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Immunoregulatory mechanism studies of ginseng leaves on lung cancer based on network pharmacology and molecular docking


Panax ginseng is one of the oldest and most generally prescribed herbs in Eastern traditional medicine to treat diseases. Several studies had documented that ginseng leaves have anti-oxidative, anti-inflammatory, and anticancer properties similar to those of ginseng root. The aim of this research was to forecast of the molecular mechanism of ginseng leaves on lung cancer by molecular docking and network pharmacology so as to decipher ginseng leaves' entire mechanism. The compounds associated with ginseng leaves were searched by TCMSP. TCMSP and Swiss Target Prediction databases were used to sort out the potential targets of the main chemical components. Targets were collected from OMIM, PharmGKB, TTD, DrugBank and GeneCards which related to immunity and lung cancer. Ginseng leaves exert its lung cancer suppressive function by regulating the several signaling proteins, such as JUN, STAT3, AKT1, TNF, MAPK1, TP53. GO and KEGG analyses indicated that the immunoreaction against lung cancer by ginseng leaves might be related to response to lipopolysaccharide, response to oxidative stress, PI3K-Akt, MAPK and TNF pathway. Molecular docking analysis demonstrated that hydrogen bonding was interaction's core forms. The results of CCK8 test and qRT-PCR showed that ginseng leaves inhibit cell proliferation and regulates AKT1 and P53 expression in A549. The present study clarifies the mechanism of Ginseng leaves against lung cancer and provides evidence to support its clinical use.


According to the IARC, lung cancer was the malignant tumor with the highest mortality rate due to the poor prognosis1. Surgery is the first choice for lung cancer treatment, but most patients have already reached the middle and late-stage and have lost the best surgery chance when diagnosed. Therefore, radiotherapy and chemotherapy became the main clinical methods for treating lung cancers. However, conventional therapies have large toxic side effects, which interrupts most patients in completing an effective radiotherapy and chemotherapy cycle. Scholars have turned their attention to the development and utilization of traditional Chinese medicine, striving to develop safe and effective lung cancer adjuvant drugs.

Ginseng, dried roots of Panax ginseng C. A. Mey., is a precious Chinese herb with a long history of medicinal use. It was first published in "Shen Nong's Materia Medica" and listed as the top grade. Modern chemical and pharmacological studies have found that the extracts of ginseng leaves contained more ginsenosides than the roots and showed potent anticancer activity2,3,4. However, most of the researches are the anti-cancer effects of individual components of ginseng leaves. The polysaccharides in ginseng leaves inhibit tumor metastasis through activating macrophages and NK cells. Ginsenosides enhance the immune response of innate immune cells such as dendritic cells, phagocytes and NK cells by affecting the immune system and kill virus infects cells and tumor cells, regulate cytokines, complement and other immune molecules to produce immunoregulatory effects5,6,7. There is no report on the overall mechanism of multi-component and multi-target of ginseng leaves in the immunomodulation treatment of lung cancer. Traditional Chinese medicine has the characteristics of multiple components and multiple targets, with definite curative effect, relatively few adverse reactions, and low price8. It has attracted more and more attention from pharmaceutical workers. However, the features of traditional Chinese medicine (TCM) have caused bottlenecks in the study of its mechanism of action, affecting its worldwide application and promotion.

In 2007, Professor Hopkins, a British scholar, put forward the concept of "network pharmacology" based on network biology. Its core idea is to reveal the mechanism of drug action and guide drug molecular designed by multi-dimensional, multi-pathway, and multi-target points of the interaction network of genes, proteins, and metabolites9. At present, a large number of reports have proved that the computer simulation such as molecular docking and network pharmacology can successfully predict the active ingredients, targets and mechanisms of TCM, save the cost of drug development, and provide new strategies for the research on the material basis and mechanism of TCM10,11,12.

Therefore, the aim of this article was to clarify the overall mechanism of ginseng leaves in lung cancer therapy by adopting network pharmacological and molecular docking, providing new insights into the effects and mechanisms of ginseng leaves and a scientific basis for the clinical application. The work flow chart is shown in Fig. 1.

Figure 1

Workflow diagram of the network pharmacology-based analysis of ginseng leaves in the treatment lung cancer.


Active ingredients and related targets of ginseng leaves

From TCMSP, one hundred and twenty-nine ingredients in ginseng leaves were acquired. Since OB and DL represent the most important pharmacokinetic properties, OB ≥ 30% and DL ≥ 0.18 were set as the limitation for the active ingredients 13,14. There were nine compounds meeting the parameter. As shown in the Table 1, eight compounds were finally identified as the active chemical ingredients of ginseng leaves (Table 1). According to the TCMSP database and the Swiss Target Prediction database, two hundred and ten targets that were associated with the above active chemical compounds were discovered.

Table 1 Core components of panax ginseng leaves.

Screening mutual targets of drugs and diseases

The core active component targets of ginseng leaves were matched with the targets of lung cancer and immunity, resulting in the selection of 193 composite targets of ginseng leaves, lung cancer and immunity (Fig. 2A) (Supplementary Table S1).

Figure 2

An association network of ginseng leaves targeted proteins associated with lung cancer and immunity. (A) Venn diagram of ginseng leaves, lung cancer and immunity targeted genes. (B) The network for 193 co-targeted genes/proteins had been selected as input for PPI analysis in STRING.

