Combining network pharmacology and molecular docking to explore the pharmacological mechanism of Codonopsis Radix.-Hedysarum Multijugum Maxim.-Atractylodes Macrocephala Koidz. in treating lung cancer

Abstract

Aim

To explore the potential mechanism of the treatment of lung cancer (LC) with Codonopsis Radix.-Hedysarum Multijugum Maxim.-Atractylodes Macrocephala Koidz. (CHA).

Methods

The active ingredients and targets of CHA were obtained from TCMSP and SwissTargetPrediction databases. LC-related genes were obtained from MalaCards, GeneCards and DisGenNET databases. A protein–protein interaction network was constructed using STRING database, and analyzed with Cystoscape software. The core targets of CHA in LC treatment were determined by topological analysis, and functional annotation of these targets was performed via Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The binding ability of the target to the active ingredient was evaluated by molecular docking. CCK-8 assay, Transwell assay, qRT-PCR, Western blot and lung metastasis model in nude mice were used to evaluate the effects of CHA on the viability, migration and invasion of LC cells.

Results

27 active components and 411 common targets of CHA in lung cancer treatment were obtained. The common targets were associated multiple biological processes and pathways including PI3K-AKT pathway. 12-senecioyl-2E,8E,10E-atractylentriol, 14-acetyl-12-senecioyl-2E,8Z,10E-atractylentriol, astrapterocarpan, isoflavanone, (R)-Isomucronulatol were identified as the main bioactive components of CHA, and SRC, HSP90AA1, AKT1, EGFR, ESR1 were identified as core targets of CHA in LC treatment. The bioactive ingredients had good binding ability with the core targets. CHA significantly inhibited the viability, migration and invasion of LC cells, and also suppressed the transcription of core genes, and repressed the activation of PI3K/AKT pathway.

Conclusion

For LC, CHA exerts tumor-suppressive effects through various bioactive components, acting on multiple targets and signaling pathways.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12906-025-04823-z.

Introduction

Lung cancer (LC), a highly heterogeneous malignancy, has the highest mortality rate among human malignancies, with about 1.8 million deaths every year [1–3]. Despite the continuous development of radiotherapy, chemotherapy, targeted therapy and immunotherapy, most of the patients with advanced LC face treatment failure in the end. Therefore, it is of great significance to explore new therapeutic methods and drugs for LC patients.

Traditional Chinese medicine (TCM) has a history of more than 2,000 years, has characteristics of wide drug sources, low toxicity and few side effects [4]. In China, a variety of bioactive compounds extracted from herbal medicine have been explored to treat human malignancies, including terpenoids, flavonoids, polysaccharides, natural polyphenols and alkaloids [5–9]. In China, TCM is commonly used as a complementary therapy for chemotherapy and radiotherapy, to enhance the sensitivity of cancer cells to treatment, relieve side effects, and improve the quality of life of patients [10]. Codonopsis Radix (Dangshen), Hedysarum Multijugum Maxim.(Huangqi), Atractylodes Macrocephala Koidz (Baizhu) (CHA) are among the mostly widely used herbal medicines in TCM, and they are usually used together in TCM formulations with anti-cancer effects, such as Ze-Qi decoction [11], Kuan-Sin-Yin [12], Huangqi-Ezhu concentrated-granules [13], Shenqi Fuzheng injection [14, 15], Shenling Baizhu additive powder [16], and Feiyanning formula [17]. However, the molecular mechanism of their synergetic pharmacological effects in cancer treatment has not been fully elucidated.

Based on the interaction network of "drug—component—target—disease", network pharmacology combines many existing research results through database analysis to systematically study the network intervention mode and influence of drugs on disease; this approach has advantages over traditional single-target studies because it takes into account the complexity and redundancy in biological systems [18, 19]. In the study of TCM, because of the large number of natural compounds, it is impossible to simply make a one-to-one correspondence, while network pharmacology studies are more in line with the theory of TCM in treating diseases with multi-components, multi-targets and multi-pathways, and grasp the network relationship between different components as a whole [18, 19]. In addition, molecular docking technology is a method to simulate the interaction between ligand and receptor, predict the binding mode and action intensity between the two, so as to conduct the virtual screening of drug targets and predict the pharmacodynamic components [20]. The complexity of components in TCM leads to the diversity of its targets. Through the "multi-target-single ligand" docking model, based on space and energy matching, small molecular compounds and biological macromolecules identify each other to form molecular complexes in molecular docking, and in the study of pharmacodynamic substances of TCM, the application of molecular docking technology is relatively simple and can obtain more accurate results [20]. With network pharmacology, molecular docking and in vitro cell experiments, the present work was performed to clarify the potential mechanism of CHA in the treatment of LC (Fig. 1). To our best knowledge, this is the first study to explain the synergetic tumor-suppressive properties of the three common herbal medicines in treating LC.

