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Article

Investigation of the Anti-Lung Cancer Mechanisms of Taraxacum officinale Based on Network Pharmacology and Multidimensional Experimental Validation

1
School of Chemical Engineering and Technology, Tianjin University of Technology, Tianjin 300384, China
2
Analysis and Testing Center, Tianjin University of Technology, Tianjin 300384, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Pharmaceuticals 2025, 18(5), 663; https://doi.org/10.3390/ph18050663
Submission received: 4 March 2025 / Revised: 8 April 2025 / Accepted: 10 April 2025 / Published: 30 April 2025
(This article belongs to the Special Issue Adjuvant Therapies for Cancer Treatment)

Abstract

:
Background:Taraxacum officinale(commonly known as dandelion) is a medicinal and edible plant, with the entire plant being used for therapeutic purposes. Studies have demonstrated that dandelion exhibits inhibitory effects against various types of cancer. However, research on its potential for lung cancer (LC) treatment is limited, and the specific compounds responsible for its anticancer effects, as well as the underlying mechanisms, remain unclear. Methods: This study aimed to elucidate the underlying pharmacological mechanisms by which dandelion exerts therapeutic effects against LC. Initially, active compounds of dandelion and their corresponding targets were retrieved from public databases. Subsequently, network pharmacology approaches were applied to identify LC-associated disease targets. By integrating drug-specific targets and disease-related targets, a comprehensive dandelion–lung cancer interaction network was established. Protein–protein interaction (PPI) analyses and functional enrichment studies were further performed to uncover potential molecular mechanisms. Additionally, molecular docking and molecular dynamics (MD) simulations were conducted to evaluate binding interactions between critical active constituents and core targets. To experimentally validate these findings, in vitro cellular assays combined with scanning electron microscopy (SEM) were employed to investigate the anticancer effects of taraxasterol, a key bioactive sterol compound isolated from dandelion, on LC cells. Results: Our analyses identified 58 active compounds in dandelion linked to 614 potential targets, of which 228 targets were associated with LC. The PPI network highlighted 16 core targets, notably TP53, CASP3 and EGFR. Functional enrichment analysis suggested that dandelion might exert its anticancer effects by modulating the tumor microenvironment through the regulation of these critical targets. Molecular docking results demonstrated stable binding interactions between major active compounds and the identified core targets. Furthermore, the anticancer activity of taraxasterol was experimentally validated through in vitro assays and SEM-based morphological assessments, confirming its inhibitory effects on A549 lung cancer cells. Conclusions: Collectively, our findings reveal a multi-targeted therapeutic mechanism of dandelion against LC and support its potential development as a novel natural candidate for lung cancer treatment.

1. Introduction

According to global cancer statistics, lung cancer ranks first in both incidence and mortality among all malignant tumors, posing a significant threat to global public health. Data from GLOBOCAN 2020 indicate that LC accounts for approximately 12.4% of all newly diagnosed cancers and contributes to 18% of total cancer-related deaths worldwide [1]. Although various treatment strategies—such as surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy—are widely applied in clinical settings [2], their efficacy remains limited due to issues such as drug resistance, high recurrence rates, severe side effects, and substantial treatment costs. In particular, the survival rate of patients with advanced LC remains poor, highlighting the urgent need for novel therapeutic strategies that are both effective and low in toxicity, while maintaining cost-efficiency.
In recent years, natural products have gained increasing attention in cancer therapy owing to their multi-target and multi-mechanism characteristics [3,4]. Dandelion, a traditional edible and medicinal plant, has demonstrated anti-inflammatory, antioxidant, and antitumor properties, and exhibits potential therapeutic value in the treatment of various malignancies [5,6]. Previous studies have shown that extracts of dandelion exert anticancer effects through multiple mechanisms, including induction of apoptosis [7,8], inhibition of cell proliferation, regulation of oxidative stress, and modulation of the tumor microenvironment [9,10]. However, the specific molecular mechanisms underlying its therapeutic potential in LC remain inadequately studied, and further systematic investigation is warranted to elucidate its key targets and pharmacological pathways.
Network pharmacology [11,12], as a powerful tool for elucidating the complex mechanisms of traditional herbal medicines, enables the systematic identification of potential targets and signaling pathways involved in multi-component therapeutic actions. In addition, molecular docking [13,14] and molecular dynamics (MD) simulations [15,16] can provide theoretical support for drug–target interactions by evaluating the binding affinity and stability between active compounds and core targets.
In this study, we utilized network pharmacology approaches to screen for active components of dandelion with potential anti-LC activity and identify their corresponding targets. Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein–protein interaction (PPI) network construction were subsequently conducted to explore the underlying molecular mechanisms. Molecular docking and MD simulations were then performed to assess the binding interactions between major active components and key targets. Finally, in vitro cell-based assays [17] and scanning electron microscopy (SEM) analysis [18] were used to validate the anti-LC activity of taraxasterol, a representative sterol compound derived from dandelion. This study aims to provide a scientific basis for the application of dandelion in LC treatment and offer new insights into the multi-target mechanisms of natural products in cancer therapy.

2. Results

2.1. Network Pharmacology Research

2.1.1. Key Active Compounds of Dandelion

Active compounds of dandelion were screened from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database using the criteria of oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18. In addition, compounds that have been explicitly reported in the literature to exhibit anticancer activity or possess anticancer potential were also included. A total of 58 active compounds and 228 lung cancer-related targets were identified, yielding 664 compound–target interaction pairs. Cytoscape 3.8.2 software was used to construct a herb–compound network (Figure 1a) and a compound–target network (Figure 1b). In the compound–target network (Figure 1b), nodes are sized according to their degree values, with larger nodes representing higher degrees and greater centrality within the network. Based on degree ranking, the top six key compounds were identified (Figure 2), namely MOL000098 (quercetin, degree = 132), MOL000008 (apigenin, degree = 61), MOL000422 (kaempferol, degree = 51), MOL000006 (luteolin, degree = 48), MOL000449 (beta-sitosterol, degree = 27), and MOL000354 (isorhamnetin, degree = 25).

2.1.2. Key Active Compounds and Their Targets Associated with LC

LC-associated targets were systematically retrieved from GeneCards, UniProt, and DisGeNET databases. Following filtration based on a relevance score ≥ 40, 416 potential disease-related targets were identified (Figure 1c).