Screening and topological analysis of core targets

The PPI of the above 193 intersection targets was obtained on the STRING platform (Fig. 2B). The PPI map was taken when the data was import into Cytoscape 3.8.0. The PPI network was built with 166 nodes and 698 edges. The interaction is represented by each edge between proteins. Each Node represents the target protein molecules15. Then, according to the network topology's features, the median values for the DC, BC, CC, EC, LAC, NC were used to analyse potential drug targets16. Fifteen highly connected nodes (degree > 10, BC > 16.3058, CC > 0.5402, LAC > 5.4444, NC > 6.3355) were confirmed as significantly related targets with Network Analyzer in Table 2 and Fig. 3. The PPI network shows the targets involved in lung cancer and immunity were HSP90AA1, JUN, STAT3 EGFR, MYC, VEGFA, CCND1, TNF, MAPK1, AKT1, RELA, CDKN1A, TP53, IL2 and IL1B which were the major targets in lung cancer.

Table 2 Information on 15 core targets.
Figure 3

The protein–protein interaction (PPI) network. (A) The PPI network containing 166 nodes and 698 edges. The areas highlighted in red are represented the candidate targets ranked in the median values for the DC, BC, CC, EC, LAC, NC. (B) The PPI network for the candidate targets ranked in the median values for the DC, BC, CC, EC, LAC, NC. The PPI network containing 48 nodes and 282 edges. The areas highlighted in yellow are represented the potential targets with the median values for the DC, BC, CC, EC, LAC, NC. (C) The PPI network for the 15 potential targets with the median values for the DC, BC, CC, EC, LAC, NC.

Enrichment analysis of GO and KEGG pathway

GO functional enrichment analysis of key targets was performed relying on Bioconductor package in R software. The GO terms were screened according to the P (P < 0.05, Q < 0.05) value. There were 2363, 45, and 181 GO terms related to biological processes, cell components, and molecular functions, respectively (Fig. 4). As showed in Fig. 4, the top 10 biological processes were related to lipopolysaccharide, nutrient levels, oxygen levels, etc. For cellular components, the targets were enriched in membrane raft, membrane microdomain, transcription regulator complex, etc. Molecular Functions analysis revealed including DNA-binding transcription factor, ubiquitin-like protein ligase, cytokine receptor, oxidoreductase activity, etc.

Figure 4

The GO enrichment analysis of core nodes. Including cellular components, molecular functions, biological processes.

The KEGG signal pathway enrichment analysis contained 164 pathways. It screened the first 30 pathways related to Human cytomegalovirus infection, PI3K–Akt pathway, MAPK signaling pathway, Small cell lung cancer, Non-small cell lung cancer (NSCLC), Proteoglycans in cancer, Prostate cancer, TNF signaling pathway, Hepatocellular carcinoma, IL-17 signaling pathway, Apoptosis, etc. (Fig. 5) 17,18,19. Comparing with the gene ontology and KEGG analysis outcomes, genes encoding proteins targeted by ginseng leaves should play a role in PI3K–Akt pathway, MAPK signaling pathway, TNF signaling pathway, Small cell lung cancer, Non-small cell lung cancer, apoptosis. These pathways involved cytokine receptor.

Figure 5

The top 30 pathways for KEGG enrichment analysis of the targets of ginseng leaves.

Active ingredients-shared key targets-signal pathway network diagram construction

The component-target-channel network diagram was formed to holistically make clear ginseng leaves' mechanism in Lung cancer immunity through Cytoscape 3.7.2. (Fig. 6). In the figure, the green Ellipse represented the ingredients in the drug, the yellow Diamond represented the targets of the ingredients, and the red V shape represented the pathways enriched in the targets. It demonstrated that each active compound could act upon multiple targets, each target can also respond to multiple active ingredients, and each target can have an effect on multiple pathways.

Figure 6

The “active components-target genes-pathways” network.

Key active ingredients and core target molecular docking results

The top six-core targets of degree in the PPI network were molecularly docked with their corresponding active ingredients. The docking protein data were downloaded from the PDB database, which was STAT3 (PDB ID 6qHD), MAPK1 (PDB ID 2OJI), TP53 (PDB ID 7BWN), JUN (PDB ID 5T01), TNF (PDB ID 2TUN) and AKT1 (PDB ID 4ejn). The 3D structure was imported into AutoDock software and docked with the active compound. The top six core targets in the PPI network and their corresponding small molecule drug ligands were docked using AutoDock Vina and R 4.0.2 software (Table 3). The smaller the binding energy is, the better the ligand can bind to the protein. Mode 1 with the lowest binding energy was visualized using Pymol software (Fig. 7). The primary forms of interaction were hydrogen bonding and π–π stacking. This result indicated that their combination might play an important role in the treatment of lung cancer with ginseng leaves.

Table 3 Docking scores of the active ingredients of ginseng leaves with their potential targets.
Figure 7

Detailed target-compound interactions.

Cytotoxicity on human lung adenocarcinoma cells line A549

A549 was significantly decreased after 24 h exposure to ginseng leaves extract and F2 in a concentration-dependent manner(Fig. 8A,B). Cytotoxic effect of ginseng leaves extract and F2 were confirmed against human Lung Adenocarcinoma Cells line A549.

Figure 8

Ginseng leaves extract and F2 significantly decreases the viability of human lung adenocarcinoma cells. (A) Effects of the ginseng leaves extract on the viability of A549 cells. (B) Effects of the F2 on the viability of A549 cells.