Fig. 1.

Workflow of the study

Methods and materials

Prediction of active ingredients of CHA and targets

Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP) database (http://lsp.nwu.edu.cn/tcmsp.php) was searched to obtain the components of Codonopsis Radix, Hedysarum Multijugum Maxim., and Atractylodes Macrocephala Koidz, respectively. SwissADME (http://www.swissadme.ch/) database was searched for obtaining the parameters of drug absorption of active ingredients, distribution, metabolism, and elimination (ADME). The effective active ingredients of CHA was screened according to the following criteria: (1) gastrointestinal absorption (GI absorption) was "High"; (2) drug-likeness (DL) properties: 3 or more “Yes, 0 violation” in “Lipinski”, “Ghose”, “Veber”, “Egan”, “Muegge”; (3) Meet Lipinski's rule of five; (4) oral bioavailability (OB) ≥ 30%; (5) DL ≥ 0.18 [21, 22].

TCMSP database and PubChem database (https://pubchem.ncbi.nlm.nih.gov/) were used to obtain the bioactive ingredient in Canonical Simplified Molecular-Input Line-Entry System (SMILES) file. SwissTargetPrediction (http://www.swisstargetprediction.ch/index.php) database was applied to predict potential target proteins [23]. A Sankey diagram was used to show the relationships among herbal medicines, bioactive components and targets. The Sankey diagram was plotted by VisualParadigm (https://online.visual-paradigm.com/app/diagrams/#infoart:proj=0&type=SankeyDiagrams&gallery=/repository/71e9f744-7c0e-4439-bb3c-53960f910e89.xml&name=Sankey%20Diagram), an online platform for data analysis and visualization.

Acquisition of LC-related genes

With "Lung cancer" as the keyword, MalaCards database (https://www.malacards.org/), GeneCards database (https://www.genecards.org/) and DisGenNET database (https://www.disgenet.org/) were searched to obtain LC-related genes.

Protein–protein interaction (PPI) network construction and core target screening

A PPI network analysis was constructed using the STRING database (https://string-db.org/) [24, 25]. The drug-bioactive ingredient-target-disease network was visualized with Cytoscape 3.9.1. Degree Centrality (DC), Betweenness Centrality (BC) and Closeness Centrality (CC) were calculated by cytoNCA plug-in, and core targets were screened according to the threshold value of median.

Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis

R language and Metascape database (http://www.metascape.org/) [26] were applied for GO analysis and KEGG pathway enrichment analysis of 411 common targets, to explore the pivotal mechanism of CHA in LC treatment, including biological process (BP), cell composition (CC), molecular function (MF) and KEGG pathways.

Molecular docking

Protein Data Bank (PDB) files of target proteins were obtained from Uniprot database (https://www.uniprot.org/). From TCMSP database and PubChem database (https://pubchem.ncbi.nlm.nih.gov/), Mol2 files of the bioactive ingredients were downloaded. AutoDockTools 1.5.7 software was used to carry out for H₂O molecule removal, hydrogen addition, calculation of charge. Molecular docking was performed with Autodock Vain. 3D maps were drawn with PyMol software. 2D maps were drawn with LigPlus.

Analysis of the clinical significance of core targets in LC

The expression characteristics of the core targets in LC in the Cancer Genome Atlas Program (TCGA) cohort were analyzed with the University of ALabama at Birmingham CANcer data analysis Portal (UALCAN) database (https://ualcan.path.uab.edu/), and the relationship between the core target expression and the survival of the LC patients was analyzed with Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia.cancer-pku.cn/).

Drug preparation and cell culture

Codonopsis Radix, Hedysarum Multijugum Maxim., Atractylodes Macrocephala Koidz. were grinded into powder at a ratio of 1:1:1 and dissolved in distilled water at a concentration of 2 g/ml, then the mixture was centrifuged at 4000 rpm for 30 min. Next, the supernatant was collected and filtered with a filter membrane (0.22 µm; Millipore, Billerica, MA, USA) to obtain 1 g/ml of raw drug, which was stored at − 80 °C.