2.1.3. Intersection Targets Between Dandelion and LC

A Venn diagram was constructed using the Bioinformatics online platform (https://www.bioinformatics.com.cn, accessed on 6 August 2024). This diagram illustrates the overlapping targets between the predicted anticancer targets of active components from dandelion (228 targets) and LC-related targets (416 targets). As shown in Figure 3a, a total of 49 common targets were identified.

2.1.4. PPI Network Analysis of Core Targets Associated with the Anti-LC Effects of Dandelion

The 49 overlapping targets were imported into the STRING database for protein–protein interaction (PPI) analysis. The resulting PPI network was visualized using Cytoscape 3.8.2 software (Figure 3b). Subsequently, key nodes were screened using CytoNCA analysis with a betweenness centrality (BC) threshold ≥ 40. This analysis yielded a refined network comprising 16 nodes and 176 edges. In the visualized network, nodes with higher BC values are depicted as larger and darker, reflecting their greater centrality, and functional significance.

2.1.5. GO and KEGG Analysis

The overlapping genes were imported into the Metascape database for enrichment analysis. Terms with a screening criterion of p < 0.05 were considered statistically significant, and the top 10 enriched terms from each GO category—including molecular function (MF), biological process (BP), and cellular component (CC)—were selected based on ascending p-values. The results of GO enrichment are presented as bar graphs (Figure 3c). The GO enrichment results revealed that, at the molecular function level, the anti-LC mechanisms of dandelion mainly involve ubiquitin-like protein ligase binding, kinase regulator activity, ubiquitin protein ligase binding, and cytokine receptor binding. At the biological process level, the enriched terms primarily include response to hormone, negative regulation of cell population proliferation, response to external stimulus, positive regulation of phosphorylation, regulation of apoptotic signaling pathways, and positive regulation of cell migration. Regarding cellular components, the key targets were mainly associated with transcription regulator complexes, caveolae, membrane rafts, and membrane microdomains. In addition, the top 20 pathways identified through KEGG pathway analysis were visualized as bar graphs (Figure 3d). The results indicate that the potential overlapping targets are closely related to several cancer-associated pathways, including pathways in cancer, bladder cancer, proteoglycans in cancer, prostate cancer, PI3K-Akt signaling pathway, non-small-cell-lung cancer, and glioma.

2.2. Molecular Docking Results

Based on the results of network pharmacology prediction, six key targets—TP53, CASP3, EGFR, AKT1, ESR1, and NQO1—were selected for molecular docking with four major active compounds: quercetin, apigenin, kaempferol, and luteolin. In addition to these network-screened components, taraxasterol, a pentacyclic triterpenoid compound unique to dandelion, was also included for further analysis. Although limited target data for taraxasterol were available in public databases, its well-documented anti-inflammatory and potential antitumor activities suggest significant pharmacological relevance. Therefore, taraxasterol was incorporated into the molecular docking, molecular dynamics (MD) simulations, and in vitro validation experiments to further evaluate its anti-LC potential. Molecular docking results revealed favorable binding affinities between all selected active compounds and the core target proteins. As shown in Table 1, all docking scores were less than −6.5 kcal/mol, indicating stable interactions. It is generally accepted that the lower the binding energy, the more stable the ligand–receptor complex and the greater the binding potential. Representative docking interactions were visualized using Discovery Studio 2019 software. Quercetin bound to ESR1 (PDB ID: 3CBP [19], modeled with 243 amino acid residues) via key residues including TYR, ASN, ASP, TRP, and THR (Figure 4a). Apigenin interacted with EGFR (PDB ID: 3POZ [20], modeled with 293 amino acid residues) primarily through HIS, PHE, TYR, and GLY residues (Figure 4b). Kaempferol formed stable interactions with ESR1 (PDB ID: 3CBP, modeled with 243 amino acid residues), involving TYR, ARG, GLY, and SER (Figure 4c). Luteolin showed strong binding to NQO1 (PDB ID: 8OK0 [21], modeled with 1084 amino acid residues) through LYS, ASN, ARG, and GLU residues (Figure 4d). Finally, taraxasterol interacted with CASP3 (PDB ID: 2J32 [22], modeled with 246 amino acid residues), with PHE and TRP identified as key binding residues (Figure 4e). Collectively, these results suggest that all five active compounds possess strong binding affinities toward their respective core target proteins, supporting their potential roles in modulating LC-related signaling pathways.

2.3. MD Simulation Results

To further evaluate the binding stability of the docked complexes, 100 ns MD simulations were performed for five ligand–target pairs. As shown in Figure 5a, the complex of quercetin with ESR1 exhibited excellent binding stability. The root mean square deviation (RMSD) of the protein backbone fluctuated within the range of 0.20–0.25 Å, indicating a stable protein conformation. The RMSD of the ligand remained between 0.05 and 0.10 Å throughout the simulation, suggesting that quercetin maintained a stable position within the binding site. The radius of gyration (Rg) of the complex stabilized after 20 ns, fluctuating between 2.0 and 2.2 nm, which indicates a compact and stable complex structure. Figure 5b illustrates that the apigenin–EGFR complex also demonstrated favorable binding stability. The RMSD of the protein backbone stabilized after 10 ns, ranging from 0.22 to 0.26 Å, confirming structural stability. The ligand RMSD remained within 0.06–0.08 Å after 10 ns with minimal fluctuation, indicating a consistently stable binding conformation. The Rg value stabilized after 5 ns and fluctuated slightly between 1.98 and 2.00 nm without significant trends, suggesting that the complex structure remained stable. As shown in Figure 5c, the kaempferol–ESR1 complex exhibited moderate binding stability. During the first 20 ns, the protein backbone RMSD reflected a conformational adjustment from the initial state. From 20 to 70 ns, the RMSD remained in the range of 0.15–0.18 Å, indicating dynamic equilibrium. After 70 ns, fluctuations further decreased, demonstrating improved stability. The ligand RMSD remained stable between 0.05 and 0.07 Å during 10–70 ns, indicating reliable positioning within the binding pocket. Although there was a slight increase beyond 70 ns, it remained between 0.05 and 0.08 Å. The Rg value stabilized after 10 ns and fluctuated within 1.94–1.96 nm. Figure 5d shows that the luteolin–NQO1 complex exhibited good structural stability. The protein backbone RMSD stabilized after 20 ns, with fluctuations between 0.20 and 0.30 Å. The ligand RMSD remained stable in the range of 0.06–0.12 Å after 10 ns. The Rg value of the complex also stabilized after 20 ns, fluctuating between 1.96 and 1.99 nm. As shown in Figure 5e, the taraxasterol–CASP3 complex displayed relatively stable binding characteristics. The RMSD of the protein backbone fluctuated between 0.20 and 0.25 Å from 20 to 60 ns, indicating that the system approached equilibrium. After 60 ns, the RMSD increased slightly to the range of 0.28–0.30 Å, reflecting structural adaptation followed by stabilization. The ligand RMSD remained stable between 0.15 and 0.20 Å after 10 ns. Although some fluctuation in Rg was observed, it consistently remained within the range of 2.5–3.5 nm, suggesting the overall stability of the complex. Taken together, these MD simulation results demonstrated strong and stable interactions between quercetin–ESR1, apigenin–EGFR, kaempferol–ESR1, luteolin–NQO1, and taraxasterol–CASP3. The findings provide theoretical support for the stable binding behavior of these active compounds at the molecular level and lay the groundwork for further mechanistic investigations.