Effect of ginseng leaves extract on the gene expression of AKT, STAT3 and P53 in A549

The mRNA expression levels of AKT1 and P53 were significantly different between the blank control group and ginseng leaves extract group (p < 0.05).Meanwhile, P53 was up-regulated expression, while AKT1 was down-regulated expression. Furthermore, there was no significant difference between groups in STAT3 mRNA expressions (Fig. 9).In conclusion, the results suggested that the above core targets could play important roles in ginseng leaves extract treatment of lung adenocarcinoma.

Figure 9

The mRNA expression levels of AKT1, STAT3, P53. Compared with the control group, the expression levels of AKT1 and P53 mRNA have significant difference in the treatment group (***p < 0.0001).


Lung cancer is one of the most ordinary malignancies on earth, and its high morbidity and mortality seriously threaten the health of people all over the world20,21. TCM exerts its anticancer effects through apoptosis induction, proliferation inhibition, metastasis suppression, multidrug resistance reversal and immune function regulation. Moreover, TCM can ameliorate patients' quality of life22,23. A number of data show that ginseng leaves can inhibit tumor migration and invasion by regulating the crosstalk between tumor-related macrophages and non-small cell lung cancer, and improve the body’s immune function24,25,26,27. Therefore, this paper researched the action mechanism from the point of immunomodulatory on lung cancer by the integrity of ginseng leaves.

In this paper, the core active components in ginseng leaves were collected through data mining. The mechanism of inhibiting lung cancer through immune regulation was explored microscopically by network pharmacology. In the Venn diagram where the active ingredients of ginseng leaves intersect with diseases, there were 209 intersecting targets between drugs and lung cancer, and 193 intersecting targets between drugs and immunity. One hundred and ninety-three composite targets of ginseng leaves, lung cancer and immunity are the same as the immune intersection targets. The results showed that 92% of the treatment of ginseng leaves for lung cancer could be achieved by immune regulation.

BP analysis results showed that core active components were involved in reactive oxygen species metabolic process, response to lipopolysaccharide, response to oxidative stress, etc. Oxidative stress is characterized by the imbalance between reactive oxygen species (ROS) production and the capacity of the antioxidant system. Oxidative stress injury results in lipid peroxidation. The final product of lipid peroxidation can cause subsequent pathological consequences through the rearrangement of peroxyl radical, especially in the lungs28. Oxidative stress had been well documented to play a major role in cancer. Oxidative damage to DNA caused by ROS is thought to be one of the causes of cancer. Lipopolysaccharide (LPS) is a crucial component of the Gram-negative bacteria cell wall29. A change in the state or activity of the body caused by lipopolysaccharide stimulation. In vivo, endothelial cells, epithelial cells, monocytes and macrophages can be activated through the cell signal transduction system. Synthesizing and releasing various cell-stimulating factors, stimulating the body's active defense, cause a series of inflammatory reactions, and exert the early immune response effect. Studies have confirmed that LPS can enhance the invasion and metastasis abilities of lung cancer and other malignant tumor cells30,31.

In the KEGG enrichment, Pathways associated with lung cancer and immunity and more enriched in 14 potential targets were PI3K–Akt pathway, MAPK signaling pathway, Non-small cell lung cancer.HSP90AA1, EGFR, MYC, VEGFA, CCND1, MAPK1, AKT1, RELA, CDKN1A, IL2 and TP53 targets are enriched in the PI3K–Akt pathway. This pathway is activated by various types of cellular stimuli or toxic insults to regulates fundamental cellular functions such as transcription, translation, proliferation, growth, and survival. PI3K/AKT pathway's deregulation is involved in lung tumorigenesis and it has been connected with high-grade tumors and advanced disease32. Deregulation of this pathway proceeds through a variety of mechanisms including upstream activation by tyrosine kinase receptors of PI3K, PIK3CA amplification as well as mutations in KRAS, PI3K or AKT33. The MAPK signaling pathway regulates the cell cycle, and the increase in its activity is positively correlated with the proliferative capacity of tumor cells, disease development and poor prognosis34. JUN, EGFR, MYC, VEGFA, TNF, MAPK1, AKT1, RELA, IL1B and TP53 are enriched in the MAPK signaling pathway. Studies have found that this effect may be related to the induction of Th1 cells to produce IFN-γ and promote cell proliferation35,36.

In the network topology analysis, the active ingredients of ginseng leaves have a greater possibility of acting on the immune regulation of lung cancer through HSP90AA1, JUN, STAT3 EGFR, MYC, VEGFA, CCND1, TNF, MAPK1, AKT1, RELA, CDKN1A, IL1B, IL2 and TP53. TP53 and JUN are important tumor suppressor genes and are new directions for gene targeted therapy of cancer37,38. TP53 strictly regulates the start of the cell cycle and can repair damaged DNA in time. When genes are mutated and signal pathways are abnormal, the cell cycle is out of control and certain genetic damages cannot be repaired and mutated, accelerating the progression of the tumor39. Studies have verified that the up-regulation of the HSP90AA1 gene reduced the body’s immune surveillance and resistance to foreign substances, and involving DNA self-repair capabilities40. STAT3 is a transcription factor activated by various external stimuli and can induce cell growth, differentiation and survival41. It is mentioned that STAT3 activity is enhanced in up to 50% of all human tumors42. It is known that the activation of STAT3 in many malignant tumors is related to tumor proliferation, invasion, metastasis, and resistance to chemotherapy and radiotherapy43,44. In NSCLC, STAT3 is continuously activated in 22–65% of cases, and STAT3 activation is linked to poor prognosis, proliferation, and chemotherapy resistance45,46,47,48. EGFR as a transmembrane glycoprotein, which is one of the four members of the tyrosine kinase receptor ErbB family49 is considered to be the driving gene of the tumor in the development of NSCLC50,51. VEGFA is a considerable target for anti-tumor therapy, and its expression is negatively associated with the differentiation and prognosis of lung cancer52. CCND1, the most important member of the cyclin family, is currently considered an oncogene and is overexpressed in human esophageal cancer, head and neck cancer, and breast cancer. The overexpression of CCND1 is considered an early incident in diverse carcinomas53.

