Waiting
Login processing...

Trial ends in Request Full Access Tell Your Colleague About Jove

Medicine

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma

Published: March 3, 2023 doi: 10.3791/64847

Summary

This study reveals the mechanism of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma based on network pharmacology and experimental verification. The study also demonstrates that the PI3K/AKT signaling pathway plays a vital role in the action of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma.

Abstract

We aimed to study the mechanism of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma (LUAD) based on network pharmacology and experimental verification. The effective components and potential targets of Trichosanthis and Fritillaria thunbergii were collected by high-throughput experiment and reference-guided (HERB) database of traditional Chinese medicine and a similarity ensemble approach (SEA) database, and the LUAD-related targets were queried by the GeneCards and Online Mendelian Inheritance in Man (OMIM) databases. A drug-component-disease-target network was constructed by Cytoscape software. Protein-protein interaction (PPI) network, gene ontology (GO) function, and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were conducted to obtain core targets and key pathways. An aqueous extract of Trichosanthes-Fritillaria thunbergii and A549 cells were used for the subsequent experimental validation. Through the HERB database and literature search, 31 effective compounds and 157 potential target genes of Trichosanthes-Fritillaria thunbergii were screened, of which 144 were regulatory targets of Trichosanthes-Fritillaria thunbergii in the treatment of lung adenocarcinoma. The GO functional enrichment analysis showed that the mechanism of action of Trichosanthes-Fritillaria thunbergii against lung adenocarcinoma is mainly protein phosphorylation. The KEGG pathway enrichment analysis suggested that the treatment of lung adenocarcinoma by Trichosanthes-Fritillaria thunbergii mainly involves the PI3K/AKT signaling pathway. The experimental validation showed that an aqueous extract of Trichosanthes-Fritillaria thunbergii could inhibit the proliferation of A549 cells and the phosphorylation of AKT. Through network pharmacology and experimental validation, it was verified that the PI3K/AKT signaling pathway plays a vital role in the action of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma.

Introduction

Lung cancer refers to malignant tumors originating from the lung bronchial mucosa, including squamous cell carcinoma, adenocarcinoma, large cell carcinoma, and small cell carcinoma1. Lung adenocarcinoma (LUAD) is the most common type of lung cancer, accounting for about 40% of the total lung cancer cases2. Most patients are diagnosed at an advanced stage or have remote metastasis and, thus, lose the opportunity of surgery3. In current clinical treatment, concurrent chemoradiotherapy is the most common strategy for treating LUAD, but its application is limited due to serious adverse reactions4.

Traditional Chinese medicine (TCM) can effectively relieve the clinical symptoms of LUAD patients and reduce the adverse reactions caused by radiotherapy and chemotherapy and has, thus, become a research hotspot5,6,7. In traditional Chinese medicine, lung cancer belongs to the category of "lung accumulation" and "pulmonary petrous". The deficiency of Qi and the interaction of phlegm, stasis, and poison are important in the pathogenesis of lung cancer. Therefore, tonifying Qi and eliminating phlegm and blood stasis are the main clinical treatment8 methods for lung cancer according to TCM theory9. Trichosanthes kirilowii Maxim (Gualou) and Fritillaria thunbergii Miq (Zhebeimu) represent a common drug pair in treating lung cancer, and this combination has the effects of clearing heat and reducing phlegm10,11,12. However, its mechanism of action is still unclear, and further research needs to be conducted.

Network pharmacology is a comprehensive method based on the theory of systems biology and multidirectional pharmacology that aims to reveal complex network relationships between multiple drugs and diseases13. Traditional Chinese prescriptions have the characteristics of being multi-component and multi-target, meaning they are very suitable for the study of network pharmacology14,15. Recently, network pharmacology has emerged as a powerful approach in the study of TCM formulas and has become a research hotspot16,17.

However, to the best of our knowledge, all of the research on network pharmacology are presented as text. Presenting this technology through video will greatly reduce the learning threshold and facilitate the promotion of this technology, which is one of the advantages of this article. In this study, we took Trichosanthes-Fritillaria thunbergii against lung adenocarcinoma as an example to carry out network pharmacology prediction and experimental validation.

Subscription Required. Please recommend JoVE to your librarian.

Protocol

All the network pharmacology procedures were carried out in accordance with the Guidelines for Network Pharmacology Evaluation Methods18. All the experimental procedures were performed in accordance with the laboratory management regulations of the Beijing University of Chinese Medicine.