2.4. In Vitro Evaluation of Anticancer Activity

The X-control group of taraxasterol (i.e., the solvent control group) and the pure culture medium control group exhibited no inhibitory effect on either A549 or L929 cells, indicating that the observed cell death was not induced by the solvent DMSO. As expected, the viability of A549 cells progressively decreased with increasing taraxasterol concentration and treatment duration, with the most significant inhibitory effect observed at 25   μ mol/L (Figure 6a). At the same concentration, taraxasterol did not exhibit significant cytotoxicity toward normal L929 cells, with cell viability remaining at or above 80% (Figure 6b). These findings further suggest that taraxasterol may exert selective antitumor activity.

2.5. SEM Analysis

To further verify the inhibitory effect of taraxasterol on A549 cells, SEM was performed following treatment with 25   μ mol/L taraxasterol on both A549 and L929 cells. As shown in Figure 7a, A549 cells exhibited significant morphological alterations after taraxasterol treatment. The degree of cell damage progressively increased with prolonged exposure. The cell surface integrity was severely disrupted, accompanied by marked cell shrinkage, increased membrane depressions, and the release of cellular debris due to partial cell rupture. These morphological features provide important visual evidence supporting the inhibitory effect of taraxasterol and offer insights into its potential mechanism of action. In contrast, as shown in Figure 7b, L929 cells treated with 25   μ mol/L taraxasterol for 24 and 48 h displayed relatively intact cellular morphology. No typical features of apoptosis or necrosis were observed. Only mild surface indentations and reduced pseudopodia were noted, which may be attributed to transient cellular stress responses. These findings indicate that L929 cells exhibit good tolerance to taraxasterol at this concentration, thereby providing further morphological evidence of its selective cytotoxicity.

3. Discussion

In recent years, various traditional Chinese medicines (TCMs) have demonstrated promising potential in the treatment of LC. For example, Hedyotis diffusa [23,24] and Scutellaria barbata [25] are widely used in clinical practice due to their traditional functions of clearing heat and detoxifying toxins. Similarly, single herbal medicines such as Sophora flavescens [26], Panax ginseng [27], and Tripterygium wilfordii [28], along with their active components, have also exhibited significant antitumor effects in modern pharmacological studies. Compared to these TCMs, dandelion presents unique advantages in LC research. As a medicinal and edible herb, dandelion is considered safe and suitable for long-term use. It is rich in bioactive compounds, including flavonoids, chlorogenic acid, and ursolic acid, which can regulate key biological processes involved in LC, such as cell proliferation, apoptosis, migration, and immune response, through multi-target and multi-pathway mechanisms [29,30]. Notably, although dandelion has traditionally been used in Chinese medicine for “clearing heat and detoxifying” [31], its antitumor pharmacological mechanisms are still under investigation, highlighting its considerable research value and translational potential.
In this study, we systematically investigated the potential mechanisms of dandelion in the treatment of LC by integrating network pharmacology, molecular docking, MD simulations [32,33], and in vitro validation of taraxasterol. These approaches are well suited to elucidate the complex interactions between LC-related targets and the major bioactive compounds in dandelion. Similar strategies have recently been employed in multiple studies to explore the mechanisms of TCMs in LC therapy. Initially, we identified 49 core LC-related targets of dandelion. Network pharmacology analysis was then performed to determine the key active compounds and their corresponding target networks. Subsequently, PPI analysis, GO, and KEGG enrichment analyses were conducted to further characterize the overlapping targets. The results indicated that the anti-LC effects of dandelion involve multiple biological processes and signaling pathways, including inflammatory responses, negative regulation of cell population proliferation, responses to external stimuli, and cell cycle regulation. Based on further analysis of network topology, six hub genes—TP53, CASP3, EGFR, AKT1, ESR1, and NQO1—were identified as potential key targets underlying the anti-lung cancer effects of dandelion. Among these, ESR1 is implicated in hormone-dependent tumorigenesis [34]; EGFR is a well-characterized driver gene and therapeutic target in NSCLC [35]; NQO1 plays an essential role in maintaining redox homeostasis and modulating oxidative stress in lung tissues [36]; and CASP3, as a central executioner caspase, is crucial for the initiation of apoptosis and is involved in tumor suppression [37]. Molecular docking and MD simulations revealed strong binding affinities between several major dandelion compounds—such as quercetin, apigenin, kaempferol, and luteolin—and the core LC-related proteins. Additionally, taraxasterol, a triterpenoid compound uniquely found in dandelion, was selected for further experimental validation in vitro. Cell viability assays showed that taraxasterol had no statistically significant cytotoxic effect on A549 cells at concentrations between 25 and 50   μ mol/L. To minimize potential non-specific toxicity at high concentrations, 25   μ mol/L was selected as the optimal treatment concentration for subsequent SEM analysis. At this concentration, taraxasterol exhibited low toxicity to normal L929 cells (viability > 80%), indicating selective inhibitory activity against tumor cells.
Taken together, we propose that dandelion may exert anti-LC effects through synergistic regulation of multiple components and targets, ultimately modulating the expression and function of key genes. The active compounds identified in this study—particularly taraxasterol, whose effects were experimentally validated—provide a reliable theoretical and experimental foundation for further development of dandelion as a candidate agent for lung cancer therapy. However, additional in vivo experiments and pharmacokinetic studies are needed to fully evaluate its potential for clinical application.

