active compounds with their respective targets were all below –5 kcal/mol, indicating that the key active compounds of Dendrobium exhibited strong binding affinities to critical targets in DED patients (Fig. 2A–H).

Fig. 2.

Molecular docking of key Dendrobium active compounds with predicted targets. Molecular docking was performed using Discovery Studio 2019. The figure displays the binding modes and lowest binding energies for the top 8 compound-target pairs, ranked from lowest to highest energy: (A) EGFR – 3-O-methylgigantol, (B) EGFR – Caffeic acid, (C) ESR1 – Caffeic acid, (D) TNF – Caffeic acid, (E) TNF – 3-O-methylgigantol, (F) SRC – Scoparone, (G) ESR1 – 3-O-methylgigantol, and (H) EGFR – Scoparone. Binding affinities less than –5 kcal/mol indicate strong interactions. EGFR, Epidermal Growth Factor Receptor; ESR1, Estrogen Receptor 1; TNF, Tumor Necrosis Factor; SRC, Proto-oncogene Tyrosine-protein Kinase Src.

Despite its high centrality across multiple targets, EGFR has been prioritized for molecular dynamics (MD) simulations owing to its biological and physical significance. Biologically, as an upstream cell surface receptor, EGFR initiates intracellular signaling cascades and is a highly accessible target for pharmacological interventions. Physically, it exhibited the lowest binding energy in docking screening, indicating the most stable interaction.

The binding stability of the 3-O-methylgigantol-EGFR complex was investigated using Gromacs, with simulations performed in triplicate. RMSD is a good indicator of the conformational stability of the protein-ligand complex and of the deviation of atomic positions from their initial positions. Lower RMSD values signified restricted backbone fluctuations, reflecting persistent ligand-binding positions. As shown in Fig. 3A, the complex system reached equilibrium after 80 ns, with fluctuations around 2 Å across the three replicates, indicating that the small molecule exhibits high stability upon binding to the target protein.

Fig. 3.

Molecular dynamics (MD) simulation and validation of the 3-O-methylgigantol–EGFR interaction. A 100 ns MD simulation was conducted using Gromacs 2022 in three independent replicates. Analysis included: (A) RMSD. (B) Radius of Gyration (Rg). (C) SASA. (D) Number of hydrogen bonds. (E) RMSF. (F) Free Energy Landscape (FEL) profile. (G) 3D and (H) 2D representations of the complex conformation at the minimum binding free energy. (I) MM/PBSA analysis identifying key amino acid residues contributing to the binding free energy. (J,K) The distances between 3-O-methylgigantol and the ASP855 (J) and LEU788 (K) residues within the EGFR active site. (L) HCEC cell lysates were incubated with biotinylated 3-OMG (Biotin-3-OMG) or biotin control, followed by precipitation with streptavidin-conjugated beads. For the competition group, lysates were pre-incubated with an excess of unlabeled 3-OMG prior to the addition of Biotin-3-OMG. The presence of EGFR in the pull-down precipitates was detected by immunoblotting. Phosphorylation levels of EGFR (Tyr1068) and total EGFR were analyzed by Western blotting. Data are presented as means $\pm$ SD. ***p 0.001. Pairwise comparisons were conducted using One-way ANOVA with Bonferroni’s post hoc test (L). RMSD, Root Mean Square Deviation; SASA, Solvent-Accessible Surface Area; RMSF, Root Mean Square Fluctuation; MM/PBSA, Molecular Mechanics/Poisson-Boltzmann Surface Area; HCEC, Human Corneal Epithelial Cell; ANOVA, Analysis of Variance.

We used two metrics, the radius of gyration (Rg) and solvent-accessible surface area (SASA), to assess the molecular stability. Rg measures protein compactness and reflects tertiary structure. The SASA quantifies the surface area of a protein that is accessible to the solvent during binding. The SASA is commonly used in simulations to study protein folding, unfolding, and structural stability. Further analysis revealed that the Rg and SASA of the complex remained stable throughout the simulation, indicating that the small molecule–target protein complex did not undergo significant expansion or contraction during movement (Fig. 3B,C). Hydrogen bonds play a crucial role in the binding between the ligand and protein. Fig. 3D shows the number of hydrogen bonds formed between the small molecule and the target protein over time. Throughout the simulation, this count fluctuated between 0 and 4, stabilizing at approximately 2 in most frames, which was consistent across the three replicates. This consistent presence indicated strong hydrogen bonding interactions within the 3-O-methylgigantol-EGFR complex (Fig. 3D).