Tumor necrosis factor TNF is called death ligand and can bind to extracellular death receptor to initiate the apoptosis program54. TNF-α interacts with TNFR1 and TNFR2 receptors and activates downstream intracellular signaling pathways. TNF-α induces both apoptosis and necrosis of cells through the TNFR155. MAPK1 is chiefly involved in the regulation of cell proliferation, differentiation and apoptosis56. RelA transcription factors is a member of the NF-κB family of transcription factors and control gene expression in response to stimuli such as inflammation57. The abnormal expression of Myc is also associated with lung cancer, which was significantly overexpressed in more than 70% of NSCLC58. Early Myc activity is causally related to cancer mainly through its ability to drive tumor cell proliferation to participate in biosynthesis, cell metabolism; angiogenesis, invasion and metastasis59. CDKN1A plays a key role in the progression of the cell cycle and regulates both G1/S and G2/M checkpoints. CDKN1A, as an oncogene, promotes cancer cell proliferation by inhibiting apoptosis60,61. Interleukin- (IL-) is an inflammatory cytokine that belongs to the IL-1 family62. IL-1β is an effective driver of tumor progression. It is highly expressed in metastatic non-small cell lung cancer and drives tumor growth, invasion and metastasis63. Interleukin-2 (IL-2) is a cytokine signaling molecule necessary for the differentiation, growth and proliferation of T lymphocytes. It has been shown to improve the survival rate of patients with non-small cell lung cancer64. IL2 stimulates the proliferation and activation of immune cells with anti-tumor activity65. In summary, multiple targets and pathways are closely related to the occurrence, development, and prognosis of lung cancer, confirming the characteristics of multi-component, multi-target and multi-channel treatment of ginseng leaves.

In the component-target-pathway network, four core active components and 15 key potential targets are involved, all of which play an important part in anti-lung cancer through immune regulation. Molecular docking results indicate that the components had effective free energy against key targets. These findings verify the reliability of the active ingredients screened by network pharmacology and their interaction with lung cancer and immunity targets. Ginsenoside F2 and STAT3 have the best affinity. It has been noted that F2 induces apoptosis by causing an accumulation of ROS and activating the ASK-1/JNK signaling pathway66. F2 induces apoptosis of breast cancer by activating the intrinsic apoptotic pathway and mitochondrial dysfunction. However, the potential anti-cancer effect of ginsenoside F2 in lung cancer cells has not been appraised67. In this study, F2 inhibits the proliferation of A549 by the CCK8 experiment, but its mechanism of action is not yet understood. Further study will be made later on the basis of this finding.

Based on the research strategy of network pharmacology, this article integrates various database information and uses molecular docking for preliminary verification, and clarifies that the core components of ginseng leaves have multi-component and multi-target mechanism characteristics in inhibiting lung cancer through immunomodulation. However, Chinese medicine is not a single set of chemical components. Data mining and network pharmacology are based on the existing big data to make reasonable predictions about the internal mechanism of ginseng leaf inhibiting lung cancer through immune function. In the later stage, the above mentioned potential medicinal ingredients, target points, and mechanism of action need to be experimentally verified to provide a more scientific basis for the development and utilization of ginseng leaves and clinical research.


Screening of core active ingredients and targets

All the chemical constituents of Ginseng Folium were retrieved based on the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP)68. According to the pharmacokinetics (ADME) parameters in the TCMSP database, oral availability (OB) ≥ 30% and drug-like activity (DL) ≥ 0.18 were set to screen the core active ingredients. Search for the active ingredients of ginseng leaves in the "Chinese Pharmacopoeia (2020)", and add the important active ingredients initially excluded by TCMSP as candidate active ingredients. TCMSP database and Swiss Target Prediction databases69 were used to retrieve the gene targets for active ingredients. Obtained targets were then mapped to the UniProt database for normalization.

Disease-associated gene mining

Targets related to lung cancer and immunization were collected from the OMIM database, PharmGkb database, TTD database70, DrugBank database and GeneCards database71. The gene name's standardization and definition of the species as "human" were executed using the UniProt database. The Venn diagram of the active components, lung cancer targets and immune targets was drawn by using the bioinformatics platform Jvenn72.

Protein–protein interaction network construction and analysis

The potential targets were formed by STRING database to acquire the targets-PPI network. Selection parameters were set to “Homo sapiens” for species and the confidence level was set at 0.900 for the minimum required interaction score. The PPI network was then visualised by the Cytoscape software. The network analyser plugin in Cytoscape was used to calculate topological parameters in the network. Finally, the key immune regulatory target of ginseng leaves suppressing lung cancer was obtained.