Human non-small lung cancer cell lines A549 and NCI-H1975 were purchased from American Type Culture Collection (ATCC; Manassas, VA, USA). The cells were cultured in Roswell Park Memorial Institute-1640 medium (Gibco, Rockville, MD, USA) supplemented with 10% fetal bovine serum (FBS), 100 U/ml penicillin, and 100 µg/ml streptomycin (Beyotime, Shanghai, China) at 37 °C in 5% CO₂.

Cell viability test

Cell viability was determined using cell counting kit-8 (CCK-8, Beyotime, Shanghai, China). In short, the cells were inoculated into 96-well culture plates at a density of 5 × 10³ cells/well. After incubation for 24 h, the cells were treated with CHA (100, 200, 400, 600, 800 μg/ml) for 24 h. After incubating the cells with 10 μl of CCK8 reagent at 37 °C for 1 h, the optical density at 450 nm was measured using a microplate reader. Half maximal inhibitory concentration (IC₅₀) values were calculated by Graphpad Prism 8.0 software on a nonlinear regression fitting method.

Migration and invasion test

Transwell assay was used to detect cell migration and invasion. The cells were re-suspended in serum-free medium, subsequently inoculated in the upper chamber of the Transwell system, and the complete medium containing 20% FBS was added to the lower chamber as a chemical attractant. For invasion tests, the filter of the Transwell system was coated with a layer of Matrigel (2.5 mg/ml, Sigma-Aldrich, St.Louis, MO, USA). After incubation at 37 °C for 24 h, the non-migrating cells in the upper chamber were carefully removed with a cotton swab, and the cells that crossed the membrane were fixed with methanol and stained with crystal violet solution. The cells were photographed under a vertical microscope (Nikon E400, Yurakucho, Tokyo, Japan) and counted in five randomly selected regions.

RNA extraction and real-time quantitative PCR

Total RNA was extracted from cells using TRIzol reagent (Invitrogen, Shanghai, China) according to manufacturer's instructions. RevertAid First Strand cDNA Synthesis Kit (Takara, Dalian, China) kit was used to reverse-transcribed RNA into cDNA. With the cDNA as the template, cDNA amplification was performed with SYBR green qPCR Supermix (Applied Biosystems, Foster City, CA, USA), on the Applied Biosystems® 7500 system (Life Technologies, USA). The primers were: SRC proto-oncogene, non-receptor tyrosine kinase.

(SRC): 5'-GGTCTATGTCGAGAGCTGGC-3' (forward) and 5'-ATGGGGGCAAGGAAGTGATG-3' (reverse); heat shock protein 90 alpha family class A member 1.

(HSP90AA1): 5'-TATAAGGCAGGCGCGGGGGT-3' (forward) and 5’- TGCACCAGCCTGCAAAGCTTCC-3' (reverse); AKT serine/threonine kinase 1.

(AKT1): 5'-GACGGGCACATTAAGATCAC-3' (forward) and 5 '-TGAGGATGAGCTCAAAAAGC-3' (reverse); epidermal growth factor receptor.

(EGFR): 5ʹ-AGGCACGAGTAACAAGCTCAC-3ʹ (forward) and 5ʹ-ATGAGGACATAACCAGCCACC-3ʹ (reverse); estrogen receptor 1.

(ESR1): 5ʹ- AGACTCGCTACTGTGCTGTGTG-3ʹ (forward) and 5ʹ- CCTGGCAACTCTTCCTCC-3ʹ (reverse); glyceraldehyde-3-phosphate dehydrogenase (GAPDH): 5ʹ-GAAGGTGAAGGTCGGAGTC-3ʹ (forward) and 5ʹ-GAAGATGGTGATGGGATTTC-3ʹ (reverse). All primers were synthesized by Shanghai Sangon Biotech Biological Co., Ltd (Shanghai, China). The 2^−∆∆Ct method was used to calculate the fold change of each gene, and the gene expression level was normalized by GAPDH.