RMSF quantifies the positional variance in protein residues and maps local flexibility along the polypeptide chain. As shown in Fig. 3E, the RMSF values of the complex are relatively low (mostly below 4 Å), indicating low flexibility and high stability. The RMSF profiles of the three replicates were nearly identical with minor variations across a few regions.

The free energy landscape (FEL) uses a visualization of free energy changes to more intuitively explore the molecular energy landscape and protein-ligand interactions. The energy minima represent stable states, whereas the maxima indicate barriers to conformational change. This method can predict ligand-binding affinities and elucidate molecular recognition mechanisms. In the conformations in which the energy minima occurred in the complex (Fig. 3F–H), van der Waals interactions were formed between the small molecule and the receptor at residues GLY857, ALA763, MET766, LEU777, CYS775, ARG776, CYS797, LEU718, MET793, LEU792, GLY796, THR854, and THR790. Additionally, the receptor residue ASP855 formed a conventional hydrogen bond with small molecules. Furthermore, ASP855 and LYS745 exhibited $\pi$-Anion interactions with the small molecule.

Based on the binding conformation of the complex, the binding free energy of 3-O-methylgigantol to EGFR was calculated using MM/PBSA. The binding energies of the three replicates were –12.054, –12.240, and –11.921 kcal/mol, respectively, with a standard deviation of 0.131 kcal/mol. respectively, with a standard deviation of 0.547 kJ/mol. This narrow range underscores the reliability of the calculated binding affinities (Fig. 3I). Further analysis identified key amino acids that contributed significantly to the binding of 3-O-methylgigantol within the complex. Residues ASP855 and LEU788 of EGFR exhibited high contribution values (Fig. 3I), suggesting they may play important roles in the catalytic process.

We monitored the dynamic distances between 3-O-methylgigantol and two key residues to validate the ligand’s stability. The hydrogen bond formed between the polar group of the ligand and the acidic side chain of ASP855 remained stable in all three replicate simulations, exhibiting minimal fluctuations throughout the simulations (Fig. 3J). Similarly, the hydrophobic $\pi$-alkyl interaction between the ligand’s benzene ring and the side chain of LEU788 remained stable, with distances consistently below 5 Å (Fig. 3K). These consistent interactions confirmed the ligands’ stable anchoring within the active site.

In conclusion, 3-O-methylgigantol binds stably to EGFR with a low binding free energy, suggesting that it may exert its effects via EGFR.

A 3-O-methylgigantol-biotin conjugate probe (biotin-3-OMG) was synthesized via click chemistry to validate EGFR as a direct target of 3-O-methylgigantol (Supplementary Fig. 1). A pull-down assay was performed using a biotinylated probe (biotin-3-OMG). Immunoblotting revealed that biotin-3-OMG significantly enriched EGFR from cell lysates compared to that in the biotin control, indicating a physical interaction (Fig. 3L). Notably, this binding was specific, as it was markedly blocked by preincubation with excess unlabeled 3-OMG (Fig. 3L). Furthermore, 3-OMG treatment significantly upregulated EGFR phosphorylation (p-EGFR, Tyr1068) (Fig. 3L). Collectively, these findings demonstrate that 3-O-methylgigantol directly binds to EGFR and induces its activation.

Cell viability of human corneal epithelial cells (HCECs) treated with varying concentrations of hypertonic solutions was assessed using the CCK-8 assay to determine the optimal concentration. In our hypertonic stress experiment, after 24 h of incubation, cell viability decreased in a dose-dependent manner as the osmotic pressure increased from 312 to 550 mOsm (Supplementary Fig. 2). When the osmotic pressure reached 550 mOsm, the cell viability declined to approximately 50%; therefore, this concentration was selected for subsequent experiments. Following treatment with different concentrations of 3-O-methylgigantol (312–550 mOsm) for 24 h, the cell viability increased in a dose-dependent manner. At a 5 µM concentration, 3-O-methylgigantol significantly enhanced the cell viability (Fig. 4A) and mitigated the reduction in cell viability induced by hypertonic stress (Fig. 4B).

Fig. 4.