Gene ontology (GO) and the Kyoto encyclopedia of genes and genomes (KEGG) enrichment

Enrichment analysis of Potential Target Gene for the core drug group Ontology (GO) Enrichment and KEGG pathway enrichment were performed using Bioconductor cluster profiler73, and DOSE of R 4.0.2. Pathways were ranked according to the number of molecules in pathways with a cut-off value (p < 0.05).

Network construction

The compound-target-pathway network was constructed using Cytoscape 3.7.2. The topological properties were analysed using the Network Analyzer plug-in for Cytoscape to confirm the key components and targets74.

Molecular docking makes sure the interaction between targets and key compounds

The target protein structure was obtained from the RCSB PDB database. The 2D structure of ginseng leaves' active substance was sought by the PubChem Database 75. ChemBio Draw 3D and Autodock Tool were applied to optimize the structure of key compounds and targets, including 3-dimensional chemical structures creation, energy minimization, and format transformation76. PyMol was used to process the protein, including getting rid of the ligands, rectifying protein structure, and doing away with water 74. Docking was completed by R 4.0.2 software and Autodock Vina, and the molecules with the lowest binding energy in the docking conformation were selected to observe the binding effect by matching with the original ligands and intermolecular interactions.

Cell culture

A549 cells were obtained from the American Type Culture Collection and were plated in RPMI 1640 medium with 10% fetal calf serum and penicillin with streptomycin. Cells were all kept at 37 °C, with 5% CO2.


Cytotoxicity was assayed using CCK8, taking ginseng leaves extract, ginsenoside F2 and cyclophosphamide as the different experimental groups and A549 cells as the blank control group. A549 cells were plated in 96-well plates in 100 μL of medium containing 10% heat-inactivated FBS. After 24 h, the cells were treated with cyclophosphamide, different concentrations of ginseng leaves extract, F2 and incubated for an additional 24 h. At the indicated time points,10 μL CCK8 reagent was added to the culture medium 1 h before analysis. Optical density (OD)450 values were measured using a microplate reader.

Gene expression using quantitative real-time PCR

In this trial, ginseng leaves extract was used as the different experimental groups, and A549 cells as the blank control group. After being placed into a six-well plate, the A549 cells were add to culture medium containing best concentrations of ginseng leaves extract and then cultured for 48 h. Next, Total RNA, was isolated utilizing Trizol reagent (Invitrogen), whose concentration and purity were detected using SPECTROstar Nano Microplate Reader (BMG Labtech). According to the manufacturer’s protocol, the RNA was reverse-transcribed into cDNA using the extracted RNA sample as a template. The primer sequences were designed in the laboratory by Sangon Biotech (Sangon, China) based on the mRNA sequences obtained from the NCBI database and were purchased from Beijing Huada Gene and Biological Company, China (Table 4). Real-time PCR was then performed using an Applied Biosystems QuantStudio 5 Real-Time PCR system. Data reported as fold mRNA expression (2−ΔΔCt) and was normalized to β-actin.

Table 4 The mRNA sequences of the targets obtained from the NCBI database.


The statistical analysis was performed by the Student’s t test using GraphPad Prism 5 software. P ≤ 0.05 value between groups was considered to be statistically significant.


  1. 1.

    Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries (vol 68, pg 394, 2018). CA-A Cancer J. Clin. 70(4), 313–313 (2020).

    Google Scholar 

  2. 2.

    Lee, K. A. et al. In vitro cytotoxic activity of ginseng leaf/stem extracts obtained by subcritical water extraction. J. Ginseng Res. 38(4), 289–292 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Lee, D. Y. et al. Isolation and quantification of ginsenoside Rh23, a new anti-melanogenic compound from the leaves of Panax ginseng. Molecules 23(2), 267 (2018).

    PubMed Central  Article  CAS  Google Scholar 

  4. 4.

    Castro-Aceituno, V. et al. Anticancer activity of silver nanoparticles from Panax ginseng fresh leaves in human cancer cells. Biomed. Pharmacother. 84, 158–165 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  5. 5.

    Zhang, B. et al. The ginsenoside PPD exerts anti-endometriosis effects by suppressing estrogen receptor-mediated inhibition of endometrial stromal cell autophagy and NK cell cytotoxicity. Cell Death Dis. 9, 1–13 (2018).

    Article  CAS  Google Scholar 

  6. 6.

    Yang, X. et al. Ginsenoside Rg3 inhibits colorectal tumor growth via down-regulation of C/EBPbeta/NF-kappaB signaling. Biomed. Pharmacother. 96, 1240–1245 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  7. 7.

    Choi, Y. J., Kang, L. J. & Lee, S. G. Stimulation of DDX3 expression by ginsenoside Rg3 through the Akt/p53 pathway activates the innate immune response via TBK1/IKK epsilon/IRF3 signalling. Curr. Med. Chem. 21(8), 1050–1060 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  8. 8.

    Liu, Z. H. & Sun, X. B. Network pharmacology: New opportunity for the modernization of traditional Chinese medicine. Acta Pharmaceut. Sin. 47(6), 696–703 (2012).

    Google Scholar 

  9. 9.