4. Materials and Methods

4.1. Reagents and Materials

Materials: Taraxacum mongolicum (collected from Bozhou, Anhui Province, production batch number: 230801) and taraxasterol (purchased from Chengdu Yirei Biotechnology Co., Ltd., Chengdu, Sichuan, China, batch number: YRP003-230901) were used in this study. Cell culture dishes, 96-well plates, and 6-well plates were obtained from NEST Biotechnology. Trypsin digestion solution, fetal bovine serum (FBS), and Type I collagen were purchased from Solarbio (Beijing, China). High-glucose DMEM and phosphate-buffered saline (PBS) were obtained from Gibco (Grand Island, NY, USA). Penicillin–streptomycin solution was purchased from Servicebio (Wuhan, Hubei, China). Glutaraldehyde was provided by Fisher Scientific, and silicon wafers were supplied by Siwafer (Quzhou, Zhejiang, China). Human lung cancer cells (A549) and mouse fibroblast epithelial cells (L929) were kindly provided by Tianjin Cancer Hospital.
Instruments: An electronic analytical balance (model AR124CN, Lei Tai Instruments, Beijing, China), a high-resolution scanning electron microscope (Verios 460L, Thermo Scientific, Waltham, MA, USA), a microplate reader (Thermo Scientific, USA), and a CO 2 incubator (Thermo Scientific, USA) were used in the experiments.

4.2. Experimental Procedures

4.2.1. Screening of Active Compounds in Dandelion and Target Prediction

Based on the literature and modern quality control strategies for traditional Chinese medicine (TCM) [38] as outlined in the Pharmacopoeia of China (Part I), compounds from dandelion were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https://www.tcmsp-e.com/, accessed on 3 July 2024) with the criteria of OB ≥ 30% and DL ≥ 0.18 [39]. Active components meeting these thresholds were identified, and their corresponding potential targets were retrieved from the TCMSP database.
Additionally, disease-related targets associated with lung cancer were obtained from the GeneCards https://www.genecards.org/ (accessed on 10 July 2024) and UniProt https://www.uniprot.org/ (accessed on 10 July 2024) databases. After intersecting the compound-related and disease-related targets, key targets of dandelion against lung cancer were identified. Cytoscape 3.8.2 software was used to construct network diagrams of “TCM–compound” and “compound–target” relationships. Network topology analysis was performed based on degree centrality, with node size reflecting degree value, to identify the most relevant active compounds.

4.2.2. PPI Network Analysis

The common targets between dandelion and LC were identified using the Bioinformatics platform https://www.bioinformatics.com.cn (accessed on 6 August 2024). A PPI network of these intersecting targets was constructed using the STRING database https://string-db.org/ (accessed on 6 August 2024), with the organism set to “Homo sapiens” and a minimum required interaction score of 0.7 (high confidence). The resulting PPI network revealed the potential interactions among the target proteins for subsequent topological analysis.

4.2.3. GO and KEGG Enrichment Analysis

GO and KEGG enrichment analyses were performed using the Metascape database https://metascape.org/ (accessed on 7 August 2024). The intersection targets between dandelion and lung cancer were imported into Metascape, and enriched terms were identified using a significance threshold of p < 0.05. GO analysis covered three categories: biological process (BP), molecular function (MF), and cellular component (CC).

4.2.4. Molecular Docking

Small-molecule ligands were obtained from the PubChem database https://pubchem.ncbi.nlm.nih.gov/ (accessed on 20 August 2024), and the corresponding protein receptor structures were retrieved from the RCSB Protein Data Bank (PDB) https://www.rcsb.org/ (accessed on 20 August 2024). The molecular geometry of each ligand was optimized using Chem3D software (version 15.1). Water molecules and native ligands were removed from the protein structures using PyMOL software (version 2.3.4). Hydrogen atoms were added to both proteins and ligands using AutoDock Tools (version 1.5.6), and the binding pockets of the target proteins were identified to define the docking grid boxes. Molecular docking was then performed using AutoDock Vina (version 1.1.2) to evaluate the binding affinities between key active compounds and core target proteins. Finally, the docking results were visualized, and the specific binding sites were identified using Discovery Studio software (version 2019).

4.2.5. Molecular Dynamics Simulation

To evaluate the binding stability of the protein–ligand complexes obtained from molecular docking, MD simulations were performed using the AMBER99SB force field implemented in the GROMACS software package (version 2020.6). The Single Point Charge Extended (SPC/E) three-site water model was employed to simulate the aqueous environment. Sodium ( Na + ) and chloride ( Cl ) ions were added to neutralize the simulation system. After energy minimization, the system was gradually heated to 300 K. Equilibration was carried out under constant volume (NVT ensemble) and constant pressure (NPT ensemble) conditions. Subsequently, a 100 ns MD simulation was conducted under periodic boundary conditions at constant temperature and pressure. Trajectory data were recorded every 1.0 picosecond (ps). Root mean square deviation (RMSD), radius of gyration (Rg), and root mean square fluctuation (RMSF) analyses were performed to assess the conformational stability and dynamic behavior of the complexes. In addition, energy profiles were evaluated throughout the simulation to further validate system stability.