Western blot

Total protein from AGS and SNU-16 cells was extracted using RIPA lysis buffer. Protein concentrations were quantified using a BCA assay kit (BioRad, Hercules, CA, USA). Equal amounts (30 µg) of proteins were separated using sodium dodecyl sulfate–polyacrylamide gel electrophoresis and transferred onto polyvinylidene difluoride membranes (Millipore, Bedford, MA, USA). After being closed with 5% skimmed milk for 1 h at room temperature, the membranes were incubated with primary antibodies: anti-p-AKT (T308) antibody (ab38449, 1:1000, Abcam), anti-AKT antibody (ab179463, 1:1000, Abcam), anti-p85 antibody (ab86714, 1: 1000, Abcam), anti-p-p85 (ab278545, 1:500, Abcam), and anti-GAPDH antibody (ab181602, 1:1000, Abcam) at 4 °C overnight. It was then incubated with HRP-coupled goat anti-rabbit secondary antibody (ab6721, 1:5000, Abcam) for 2 h. The protein signal of the membrane was detected using the ECL Plus assay kit (Pierce, Rockford, IL, USA). Protein expression was quantified using ImageLab software version 4.1 (Bio-Rad Laboratories, Hercules, CA, USA).

Animal assay

Animal experiments of this study followed the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines. The procedures of animal assays were approved by the Ethics Committee of the Xiangyang Central Hospital (Approval No. 2024037).

Male BALB/c nude mice (6 weeks old) were obtained from Shulaibao Co., Ltd (Wuhan, China). The mice were divided into 3 groups (5 mice per group). A549 cells were cultured with different doses of CHA extract (0, 200, 600 μg/ml) for 24 h, and subsequently 5 × 10⁶ cells were injected into the tail vein of each mouse. Mice were euthanized 3 weeks after injection, and the lungs were examined with hematoxylin–eosin (H&E) staining, and the metastasis was observed and evaluated under a microscope.

Statistical analysis

All data were analyzed using Graphpad Prism 8.0 software and expressed as “mean ± standard deviation”. One-way analysis of variance (ANOVA) with a Student–Newman–Keuls (SNK) post-hoc test was used to conduct multi-group data analysis. *P < 0.05, **P < 0.01, and P < 0.001 is considered to be significant.

Result

Acquisition of active ingredients of CHA and target prediction

According to the relevant parameters of Lipinski's rule of five (molecular weight ≤ 500, octanol/water partition coefficient ≤ 5, hydrogen bond donors ≤ 5, hydrogen bond acceptors ≤ 10, rotation bond number ≤ 10), 27 active ingredients of CHA were obtained through TCMSP and SwissADME databases, of which 8 were from Codonopsis Radix, 5 were from Hedysarum Multijugum Maxim., and 14 were from Atractylodes Macrocephala Koidz. (Table S1). Subsequently, the targets of bioactive ingredients were predicted by SwissTargetPrediction database, and 518 potential targets were obtained (Probability ≥ 0.1).

Collection the targets of CHA in LC treatment

A total of 934, 4373, 4217 LC-related genes were obtained from MalaCards, GeneCards and DisGenNET databases, respectively, and 6092 LC-related genes were obtained after combination (Fig. 2A). The Venn diagram showed a total of 411 genes belonged to both LC-related genes and drug targets of CHA (“common targets”) (Fig. 2B).

Fig. 2.

Collection of CHA’s targets in LC treatment. A LC-related genes in MalaCards, GeneCards and DisGenNET databases. B The Venn diagram of CHA active ingredients’ targets and LC-related genes

GO and KEGG analyses based on the common targets of CHA in LC treatment

To elucidate the biological function of 411 common targets, which could probably modulate by CHA, GO analysis and KEGG enrichment analysis were then performed, and the results showed that common targets were associated with protein phosphorylation modification, regulation of mitogen-activated protein kinase (MAPK) activity, glandular development, cellular calcium homeostasis, and signal transduction of immune response activation (Fig. 3A). KEGG enrichment analysis showed that the common targets were associated with prostate cancer, EGFR tyrosine kinase inhibitor resistance, endocrine resistance, hepatitis B, PI3K-Akt signaling pathway (with highest gene counts), proteoglycans in cancer, T cell receptor signaling pathway, AGE-RAGE signaling pathway in diabetic complications, focal adhesion, neurotrophin signaling pathway (Fig. 3B). Consistent with these results, Metascape database suggested that these common genes were mainly enriched in protein tyrosine kinase activity, metabolism, EGFR signaling pathway and cancer-related pathways (Fig. 3C&D; Table 1).

Fig. 3.