    Huang, S. J. et al. Potential mechanism study of herbal pair Schizonepetae herba and Saposhnikoviae radix against coronavirus pneumonia via network pharmacology and molecular docking. Nat. Prod. Res. Dev. 32, 1087–1098 (2020).

    Google Scholar 

  10. 10.

    Li, S. & Zhang, B. Traditional Chinese medicine network pharmacology: Theory, methodology and application. Chin. J. Nat. Med. 11(2), 110–120 (2013).

    ADS  PubMed  Article  PubMed Central  Google Scholar 

  11. 11.

    Zhang, R., Zhu, X., Bai, H. & Ning, K. Network pharmacology databases for traditional Chinese medicine: Review and assessment. Front. Pharmacol. 10, 123 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  12. 12.

    Zhou, Z. et al. Applications of network pharmacology in traditional Chinese medicine research. Evid. Based Complement. Alternat. Med. 2020, 1646905 (2020).

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Pei, T. et al. Systematic understanding the mechanisms of vitiligo pathogenesis and its treatment by Qubaibabuqi formula. J. Ethnopharmacol. 190, 272–287 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  14. 14.

    Chenglin, M. et al. Potential compound from herbal food of Rhizoma Polygonati for treatment of COVID-19 analyzed by network pharmacology: Viral and cancer signaling mechanisms. J. Funct. Foods 77, 104149. (2021).

    CAS  Article  Google Scholar 

  15. 15.

    Sizhen, G. et al. Mechanisms of Indigo naturalis on treating ulcerative colitis explored by GEO gene chips combined with network pharmacology and molecular docking. Sci Rep 10(1), 15204 (2020).

    Article  CAS  Google Scholar 

  16. 16.

    Sakle Nikhil, S., More Shweta, A. & Mokale Santosh, N. A network pharmacology-based approach to explore potential targets of Caesalpinia pulcherima: An updated prototype in drug discovery. Sci. Rep. 10(1), 17217. (2020).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 28, 1947–1951 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Kanehisa, M., Furumichi, M., Sato, Y., Ishiguro-Watanabe, M. & Tanabe, M. KEGG: Integrating viruses and cellular organisms. Nucleic Acids Res. 49, D545–D551 (2021).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  20. 20.

    Carsten, N. et al. Validation of the graded prognostic assessment for gastrointestinal cancers with brain metastases (GI-GPA). Radiat Oncol. 15, 35 (2020).

    Article  Google Scholar 

  21. 21.

    Tang, X. et al. SIX4 acts as a master regulator of oncogenes that promotes tumorigenesis in non-small-cell lung cancer cells. Biochem. Biophys. Res. Commun. 516(3), 851–857 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  22. 22.

    Xiang, Y., Guo, Z., Zhu, P., Chen, J. & Huang, Y. Traditional Chinese medicine as a cancer treatment: Modern perspectives of ancient but advanced science. Cancer Med. 8(5), 1958–1975 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Yan, Z., Lai, Z. & Lin, J. Anticancer properties of traditional Chinese medicine. Comb. Chem. High Throughput Screen. 20(5), 423–429 (2017).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  24. 24.

    Shin, M. S., Hwang, S. H., Yoon, T. J., Kim, S. H. & Shin, K. S. Polysaccharides from ginseng leaves inhibit tumor metastasis via macrophage and NK cell activation. Int. J. Biol. Macromol. 103, 1327–1333. (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Lee, Y. S. et al. Activation of multiple effector pathways of immune system by the antineoplastic immunostimulator acidic polysaccharide ginsan isolated from Panax ginseng. Anticancer Res. 17(1A), 323–331 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Wang, Y., Huang, M., Sun, R. & Pan, L. Extraction, characterization of a Ginseng fruits polysaccharide and its immune modulating activities in rats with Lewis lung carcinoma. Carbohydr. Polym. 127, 215–221 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  27. 27.

    Honglin, L. et al. Modulation the crosstalk between tumor-associated macrophages and non-small cell lung cancer to inhibit tumor migration and invasion by ginsenoside Rh2. BMC Cancer 18, 579 (2018).

    Article  CAS  Google Scholar 

  28. 28.

    Kryston, T. B., Georgiev, A. B., Pissis, P. & Georgakilas, A. G. Role of oxidative stress and DNA damage in human carcinogenesis. Mutat. Res. 711, 193–20129 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  29. 29.

    Loke, M. F., Ng, C. G., Vilashni, Y., Lim, J. & Ho, B. Understanding the dimorphic lifestyles of human gastric pathogen Helicobacter pylori using the SWATH-based proteomics approach. Sci. Rep. 6, 26784 (2016).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Jianhui, L. et al. LPS induced miR-181a promotes pancreatic cancer cell migration via targeting PTEN and MAP2K4. Dig. Dis. Sci. 59, 1452–1460 (2014).

    Article  CAS  Google Scholar 

  31. 31.

    Wenwen, Z., Guixin, Ma. & Xiuping, C. Lipopolysaccharide induced LOX-1 expression via TLR4/MyD88/ROS activated p38MAPK-NF-κB pathway. Vascul. Pharmacol. 63, 162–172 (2014).

    Article  CAS  Google Scholar 

  32. 32.

    Scrima, M. et al. Signaling networks associated with AKT activation in non-small cell lung cancer (NSCLC): New insights on the role of phosphatydil-inositol-3 kinase. PLoS ONE 7, 30427 (2012).

    ADS  Article  CAS  Google Scholar 

  33. 33.