4.2.6. Cell Viability Assay

Taraxasterol powder (1 mg) was precisely weighed and dissolved in dimethyl sulfoxide (DMSO) to prepare a 100 mM stock solution, which was stored at –20 °C until use. Culture medium without any treatment was used as the negative control, and a DMSO control group (X group, containing 0.005% DMSO in medium) was included. Taraxasterol was applied to cells at final concentrations of 0, 15, 20, 25, 30, 35, 40, 45, and 50   μ mol/L. Exponentially growing A549 and L929 cells were diluted with DMEM to a concentration of 6 × 10 4 cells/mL and seeded into 96-well plates at 100   μ L per well. For each concentration, four replicate wells were set up. After incubation for 24 h, the cells were treated with the respective concentrations of taraxasterol and incubated for an additional 24 and 48 h. At the end of each treatment period, 10% MTT solution was added to each well and incubated for 4 h. Afterward, the supernatant was removed and replaced with 150   μ L of DMSO to dissolve the formazan crystals. Absorbance was measured using a microplate reader, and cell viability was calculated using the following Formula (1). The cell viability curves were plotted using GraphPad Prism software (version 9.0.0).
Cell viability = OD test OD blank / OD control OD blank × 100 %

4.2.7. SEM Experiment

Silicon wafers were cut into small pieces of approximately 3–5 mm in size and thoroughly rinsed with anhydrous ethanol. The wafers were then sterilized at 120 °C for 20 min. After sterilization, they were placed in culture dishes and exposed to ultraviolet (UV) light in a biosafety cabinet for 12 h to ensure complete sterility. The sterilized wafers were coated with Type I collagen solution (0.01 mg/mL) and incubated at 4 °C for 12 h. Subsequently, excess collagen was removed by rinsing twice with phosphate-buffered saline (PBS), and the wafers were air-dried before being transferred into 6-well plates for further use. A549 and L929 cells were treated with taraxasterol at a final concentration of 25   μ mol/L, with untreated cells in pure medium serving as the control. Log-phase A549 and L929 cells were diluted to a density of 6 × 10 4 cells/mL in DMEM and seeded onto the prepared silicon wafers in 6-well plates. After a 24-h incubation, taraxasterol was added, and the cells were further incubated for 24 and 48 h. Following treatment, the silicon wafers were gently rinsed with PBS and fixed by adding 2.5% glutaraldehyde solution directly onto the wafer surface. The samples were stored at 4 °C for 2 h for fixation, then washed with PBS to remove residual fixative. Samples were dehydrated using a graded ethanol series (30%, 50%, 70%, 90%, and 100%), with each step lasting 20 min. The 100% ethanol step was repeated three times. Finally, the samples were sputter-coated with gold to enhance conductivity and then observed under an SEM for morphological analysis.

5. Conclusions

This study explored the interaction between active compounds of dandelion and LC-related targets through network pharmacology analysis, molecular docking, and molecular dynamics simulations. Our research predicted the binding of key bioactive compounds from dandelion with several important LC-related targets, such as TP53, CASP3, and EGFR. Based on these analyses, we hypothesize that dandelion may exert its anticancer effects by modulating key biological processes, including tumor cell proliferation and apoptosis. The molecular docking and molecular dynamics simulation results further support the stable binding of these compounds to lung cancer targets, suggesting the potential of dandelion in lung cancer treatment.
In vitro cell assays and scanning electron microscopy (SEM) analysis confirmed that taraxasterol, a key compound of dandelion, exhibited a strong inhibitory effect on A549 lung cancer cells while showing no significant cytotoxicity to normal L929 cells. These findings provide strong experimental support for taraxasterol as a candidate compound for lung cancer therapy.
Although this study reveals the potential of dandelion in anticancer therapy, further animal experiments and clinical studies are necessary to validate its therapeutic efficacy. Additionally, future research could include comparisons with standard anticancer drugs to provide a more comprehensive evaluation of the efficacy and safety of dandelion.
In conclusion, this study provides a theoretical basis for the development of taraxasterol as a new candidate drug for lung cancer treatment and offers new insights and methods for studying the multi-target mechanisms of natural products in cancer therapy.

Author Contributions

Conceptualization, J.L. (Jianing Liu) and H.Y.; methodology, J.L. (Jianing Liu), R.L., D.S. and Y.L.; software, J.L. (Jianing Liu); validation, H.Y., R.L. and D.S.; formal analysis, J.L. (Jianing Liu) and J.L. (Jing Lu); investigation, H.Y., R.L. and Y.L.; resources, J.L. (Jinbiao Liu); data curation, J.L. (Jianing Liu); writing—original draft preparation, J.L. (Jianing Liu); writing—review and editing, J.L. (Jinbiao Liu); visualization, Y.L.; supervision, J.L. (Jing Lu) and J.L. (Junrui Lu); project administration, D.S., J.L. (Junrui Lu), J.L. (Jing Lu) and J.L. (Jinbiao Liu); funding acquisition, J.L. (Jinbiao Liu). All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (42077212).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained in the paper.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Abbreviations

The following abbreviations are used in this manuscript:
A549Human Lung Adenocarcinoma Cell Line
ARGArginine
ASNAsparagine
BPBiological Process
CASP3Cysteine-aspartic Protease 3
CCCellular Component
DLDrug-likeness
EGFREpidermal Growth Factor Receptor
ESR1Estrogen Receptor 1
GLUGlutamic Acid
GLYGlycine
GOGene Ontology
HISHistidine
KEGGKyoto Encyclopedia of Genes and Genomes
L929Mouse Fibroblast Cell Line
LYSLysine
MDMolecular Dynamics
MFMolecular Function
MTT3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide
NPTIsothermal Isobaric Ensemble
NQO1NAD(P)H Quinone Dehydrogenase 1
NVTCanonical Ensemble
OBOral Bioavailability
ODOptical Density
PHEPhenylalanine
PPIProtein–Protein Interaction
RgGyration Analysis
RMSDRoot Mean Square Deviation
SEMScanning Electron Microscopy
SERSerine
SPC/ESingle Point Charge Extended
TCMTraditional Chinese Medicine
TCMSPTraditional Chinese Medicine Systems Pharmacology Database and Analysis Platform
TP53Tumor Protein p53
TRPTryptophan
TYRTyrosine
UVUltraviolet