3-O-methylgigantol ameliorates hyperosmotic-induced cytotoxicity in HCECs via the EGFR pathway. (A–D) Viability of Human Corneal Epithelial Cells (HCECs) measured by the CCK-8 assay (n = 4 biological repeats): (A) HCECs treated with different concentrations of 3-O-methylgigantol for 24 h under normal conditions. (B) HCECs were treated with different concentrations of 3-O-methylgigantol for 24 h under 550 mOsM hyperosmotic stress. (C) HCECs were treated with different concentrations of the EGFR inhibitor Erlotinib for 24 h under normal conditions. (D) HCECs treated with 5 µM 3-O-methylgigantol alone or combined with different concentrations of Erlotinib for 24 h under 550 mOsM stress. (E–J) HCECs treated with 5 µM 3-O-methylgigantol $\pm$ 5 µM Erlotinib under 550 mOsM stress for 24 h (n = 5 biological repeats): (E) Calcein-AM/PI staining for cell apoptosis (Green: live cells, Red: apoptotic cells). Scale bar = 60 µm. (F) DCFH-DA staining for intracellular ROS. Scale bar = 60 µm. (G–J) ELISA measurement of inflammatory cytokines TNF-$\alpha$, IL-1$\beta$, IL-6, and IL-8. (K–M) Western blot analysis of EGFR and AKT phosphorylation. Cells were treated with EGF (100 ng/mL) for 5 min. Representative protein bands and quantification of the p-EGFR/EGFR and p-AKT/AKT ratios. Data are presented as means $\pm$ SD. N.S., p $\ge$ 0.05; *p 0.05, **p 0.01, ***p 0.001. Pairwise comparisons were conducted using One-way ANOVA with Bonferroni’s post hoc test (A–M). CCK-8, Cell Counting Kit-8; DCFH-DA, 2^′,7^′-dichlorofluorescin diacetate; ROS, Reactive Oxygen Species; ELISA, Enzyme-linked Immunosorbent Assay; TNF-$\alpha$, Tumor Necrosis Factor-Alpha; IL-1$\beta$, Interleukin-1 Beta; IL-6, Interleukin-6; IL-8, Interleukin-8; AKT, AKT serine/threonine kinase; EGF, Epidermal Growth Factor; SD, Standard Deviation.

To determine whether EGFR is a key mediator of 3-O-methylgigantol-induced HCEC activation, we used the EGFR inhibitor, erlotinib. Erlotinib binds to the ATP-binding site, preventing the activation of EGFR kinase by binding to ATP and transferring phosphate groups to substrates. First, to rule out potential off-target cytotoxicity, we evaluated the viability of HCECs treated with a concentration gradient of Erlotinib (0.1, 0.5, 1, 5, and 10 µM) under normal culture conditions. The CCK-8 assay indicated that Erlotinib exhibited no significant toxicity at concentrations up to 5 µM (Fig. 4C). Therefore, we performed dose-dependent blocking assays. Under hypertonic stress, HCECs were co-treated with 5 µM 3-O-methylgigantol and increasing concentrations of Erlotinib (0.1, 1.0, and 5.0 µM). Erlotinib attenuated the protective effect of 3-O-methylgigantol in a dose-dependent manner. While 0.1 µM showed partial inhibition, 5 µM Erlotinib completely abolished the 3-O-methylgigantol-mediated restoration of cell viability and exacerbated hypertonic damage (Fig. 4D). These findings confirm that the protective action of 3-O-methylgigantol is specifically dependent on EGFR signaling activity.

Cell apoptosis was assessed using calcein-AM/PI staining, revealing that 3-O-methylgigantol alleviated hypertonic-induced apoptosis in HCECs, an effect that was blocked by Erlotinib (Fig. 4E). DCFH-DA staining, used to measure intracellular ROS levels, showed that 3-O-methylgigantol reduced hypertonic-induced ROS accumulation in HCECs, and this effect was inhibited by Erlotinib (Fig. 4F). ELISA assays measured the levels of TNF-$\alpha$, IL-1$\beta$, IL-6, and IL-8, which were elevated in HCECs under hypertonic conditions (Fig. 4G–J). Treatment with 3-O-methylgigantol reduced the hypertonicity-induced increase in inflammatory cytokines, an effect blocked by Erlotinib (Fig. 4G–J). Western blotting was performed to elucidate the underlying mechanism. The results indicated that the phosphorylation levels of EGFR and its downstream effector AKT were suppressed under hypertonic conditions (Fig. 4K–M). However, 3-O-methylgigantol treatment restored p-EGFR and p-AKT expression, whereas the addition of Erlotinib abolished this activation (Fig. 4K–M), confirming the dependence of 3-O-methylgigantol on EGFR signal activation. These results suggest that 3-O-methylgigantol acts through EGFR to significantly alleviate epithelial apoptosis, oxidative stress, and inflammation under hypertonic conditions.