    Fumarola, C., Bonelli, M. A., Petronini, P. G. & Alfieri, R. R. Targeting PI3K/AKT/mTOR pathway in non small cell lung cancer. Biochem. Pharmacol. 90(3), 197–207.,11(2014) (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Zhang, Y. X., Yuan, J., Gao, Z. M. & Zhang, Z. G. LncRNA TUC338 promotes invasion of lung cancer by activating MAPK pathway. Eur. Rev. Med. Pharmacol. Sci. 22(2), 443–449 (2018).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Wang, Y. et al. Xiaoai Jiedu recipe inhibits proliferation and metastasis of non-small cell lung cancer cells by blocking the P38 mitogen-activated protein kinase (MAPK) pathway. Med. Sci. Monit. 25, 7538–7546. (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Wu, Z., He, D., Zhao, S. & Wang, H. IL-17A/IL-17RA promotes invasion and activates MMP-2 and MMP-9 expression via p38 MAPK signaling pathway in non-small cell lung cancer. Mol. Cell Biochem. 455(1–2), 195–206. (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Bei, Li. et al. Epigenetic silencing of CDKN1A and CDKN2B by SNHG1 promotes the cell cycle, migration and epithelial-mesenchymal transition progression of hepatocellular carcinoma. Cell Death Dis. 11, 823 (2020).

    Article  CAS  Google Scholar 

  38. 38.

    Irina, V. & O’Dwyer Peter, J. Role of Jun and Jun kinase in resistance of cancer cells to therapy. Drug Resist. Update 6, 147–156 (2003).

    Article  CAS  Google Scholar 

  39. 39.

    Ie, L. et al. Expression and prognostic significance of the P53-related DNA damage repair proteins checkpoint kinase 1 (CHK1) and growth arrest and DNA-damage-inducible 45 alpha (GADD45A) in human oral squamous cell carcinoma. Eur. J. Oral Sci. 128(2), 128–135 (2020).

    Article  CAS  Google Scholar 

  40. 40.

    Xiang, X., You, X. M. & Li, L. Q. Expression of HSP90AA1/HSPA8 in hepatocellular carcinoma patients withdepression. Onco Targets Ther. 11, 3013–3023 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Li, Ma. et al. Lysyl oxidase 3 is a dual-specificity enzyme involved in STAT3 deacetylation and deacetylimination modulation. Mol. Cell. 65, 296–309 (2017).

    Article  CAS  Google Scholar 

  42. 42.

    Guanizo Aleks, C. et al. STAT3: A multifaceted oncoprotein. Growth Factors 36, 1–14 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  43. 43.

    Yu, H., Kortylewski, M. & Pardoll, D. Crosstalk between cancer and immune cells: Role of STAT3 in the tumour microenvironment. Nat. Rev. Immunol. 7, 11 (2007).

    Article  CAS  Google Scholar 

  44. 44.

    Yu, H., Pardoll, D. & Jove, R. STATs in cancer inflammation and immunity: A leading role for STAT3. Nat. Rev. Cancer. 9(11), 798–809 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Harada, D., Takigawa, N. & Kiura, K. The role of STAT3 in non-small cell lung cancer. Cancers 6(2), 708–722 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Zimmer, S. et al. Epidermal growth factor receptor mutations in non-small cell lung cancer influence downstream Akt, MAPK and Stat3 signaling. J. Cancer Res. Clin. Oncol. 135, 723–730 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  47. 47.

    Looyenga, B. D. et al. STAT3 is activated by JAK2 independent of key oncogenic driver mutations in non-small cell lung carcinoma. PLoS ONE 7, 30820 (2012).

    ADS  Article  CAS  Google Scholar 

  48. 48.

    Jiang, R. et al. Correlation of activated STAT3 expression with clinicopathologic features in lung adenocarcinoma and squamous cell carcinoma. Mol. Diagn. Ther. 15, 347–352 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  49. 49.

    Min, L. S. et al. Acquired resistance to EGFR targeted therapy in non-small cell lung cancer: Mechanisms and therapeutic strategies. Cancer Treat Rev. 65, 1–10 (2018).

    Article  CAS  Google Scholar 

  50. 50.

    Lui, V. W. & Grandis, J. R. EGFR-mediated cell cycle regulation. Anticancer Res. 22, 1-11.33 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Shelton, J. G. et al. Conditional EGFR promotes cell cycle progression and prevention of apoptosis in the absence of autocrine cytokines. Cell Cycle 4, 822–830 (2005).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  52. 52.

    Xu, L. et al. New microtubulin inhibitor MT189 suppresses angiogenesis via the JNK-VEGF/VEGFR2 signaling axis. Cancer Lett. 416(1), 57–65 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Jianming, L. et al. Cyclin D1 G870A polymorphism and lung cancer risk: A meta-analysis. Tumour Biol. 33, 1467–1476 (2012).

    Article  CAS  Google Scholar 

  54. 54.

    Balkwill, F. TNF-alpha in promotion and progression of cancer. Cancer Metastasis Rev. 25(3), 409–416 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  55. 55.

    Chen, J., Gusdon, A. M., Piganelli, J., Leiter, E. H. & Mathews, C. E. mt-Nd2(a) modifies resistance against autoimmune type 1 diabetes in NOD mice at the level of the pancreatic β-cell. Diabetes 60(1), 355–359. (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Boyuan, Y. et al. MAPK signaling pathways in eye wounds: Multifunction and cooperation. Exp. Cell Res. 359, 10–16 (2017).