References

  1. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef] [PubMed]
  2. Sandler, A.B.; Buzaid, A.C. Lung cancer: A review of current therapeutic modalities. Lung 1992, 170, 249–265. [Google Scholar] [CrossRef] [PubMed]
  3. Huang, M.; Lu, J.-J.; Ding, J. Natural products in cancer therapy: Past, present and future. Nat. Prod. Bioprospect. 2021, 11, 5–13. [Google Scholar] [CrossRef] [PubMed]
  4. Ali Abdalla, Y.O.; Subramaniam, B.; Nyamathulla, S.; Shamsuddin, N.; Arshad, N.M.; Mun, K.S.; Awang, K.; Nagoor, N.H. Natural products for cancer therapy: A review of their mechanism of actions and toxicity in the past decade. J. Trop. Med. 2022, 2022, 5794350. [Google Scholar] [CrossRef]
  5. Sigstedt, S.C.; Hooten, C.J.; Callewaert, M.C.; Jenkins, A.R.; Romero, A.E.; Pullin, M.J.; Steelant, W.F. Evaluation of aqueous extracts of Taraxacum officinale on growth and invasion of breast and prostate cancer cells. Int. J. Oncol. 2008, 32, 1085–1090. [Google Scholar] [CrossRef]
  6. Choi, U.-K.; Lee, O.-H.; Yim, J.H.; Cho, C.-W.; Rhee, Y.K.; Lim, S.-I.; Kim, Y.-C. Hypolipidemic and antioxidant effects of dandelion (Taraxacum officinale) root and leaf on cholesterol-fed rabbits. Int. J. Mol. Sci. 2010, 11, 67–78. [Google Scholar] [CrossRef]
  7. Li, X.H.; He, X.R.; Zhou, Y.Y.; Yang, L.; Zeng, S.; Sun, Y.H.; Chen, W.S. Taraxacum mongolicum extract induced endoplasmic reticulum stress-associated apoptosis in triple-negative breast cancer cells. J. Ethnopharmacol. 2017, 206, 55–64. [Google Scholar] [CrossRef]
  8. Trinh, N.V.; Doan-Phuong Dang, N.; Hong Tran, D.; Nguyen, T.T.; Tran, L.T.; Nguyen, T.H. Taraxacum officinale dandelion extracts efficiently inhibited the breast cancer stem cell proliferation. Biomed. Res. Ther. 2016, 3, 733–741. [Google Scholar] [CrossRef]
  9. Yan, S.; Yin, L.; Dong, R. Inhibition of IEC-6 cell proliferation and the mechanism of ulcerative colitis in C57BL/6 mice by dandelion root polysaccharides. Foods 2023, 12, 3800. [Google Scholar] [CrossRef]
  10. Wang, S.-H.; Wu, C.-H.; Tsai, C.-C.; Chen, T.-Y.; Tsai, K.-J.; Hung, C.-M.; Hsu, C.-Y.; Wu, C.-W.; Hsieh, T.-H. Effects of luteolin on human breast cancer using gene expression array: Inferring novel genes. Curr. Issues Mol. Biol. 2022, 44, 2107–2121. [Google Scholar] [CrossRef]
  11. Liang, X.-L.; Ji, M.-M.; Chen, L.; Liao, Y.; Kong, X.-Q.; Xu, X.-Q.; Liao, Z.-G.; Wilson, D.W. Traditional Chinese herbal medicine Astragalus Radix and its effects on intestinal absorption of aconite alkaloids in rats. Chin. Herb. Med. 2021, 13, 235–242. [Google Scholar] [CrossRef] [PubMed]
  12. Hopkins, A. Network pharmacology: The next paradigm in drug discovery. Nat. Chem. Biol. 2008, 4, 682–690. [Google Scholar] [CrossRef] [PubMed]
  13. Fan, J.; Fu, A.; Zhang, L. Progress in molecular docking. Quant. Biol. 2019, 7, 83–89. [Google Scholar] [CrossRef]
  14. Pagadala, N.S.; Syed, K.; Tuszynski, J. Software for molecular docking: A review. Biophys. Rev. 2017, 9, 91–102. [Google Scholar] [CrossRef]
  15. Hollingsworth, S.A.; Dror, R.O. Molecular dynamics simulation for all. Neuron 2018, 99, 1129–1143. [Google Scholar] [CrossRef]
  16. Karplus, M.; Petsko, G.A. Molecular dynamics simulations in biology. Nature 1990, 347, 631–639. [Google Scholar] [CrossRef] [PubMed]
  17. Nwachukwu, I.D.; Sarteshnizi, R.A.; Udenigwe, C.C.; Aluko, R.E. A concise review of current in vitro chemical and cell-based antioxidant assay methods. Molecules 2021, 26, 4865. [Google Scholar] [CrossRef]
  18. Passey, S.; Pellegrin, S.; Mellor, H. Scanning electron microscopy of cell surface morphology. Curr. Protoc. Cell Biol. 2007, 37, 4.17.1–4.17.13. [Google Scholar] [CrossRef]
  19. Subramanian, K.; Jia, D.; Kapoor-Vazirani, P.; Powell, D.R.; Collins, R.E.; Sharma, D.; Peng, J.; Cheng, X.; Vertino, P.M. Regulation of estrogen receptor alpha by the SET7 lysine methyltransferase. Mol. Cell 2008, 30, 336–347. [Google Scholar] [CrossRef] [PubMed]
  20. Aertgeerts, K.; Skene, R.; Yano, J.; Sang, B.C.; Zou, H.; Snell, G.; Jennings, A.; Iwamoto, K.; Habuka, N.; Hirokawa, A.; et al. Structural Analysis of the Mechanism of Inhibition and Allosteric Activation of the Kinase Domain of HER2 Protein. J. Biol. Chem. 2011, 286, 18756–18765. [Google Scholar] [CrossRef] [PubMed]
  21. Grieco, A.; Ruiz-Fresneda, M.A.; Gomez-Mulas, A.; Pacheco-Garcia, J.L.; Quereda-Moraleda, I.; Pey, A.L.; Martin-Garcia, J.M. Structural Dynamics at the Active Site of the Cancer-Associated Flavoenzyme NQO1 Probed by Chemical Modification with PMSF. FEBS Lett. 2023, 597, 2687–2698. [Google Scholar] [CrossRef] [PubMed]
  22. Feeney, B.; Pop, C.; Swartz, P.; Mattos, C.; Clark, A.C. Role of Loop Bundle Hydrogen Bonds in the Maturation and Activity of (Pro)Caspase-3. Biochemistry 2006, 45, 13249–13257. [Google Scholar] [CrossRef] [PubMed]
  23. Lv, Y.; Wang, Y. Chemical constituents from Oldenlandia diffusa and their cytotoxic effects on human cancer cell lines. Nat. Prod. Res. 2021, 37, 397–403. [Google Scholar] [CrossRef]
  24. Wang, S.; Yin, N.; Li, Y.; Ma, Z.; Lin, W.; Zhang, L.; Cui, Y.; Xia, J.; Geng, L. Molecular mechanism of the treatment of lung adenocarcinoma by Hedyotis diffusa: An integrative study with real-world clinical data and experimental validation. Front. Pharmacol. 2024, 15, 1355531. [Google Scholar] [CrossRef] [PubMed]