GO and KEGG analysis. A The biological processes (BP), molecular function (MF) and cellular components (CC) probably regulated by the common target genes. B KEGG pathways probably modulated by the common target genes. Bubble size represents the number of enriched genes, and bubble color difference represents the significant degree of target gene enrichment. C The pathway enrichment analysis of common target genes using Metascape. Nodes of the same enrichment network were colored. The darker the color, the greater the statistical significance of the node. D Protein–protein interaction network and Molecular Complex Detection (MCODE) components identified in the gene lists using Metascape

Table 1.

MCODE enrichment analysis of the common genes of CHA in LC treatment

MCODE	GO	Description	Log10(P)
MCODE_1	GO:0004713	protein tyrosine kinase activity	-36.3
	GO:0004672	protein kinase activity	-35.3
	GO:0016301	kinase activity	-34
MCODE_2	GO:0006690	icosanoid metabolic process	-19
	GO:0044597	daunorubicin metabolic process	-18.5
	GO:0030647	aminoglycoside antibiotic metabolic process	-18
MCODE_3	GO:0007200	phospholipase C-activating G protein-coupled receptor signaling pathway	-21.7
	hsa04080	Neuroactive ligand-receptor interaction	-21.2
	GO:0007610	behavior	-17.5
MCODE_4	GO:0007169	transmembrane receptor protein tyrosine kinase signaling pathway	-56.3
	GO:0007167	enzyme-linked receptor protein signaling pathway	-50.7
	GO:0004713	protein tyrosine kinase activity	-45.8
MCODE_5	hsa05200	Pathways in cancer	-14.8
	hsa05169	Epstein-Barr virus infection	-13.6
	hsa05170	Human immunodeficiency virus 1 infection	-13.4

We then constructed a network diagram of the relationships between the top 10 signaling pathways enriched by the common targets. As shown (Fig. 4A), the network consisted of 133 nodes and 375 edges. After integrating core targets, the results of functional enrichment analysis, and the results of target-pathway network analysis, it was revealed that CHA played a role mainly through the PI3K-AKT signaling pathway, which contained a total of 61 common targets. These targets participated in Toll-like receptor signaling pathway, B cell receptor signaling pathway, JAK/STAT signaling pathway, Focal adhesion, Chemokine signaling pathway, mechanistic target of rapamycin kinase (mTOR) signaling pathway, vascular endothelial growth factor (VEGF) signaling pathway, MAPK signaling pathway and forkhead box transcription factor signaling pathway (Fig. 4B). These results suggested that CHA exerted anti-LC effects by influencing multiple targets and regulating multiple signaling pathways.

Fig. 4.

The relationships between the top 10 signaling pathways. A Pathway and target network diagram of the top 10 signaling pathways. B The target genes in PI3K-Akt pathway were marked with red, and the related pathways were shown

Construction of PPI network and screening of core targets

We then constructed a PPI network analysis with String database, and the individual nodes were removed, and the minimum required interaction score was set to 0.9 (0.9 has the highest confidence in STRING database), to obtain the interacted proteins with the highest confidence, and finally 410 nodes with 1310 edges were obtained, with an average node degree of 6.39. PPI enrichment p-value < 1.0e-16 (Fig. 5A). Then, CytoNCA plug-in in Cytoscape 3.9.1 software was used to calculate DC, BC and CC of each node, and then common targets are screened according to the median value. After three rounds of screening (BC ≥ 3507.56, CC ≥ 0.35, DC ≥ 23.00), 10 core targets were obtained, which included SRC, growth factor receptor bound protein 2 (GRB2), HSP90AA1, AKT1, MAPK1, EGFR, ESR1, protein kinase cAMP-activated catalytic subunit alpha (PRKACA), heat shock protein 90 alpha family class B member 1 (HSP90AB1), and E1A binding protein p300 (EP300) (Fig. 5B-C; Table 2).

Fig. 5.

PPI network construction and core target genes screening. A PPI network diagram of 411 common target genes was constructed with STRING database. B The PPI network was visualized with Cytoscape 3.9.1 software. The larger the node, the darker the color, the larger the DC value. The circle on the right represents 10 core targets. C The bar chart shows the DC, BC, and CC values for the 10 core targets

Table 2.