    Article  CAS  Google Scholar 

  57. 57.

    Chow, C. Y., Wang, X., Riccardi, D., Wolfner, M. F. & Clark, A. G. The genetic architecture of the genome-wide transcriptional response to ER stress in the mouse. PLoS Genet. 11(2), 1004924 (2015).

    Article  CAS  Google Scholar 

  58. 58.

    Dangchi, V. MYC, metabolism, cell growth, and tumorigenesis. Cold Spring Harb. Perspect. Med. 3, 14217 (2013).

    Article  CAS  Google Scholar 

  59. 59.

    Rapp, U. R. et al. MYC is a metastasis gene for non-small-cell lung cancer. PLoS ONE 4, 6029 (2009).

    ADS  Article  CAS  Google Scholar 

  60. 60.

    Georgakilas, A. G., Martin, O. A. & Bonner, W. M. P21: A two-faced genome guardian. Trends Mol. Med. 23, 310–319 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  61. 61.

    Alice, Z. et al. CDKN1A upregulation and cisplatin-pemetrexed resistance in non-small cell lung cancer cells. Int. J. Oncol. 56, 1574–1584 (2020).

    Google Scholar 

  62. 62.

    Jing, L. et al. Changes in plasma NPY, IL-1β and hypocretin in people who died by suicide. Neuropsychiatr. Dis. Treat. 15, 2893–2900 (2019).

    Article  Google Scholar 

  63. 63.

    Elaraj, D. M. et al. The role of interleukin 1 in growth and metastasis of human cancer xenografts. Clin. Cancer Res. 12(4), 1088–1096 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  64. 64.

    Ardizzoni, A. et al. Biologic and clinical effects of continuous infusion interleukin-2 in patients with non-small cell lung cancer. Cancer 73(5), 1353–1360 (1994).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  65. 65.

    Rosenberg, S. A., Yang, J. C., White, D. E. & Steinberg, S. M. Durability of complete responses in patients with metastatic cancer treated with high-dose interleukin-2: Identification of the antigens mediating response. Ann. Surg. 228, 307–319 (1998).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

    Mao, Q., Zhang, P. H., Wang, Q. & Li, S. L. Ginsenoside F(2) induces apoptosis in humor gastric carcinoma cells through reactive oxygen species-mitochondria pathway and modulation of ASK-1/JNK signaling cascade in vitro and in vivo. Phytomedicine 21(4), 515–522 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  67. 67.

    Mai, T. T. et al. Ginsenoside F2 induces apoptosis accompanied by protective autophagy in breast cancer stem cells. Cancer Lett. 321(2), 144–153 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  68. 68.

    Ru, J. et al. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform. 6, 13 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  69. 69.

    Daina, A., Michielin, O., & Zoete, V. Swiss Target Prediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucl. Acids Res. 47, W357–W364. (2019).

    CAS  Article  Google Scholar 

  70. 70.

    Wang, Y. et al. Therapeutic target database 2020: Enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Res. 48(1), 1031–1041 (2020).

    Google Scholar 

  71. 71.

    Stelzer, G. et al. The GeneCards Suite: From gene data mining to disease genome sequence analyses. Curr. Protoc. Bioinform. 54, 1–30 (2016).

    Article  Google Scholar 

  72. 72.

    Bardou, P., Mariette, J., Escudié, F., Djemiel, C. & Klopp, C. jvenn: An interactive Venn diagram viewer. BMC Bioinform. 15, 293 (2014).

    Article  Google Scholar 

  73. 73.

    Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: An R package for comparing biological themes among gene clusters. Omics J. Integr. Biol. 16, 284–287 (2012).

    CAS  Article  Google Scholar 

  74. 74.

    Xiaobo, Z. et al. Network pharmacology based virtual screening of active constituents of Prunella vulgaris L. and the molecular mechanism against breast cancer. Sci. Rep. 10, 15730 (2020).

    ADS  Article  CAS  Google Scholar 

  75. 75.

    Jingyuan, Z. et al. A bioinformatics investigation into molecular mechanism of Yinzhihuang granules for treating hepatitis B by network pharmacology and molecular docking verification. Sci. Rep. 10, 11448 (2020).

    Article  CAS  Google Scholar 

  76. 76.

    Xinqiang, S. et al. Prediction of triptolide targets in rheumatoid arthritis using network pharmacology and molecular docking. Int. Immunopharmacol. 80, 106179 (2020).

    Article  CAS  Google Scholar 

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This research was funded by the Scientific Research Foundation of Heilongjiang University of Chinese Medicine and Science and Technology Funded Project for Overseas Scholars and Teachers of Education Department of Heilongjiang Province (Grant number 2018jkcy01 and 1053HQ012).

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L.Z.H. wrote the manuscript; Y.D. and H.N.N. collected the data; W.J.K. performed the data analyses; D.X.W. and W.X.J. directed the research and proposed changes to the manuscript, and all authors reviewed the manuscript and approved the final version of the manuscript.

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Correspondence to Xiao-Wei Du or Xi-Jun Wang.

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Li, ZH., Yu, D., Huang, NN. et al. Immunoregulatory mechanism studies of ginseng leaves on lung cancer based on network pharmacology and molecular docking. Sci Rep 11, 18201 (2021).

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