  25. Yin, X.; Zhou, J.; Jie, C.; Xing, D.; Zhang, Y. Anticancer activity and mechanism of Scutellaria barbata extract on human lung cancer cell line A549. Life Sci. 2004, 75, 2233–2244. [Google Scholar] [CrossRef]
  26. Chen, H.; Yang, J.; Hao, J.; Lv, Y.; Chen, L.; Lin, Q.; Yuan, J.; Yang, X. A novel flavonoid kushenol Z from Sophora flavescens mediates mTOR pathway by inhibiting phosphodiesterase and Akt activity to induce apoptosis in non-small-cell lung cancer cells. Molecules 2019, 24, 4425. [Google Scholar] [CrossRef] [PubMed]
  27. Li, Z.-H.; Yu, D.; Huang, N.-N.; Wu, J.-K.; Du, X.-W.; Wang, X.-J. Immunoregulatory mechanism studies of ginseng leaves on lung cancer based on network pharmacology and molecular docking. Sci. Rep. 2021, 11, 18201. [Google Scholar] [CrossRef]
  28. Ni, X.; Jiang, X.; Yu, S.; Wu, F.; Zhou, J.; Mao, D.; Wang, H.; Liu, Y.; Jin, F. Triptonodiol, a diterpenoid extracted from Tripterygium wilfordii, inhibits the migration and invasion of non-small-cell lung cancer. Molecules 2023, 28, 4708. [Google Scholar] [CrossRef]
  29. Man, J.; Wu, L.; Han, P.; Hao, Y.; Li, J.; Gao, Z.; Wang, J.; Yang, W.; Tian, Y. Revealing the metabolic mechanism of dandelion extract against A549 cells using UPLC-QTOF MS. Biomed. Chromatogr. 2022, 36, e5272. [Google Scholar] [CrossRef]
  30. Kang, L.; Miao, M.-S.; Song, Y.-G.; Fang, X.-Y.; Zhang, J.; Zhang, Y.-N.; Miao, J.-X. Total flavonoids of Taraxacum mongolicum inhibit non-small cell lung cancer by regulating immune function. J. Ethnopharmacol. 2021, 281, 114514. [Google Scholar] [CrossRef]
  31. Fan, M.; Zhang, X.; Song, H.; Zhang, Y. Dandelion (Taraxacum genus): A review of chemical constituents and pharmacological effects. Molecules 2023, 28, 5022. [Google Scholar] [CrossRef] [PubMed]
  32. Banu, H.; Al-Shammari, E.; Shahanawaz, S.; Azam, F.; Patel, M.; Alarifi, N.A.; Ahmad, M.F.; Adnan, M.; Ashraf, S.A. Insights into the therapeutic targets and molecular mechanisms of Eruca sativa against colorectal cancer: An integrated approach combining network pharmacology, molecular docking and dynamics simulation. Pharmaceuticals 2025, 18, 453. [Google Scholar] [CrossRef] [PubMed]
  33. OréMaldonado, K.A.; Cuesta, S.A.; Mora, J.R.; Loroño, M.A.; Paz, J.L. Discovering new tyrosinase inhibitors by using in silico modelling, molecular docking, and molecular dynamics. Pharmaceuticals 2025, 18, 418. [Google Scholar] [CrossRef] [PubMed]
  34. Stabile, L.P.; Davis, A.L.G.; Gubish, C.T.; Hopkins, T.M.; Luketich, J.D.; Christie, N.; Finkelstein, S.; Siegfried, J.M. Human Non-Small Cell Lung Tumors and Cells Derived from Normal Lung Express Both Estrogen Receptor α and β and Show Biological Responses to Estrogen. Cancer Res. 2002, 62, 2141–2150. [Google Scholar] [PubMed]
  35. Meador, C.B.; Micheel, C.M.; Levy, M.A.; Lovly, C.M.; Horn, L.; Warner, J.L.; Johnson, D.B.; Zhao, Z.; Anderson, I.A.; Sosman, J.A.; et al. Beyond Histology: Translating Tumor Genotypes into Clinically Effective Targeted Therapies. Clin. Cancer Res. 2014, 20, 2264–2275. [Google Scholar] [CrossRef]
  36. Chao, C.; Zhang, Z.-F.; Berthiller, J.; Boffetta, P.; Hashibe, M. NAD(P)H:Quinone Oxidoreductase 1 (NQO1) Pro187Ser Polymorphism and the Risk of Lung, Bladder, and Colorectal Cancers: A Meta-analysis. Cancer Epidemiol. Biomarkers Prev. 2006, 15, 979–987. [Google Scholar] [CrossRef] [PubMed]
  37. Porter, A.; Jänicke, R. Emerging roles of caspase-3 in apoptosis. Cell Death Differ. 1999, 6, 99–104. [Google Scholar] [CrossRef]
  38. Ru, J.; Li, P.; Wang, J.; Zhou, W.; Li, B.; Huang, C.; Li, P.; Guo, Z.; Tao, W.; Yang, Y.; et al. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform. 2014, 6, 13. [Google Scholar] [CrossRef]
  39. Xu, X.; Zhang, W.; Huang, C.; Li, Y.; Yu, H.; Wang, Y.; Duan, J.; Ling, Y. A novel chemometric method for the prediction of human oral bioavailability. Int. J. Mol. Sci. 2012, 13, 6964–6982. [Google Scholar] [CrossRef]
Figure 1. Network pharmacology analysis. (a) Herb–compound network. (b) Compound–target interaction network. (c) LC-target network.
Figure 1. Network pharmacology analysis. (a) Herb–compound network. (b) Compound–target interaction network. (c) LC-target network.
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Figure 2. Chemical structures of key compounds.
Figure 2. Chemical structures of key compounds.
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Figure 3. Network-based functional enrichment and interaction analysis. (a) Overlapping targets between dandelion and LC. (b) PPI network of the overlapping targets. (c) GO enrichment analysis of overlapping targets. (d) KEGG pathway enrichment analysis of overlapping targets.
Figure 3. Network-based functional enrichment and interaction analysis. (a) Overlapping targets between dandelion and LC. (b) PPI network of the overlapping targets. (c) GO enrichment analysis of overlapping targets. (d) KEGG pathway enrichment analysis of overlapping targets.