Protein classes of key targets

Common name	Protein	UniProt ID	Protein class
SRC	Tyrosine-protein kinase SRC	P12931	Kinase
GRB2	Growth factor receptor-bound protein 2	P62993	Other cytosolic protein
HSP90AA1	Heat shock protein 90 alpha	P07900	Other cytosolic protein
AKT1	RAC-alpha serine/threonine-protein kinase	P31749	Kinase
MAPK1	Mitogen-activated protein kinase 1	P28482	Kinase
EGFR	Epidermal growth factor receptor	P00533	Kinase
ESR1	Estrogen receptor	P03372	Nuclear receptor
PRKACA	cAMP-dependent protein kinase catalytic subunit alpha	P17612	Kinase
HSP90AB1	Heat shock protein HSP 90-beta	P08238	Other cytosolic protein
EP300	Histone acetyltransferase p300	Q09472	Writer

Clinical significance of the 10 core targets in LC

The TCGA dataset in UALCAN database was then used to analyze the expression of 10 core targets in LC, and the results showed that HSP90AA1, SRC, HSP90AB1, EGFR and ESR1 were significantly up-regulated in LC (P < 0.05). GRB2, PRKACA and MAPK1 were significantly down-regulated in LC (P < 0.05), while AKT1 and EP300 were not significantly changed (P > 0.05) (Supplementary Fig. 1). Survival analysis showed that patients with high HSP90AA1 expression had short survival time and poor prognosis, and the expression of other genes had no significant relationship with survival time (Supplementary Fig. 2).

Results of molecular docking

Sankey diagram was used to show the relationship between 10 core targets, active molecules and drugs, and it was found that the 10 core targets were potential targets of 20 CHA bioactive molecules (Supplementary Fig. 3A). Based on the DC values, 12-senecioyl-2E,8E,10E-atractylentriol (MOL000020, DC = 92), 14-acetyl-12-senecioyl-2E,8Z,10E-atractylentriol (MOL000022, DC = 91), Astrapterocarpan (MOL000380, DC = 93), isoflavanone (MOL000398, DC = 93), (R)-Isomucronulatol (MOL000438, DC = 92) were considered to be the important bioactive ingredients of CHA; SRC, HSP90AA1, AKT1, EGFR, ESR1 were identified as the top 5 targets. Molecular docking was then performed respectively, with binding energies less than " − 5" indicating that the compound and target were well bound to each other [27]. The results showed that the docking scores of 5 active ingredients and core targets ranged from -5.0 to -9.2 kcal/mol, indicating a good binding ability between the active ingredients and the key targets (Supplementary Fig. 3B). Among them, the 8 groups of complexes with the lowest binding energies were shown (Fig. 6). As shown, (R)-Isomucronulatol formed 2 hydrogen bonds with amino acid residues PH-405 (A) and Thr-338 (A), forming the most stable complex with ESR1. Astrapterocarpan formed a hydrogen bond with amino acid residue PH-405 (A) to bind with ESR1. Isoflavanone formed a hydrogen bond with amino acid residue PH-405 (A) to bind with ESR1. Astrapterocarpan formed a hydrogen bond with amino acid residue Asp-93 (A) to bind to HSP90AA1. 14-acetyl-12-senecioyl-2E,8Z,10E-atractylentriol formed 3 hydrogen bonds with amino acid residues Asn-54 (A), Tyr-326 (A), and Thr-211 (A) of AKT1. Astrapterocarpan formed 3 hydrogen bonds with amino acid residues Asn-204 (A) and Ser-205(A) of AKT1. Isoflavanone formed 3 hydrogen bonds with amino acid residues Thr-82 (A) and Tyr272 (A) of AKT1. (R)-Isomucronulatol formed three hydrogen bonds with amino acid residues Asn-53 (A) and Ile-290 (A) to form stable complexes with AKT1.

Fig. 6.

Molecule docking between target proteins and the bioactive components of CHA

CHA extract inhibits the proliferation, migration and invasion of LC cells

We then evaluated the effect of CHA on the malignant biological behaviors of NCI-H1975 and A549 cells. The results of CCK8 experiment showed that the survival rate of LC cells was significantly decreased with the increase of CHA concentration (from 100 μg/mL to 800 μg/mL) (Fig. 7A). The IC₅₀ values of CHA for NCI-H1975 and A549 cell lines were 375.6 μg/mL and 548.3 μg/mL, respectively. Next, 200, 400 and 600 μg/mL CHA were selected for further experiments. Transwell assay showed that CHA significantly inhibited the migration and invasion ability of NCI-H1975 and A549 (Fig. 7B-C). Additionally, qRT-PCR showed that CHA significantly inhibited the expression levels of SRC, HSP90AA1, AKT1, EGFR, and ESR1 mRNA (Fig. 7D). To evaluate the regulatory effects of CHA on PI3K/AKT pathway, the phosphorylation levels of PI3K p85 and AKT in NCI-H1975 and A549 cells after CHA treatment were examined by Western blot. The results showed that CHA treatment significantly repressed the activation of PI3K/AKT pathway (Fig. 8A&B). These results indicated that CHA significantly inhibited the viability, migration and invasion of LC cells by regulating the expression of the core genes and the activation of PI3K/AKT pathway. To further validate that CHA could repressed the malignancy of LC cells, A549 cells were treated with CHA, and then transplanted into nude mice. As expected, after A549 cells were treated with CHA, their ability to form tumor nodules in lung tissues of the mice were markedly repressed (Fig. 9).

Fig. 7.

CHA can inhibit the proliferation, migration and invasion of LC cells. A CCK-8 assay was used to detect the viability of LC cells treated with different concentrations of CHA for 24 h. B&C Transwell assay was used to detect the migration and invasion ability of LC cells. D qRT-PCR assay was used to detect the mRNA expression levels of SRC, HSP90AA1, AKT1, EGFR and ESR1 in LC cells treated with different concentrations of CHA. *P < 0.05, **P < 0.01, ***P < 0.001 versus 0 μg/mL of CHA group

Fig. 8.

CHA can inhibit activation of PI3K/AKT pathway. A&B Western blot was used to detect the expression levels of p-PI3K (p85), total PI3K (p85), p-AKT and AKT in LC cells treated with different concentrations of CHA. *P < 0.05, **P < 0.01, ***P < 0.001 versus 0 μg/mL of CHA group

Fig. 9.

CHA can inhibit the metastasis of A549 cells in vivo. A&B A549 cells were treated with different concentrations of CHA (0, 200, 600 μg/ml), and then the malignancy of A549 cells was evaluated with a lung metastasis model in nude mice. ***P < 0.001 versus 0 μg/mL of CHA group

Discussion

TCM may improve the clinical efficacy of chemotherapy, radiotherapy, targeted therapy and immunotherapy in cancer treatment [5, 10]. Codonopsis Radix., Hedysarum Multijugum Maxim., and Atractylodes Macrocephala Koidz. have shown good tumor-suppressive properties in pre-clinical study. Specifically, Astragalus polysaccharide in Codonopsis Radix. induces the apoptosis of LC cells [28]. Lobetyolin is one of the main bioactive extracts of the root of Codonopsis Radix., which can down-regulate glutamine metabolism and contribute to drug-induced apoptosis and tumor growth inhibition [29]. Atractylodes Macrocephala Koidz. inhibits cell proliferation and promotes apoptosis of small cell lung cancer through PI3K/Akt/mTOR signaling pathway [16].

In the present work, 518 target genes of CHA were obtained, and 411 of them were associated with tumorigenesis and progression of LC. GO analysis showed that the 411 genes were involved in multiple biological components / processes. Additionally, KEGG enrichment analysis showed that CHA could probably have therapeutic effects on LC through PI3K-AKT signaling, MAPK signaling and EGFR signaling. PI3K-AKT pathway plays an important role in the regulation of cell survival, growth and proliferation [30]. PI3K can be activated by a variety of growth factors and signal transduction complexes, such as G protein-coupled receptors, B cell receptors, VEGF, and fibroblast growth factor; when PI3K is activated, it recruits the downstream signaling molecule AKT; and activated AKT regulates LC cell proliferation [31]. MAPK pathway also plays a key role in dominating cancer cell growth, differentiation, and apoptosis [32]. MAPK pathway is often triggered by extracellular stress, inducing inflammatory cytokines, and excessive inflammation leads to many diseases, including cancer [33]. It has been confirmed that activation of MAPK signaling promotes the proliferation of LC cells and reduce apoptosis [34]. When activated, EGFR pathway will also activate a series of downstream pathways, such as RAS/RAF/MAPK, PI3K/AKT, JAK/STAT, which can enhance the malignant biological behaviors of tumor cells [35].