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Figure 4. The docking map of compound–target interactions: (a) The docking map of quercetin–ESR1 interaction, with a binding energy of −8.9 kcal/mol. (b) The docking map of apigenin–EGFR interaction, with a binding energy of −7.1 kcal/mol. (c) The docking map of kaempferol–ESR1 interaction, with a binding energy of −9.0 kcal/mol. (d) The docking map of luteolin–NQO1 interaction, with a binding energy of −8.3 kcal/mol. (e) The docking map of taraxasterol–CASP3 interaction, with a binding energy of −6.8 kcal/mol.
Figure 4. The docking map of compound–target interactions: (a) The docking map of quercetin–ESR1 interaction, with a binding energy of −8.9 kcal/mol. (b) The docking map of apigenin–EGFR interaction, with a binding energy of −7.1 kcal/mol. (c) The docking map of kaempferol–ESR1 interaction, with a binding energy of −9.0 kcal/mol. (d) The docking map of luteolin–NQO1 interaction, with a binding energy of −8.3 kcal/mol. (e) The docking map of taraxasterol–CASP3 interaction, with a binding energy of −6.8 kcal/mol.
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Figure 5. MD simulation results of five ligand–protein complexes over a 100 ns trajectory. (a) Quercetin–ESR1: Exhibits excellent binding stability. (b) Apigenin–EGFR: Exhibits excellent binding stability. (c) Kaempferol–ESR1: Demonstrates good binding stability. (d) Luteolin–NQO1: Exhibits strong interaction strength. (e) Taraxasterol–CASP3: Shows favorable binding stability.
Figure 5. MD simulation results of five ligand–protein complexes over a 100 ns trajectory. (a) Quercetin–ESR1: Exhibits excellent binding stability. (b) Apigenin–EGFR: Exhibits excellent binding stability. (c) Kaempferol–ESR1: Demonstrates good binding stability. (d) Luteolin–NQO1: Exhibits strong interaction strength. (e) Taraxasterol–CASP3: Shows favorable binding stability.
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Figure 6. In vitro cytotoxicity evaluation of taraxasterol. (a) Treatment of A549 lung cancer cells with taraxasterol at 25   μ mol/L for 48 h reduced cell viability to 56.26%. (b) Taraxasterol treatment showed no significant inhibitory effect on L929 normal cells. Data are presented as mean ± SD (N = 4).
Figure 6. In vitro cytotoxicity evaluation of taraxasterol. (a) Treatment of A549 lung cancer cells with taraxasterol at 25   μ mol/L for 48 h reduced cell viability to 56.26%. (b) Taraxasterol treatment showed no significant inhibitory effect on L929 normal cells. Data are presented as mean ± SD (N = 4).
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Figure 7. SEM analysis of the morphological effects of taraxasterol ( 25   μ mol/L) on A549 and L929 cells. (a) SEM images of A549 lung cancer cells treated with taraxasterol for 24 h and 48 h, compared with the untreated control group. Significant surface damage and morphological alterations were observed over time. (b) SEM images of L929 cells under the same treatment conditions. No notable cytotoxic effects were observed, and overall cell morphology remained intact.
Figure 7. SEM analysis of the morphological effects of taraxasterol ( 25   μ mol/L) on A549 and L929 cells. (a) SEM images of A549 lung cancer cells treated with taraxasterol for 24 h and 48 h, compared with the untreated control group. Significant surface damage and morphological alterations were observed over time. (b) SEM images of L929 cells under the same treatment conditions. No notable cytotoxic effects were observed, and overall cell morphology remained intact.
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Table 1. Binding energy data table for different compounds and targets.
Table 1. Binding energy data table for different compounds and targets.
Target ComponentBinding Energy (kcal/mol)
QuercetinApigeninKaempferolLuteolinTaraxasterol
TP53−8.2−7.7−7.6−7.9−7.7
CASP3−7.0−7.4−7.2−7.2−6.8
EGFR−6.9−7.1−6.7−7.5−7.5
AKT1−7.7−8.0−8.0−8.1−9.7
ESR1−8.9−8.5−9.0−9.0−8.1
NQO1−8.8−9.5−8.7−8.3−8.1
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Liu, J.; Yang, H.; Liu, R.; Sun, D.; Liu, Y.; Lu, J.; Liu, J.; Lu, J. Investigation of the Anti-Lung Cancer Mechanisms of Taraxacum officinale Based on Network Pharmacology and Multidimensional Experimental Validation. Pharmaceuticals 2025, 18, 663. https://doi.org/10.3390/ph18050663

AMA Style

Liu J, Yang H, Liu R, Sun D, Liu Y, Lu J, Liu J, Lu J. Investigation of the Anti-Lung Cancer Mechanisms of Taraxacum officinale Based on Network Pharmacology and Multidimensional Experimental Validation. Pharmaceuticals. 2025; 18(5):663. https://doi.org/10.3390/ph18050663

Chicago/Turabian Style

Liu, Jianing, Hailing Yang, Ran Liu, Dongjin Sun, Yongbao Liu, Jing Lu, Jinbiao Liu, and Junrui Lu. 2025. "Investigation of the Anti-Lung Cancer Mechanisms of Taraxacum officinale Based on Network Pharmacology and Multidimensional Experimental Validation" Pharmaceuticals 18, no. 5: 663. https://doi.org/10.3390/ph18050663

APA Style

Liu, J., Yang, H., Liu, R., Sun, D., Liu, Y., Lu, J., Liu, J., & Lu, J. (2025). Investigation of the Anti-Lung Cancer Mechanisms of Taraxacum officinale Based on Network Pharmacology and Multidimensional Experimental Validation. Pharmaceuticals, 18(5), 663. https://doi.org/10.3390/ph18050663

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