According to DC, BC and CC scores, the top 10 targets of CHA were identified as SRC, GRB2, HSP90AA1, AKT1, MAPK1, EGFR, ESR1, PRKACA, HSP90AB1 and EP300. Previous studies have confirmed that these genes are closely associated with the occurrence and development of LC. The main bioactive components of CHA, including 12-senecioyl-2E,8E,10E-atractylentriol, 14-acetyl-12-senecioyl-2E,8Z,10E-atractylentriol, astrapterocarpan, isoflavanone, (R)-Isomucronulatol were further identified through drug-active ingredient-target network analysis and molecular docking, which mainly acted on key targets including SRC, HSP90AA1, AKT1, EGFR, and ESR1. SRC is a non-receptor tyrosine kinase protein that has been reported to promote tumorigenesis and metastasis of various cancers [36]. In addition, SRC was found to interact with a variety of growth factor receptors in LC cells, including EGFR, human epidermal growth factor receptor 2 (HER2), insulin-like growth factor receptor (IGFR), fibroblast growth factor receptor (FGFR), et al. [37]. EGFR is a transmembrane tyrosine kinase receptor and a member of the ErbB family; it is well known that EGFR mutations are major driver mutations of some non-small cell lung cancer cases [38]. As a highly conserved chaperone protein, HSP90AA1 is involved in tumor cell differentiation, proliferation and angiogenesis. It is highly expressed in a variety of cancers, such as LC [39], breast cancer [40], head and neck cancer [41], esophageal squamous cell carcinoma [42], etc. In addition, HSP90AA1 is involved in the regulation of apoptosis and signal transduction triggered by growth factors, death receptors and stress signals [43]. Moreover, HSP90AA1 protein is believed to play a key role in the regulation of tumor invasion and migration [44, 45]. As a crucial part of PI3K-AKT pathway, AKT1 can regulate glucose metabolism, cell cycle progression and apoptosis of cancer cells [46, 47]. ESR1 encodes estrogen receptor, and it is involved in a variety of cancers and other pathological processes [48, 49]. Notably, ESR1 overexpression is associated with the prognosis of LC [50]. In vitro assay and in vivo assay of this study confirmed that CHA inhibited the viability, migration and invasion of LC cells, which is consistent with the previous reports about the tumor-suppressive properties of the 3 kinds of herbal medicine [11–17]. Interestingly, it was observed that CHA negatively regulated the expression levels of SRC, HSP90AA1, AKT1, EGFR, and ESR1 mRNA in cancer cells. This suggests that CHA not only disturbs the protein function of these downstream targets, but also suppresses their transcription, and the detailed mechanism, by which CHA regulates the transcription of the targets, remains to be explored in the following work.

In the present work, our data implied that 12-senecioyl-2E,8E,10E-atractylentriol, 14-acetyl-12-senecioyl-2E,8Z,10E-atractylentriol, astrapterocarpan, isoflavanone, (R)-Isomucronulatol were promising anti-cancer drugs for treating LC, and our data preliminarily explained the downstream mechanism by which CHA repressed LC progression. However, there are some limitations in this study. Network pharmacology grasps the network relationship between different components as a whole, and in a single network pharmacology, it is difficult to clarify the biological effects of each component with details. This study only conducted molecular docking based on the 5 active ingredients, and the in vitro and in vivo assays were only based on CHA extract, and the tumor-suppressive properties of these 5 bioactive components, and other components of CHA, remain to be evaluated with more functional assays, respectively. Additionally, in the present work, the components of CHA are collected by open access databases, and some components may be missed. Technologies such as liquid chromatograph mass spectrometer may be helpful to discover the functional components more comprehensively in the future studies.

Conclusion

This study used network pharmacology to analyze the potential mechanism of CHA-based prescription in LC treatment. The results of this study suggests that the bioactive ingredients of CHA, including 12-senecioyl-2E,8E,10E-atractylentriol, 14-acetyl-12-senecioyl-2E,8Z,10E-atractylentriol, astrapterocarpan, isoflavanone, (R)-Isomucronulatol, act on key target genes such as SRC, HSP90AA1, AKT1, EGFR, and ESR1 in lung cancer cells, and participate in the regulation of various signaling pathways such as PI3K-AKT. Our work provides a theoretical basis for the clinical application of CHA.

Authors’ contributions

Conceived and designed the experiments: Zengwang Zhang and Yongjun Zhang; Collected and assembled data: Bin He and Jun Cui; Analyzed the data: Bin He and Jun Cui; Wrote the paper: Zengwang Zhang and Yongjun Zhang. All authors read and approved the final manuscript.

Funding

This study received no funding.

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Animal experiments of this study followed the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines. The procedures of animal assays were approved by the Ethics Committee of the Xiangyang Central Hospital (Approval No. 2024037).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.