1. Network pharmacological prediction

  1. Selection of active components
    1. Open the HERB database (http://herb.ac.cn)19, and use "Gualou" (the Chinese name for Trichosanthes kirilowii Maxim) and "Zhebeimu" (the Chinese name for Bulbus Fritillariae thunbergii) as keywords to obtain the components of the two drugs. Download the list and the canonical SMILES structures of the related components of the two drugs.
    2. Determine whether the obtained component is the active component.
      1. Include as active components those that have oral bioavailability (OB) and drug-like (DL) values in the HERB group database (i.e., components with OB ≥ 30% and DL ≥ 0.18)20,21.
      2. If the component has no OB and DL values, input the component into the Swiss ADME database (http://www.swissadme.ch/index.php)22 to obtain the information on each component. Include components with “high” GI absorption and at least two “Yes” DL values as active components.​
  2. Target prediction of the active components
    1. Open the HERB database (http://herb.ac.cn). Search and copy the canonical SMILES structures of the active components.
    2. Open the SEA (Similarity ensemble approach, http://sea.bkslab.org) database23. Paste the canonical SMILES structures of the active components into the search box, and click on Try SEA to obtain the Target key, Target name, P-Value, and MaxTC of each active component.
    3. Copy the data into a spreadsheet, and use the filtering function of the spreadsheet file to filter the targets of the active components by Target key (Human, P < 0.05, and MaxTC > 0.5).
    4. Copy all the targets to a spreadsheet, and remove the duplicates to obtain the drug targets.
  3. Prediction of disease targets
    1. Open the GeneCards database (https://www.genecards.org)24 and Online Mendelian Inheritance in Man database (OMIM, https://www.omim.org)25 and use Lung Adenocarcinoma as the keyword to obtain the disease targets of the lung adenocarcinoma.
    2. Download the spreadsheets of the disease targets. Delete the repeated targets to obtain the LUAD targets.
  4. Construction of a drug-component-disease-target network
    1. Copy the LUAD-related targets and drug targets into the same column in a new spreadsheet. Use the Data - Identify Duplicates function in the toolbar to obtain intersection targets of the LUAD-related targets and the Trichosanthes-Fritillaria thunbergii active component-related targets.
    2. Open Cytoscape 3.8.0. Click on File in the menu bar, and then select Import > Network from File to import the spreadsheet file. Optimize the size and color of the network nodes through the style bar in the left control panel.
    3. Use the Analyze Network function for the network topology analysis. Click on Tools in the menu bar, and then select Analyze Network. On the Table panel, click on the Degree in the title bar to arrange the components by degree in descending order. Take the top ten components and targets as the main active components and core targets.
  5. Construction of the PPI network and screening of the core proteins
    1. Open the STRING database (https://cn.string-db.org/)26. Paste the text-format list of the potential targets of Trichosanthes-Fritillaria thunbergii against LUAD into the List of Names dialog box. Select Homo sapiens in Organisms, and click on the SEARCH > CONTINUE buttons.
    2. When the results are available, click on Settings, and tick High Confidence (0.700) in Basic Settings > Minimum Required Interaction Score. Tick Hide Disconnected Nodes in the Network in Advanced Settings, and then click on the UPDATE button.
    3. Click on Exports in the title bar, and download the short tabular text of the PPI relationship in TSV format.
    4. Open Cytoscape 3.8.0. Click on File > Import > Network from File to import the TSV format file for visual analysis.
    5. Use the Network Analyzer function to conduct the topological analysis. Optimize the size and color of the network nodes through the style bar in the left control panel.
  6. KEGG enrichment analysis
    1. Open the Metascape (https://metascape.org/)27 platform. Paste the text-format list of the potential therapeutic targets into the dialog box, and then click on the Submit button. Tick H. sapiens in both Input as Species and Analysis as Species, and then click on the Custom Analysis button. Select Enrichment, tick only the KEGG Pathway, and then click on Enrichment Analysis. After the progress bar reaches 100%, click on the Analysis Report Page orange button for the enrichment results.
    2. Click on All in One Zip File for the download of the enrichment result, and then open the _ FINAL_GO.csv file in the Enrichment_GO folder to obtain the result.
    3. Open R software (https://cran.r-project.org/). Type install.package ("ggplot2") and library (ggplot2) in R for the installation of the ggplot2 R package. Press Enter to run the KEGG visualization program.

2. Experimental verification

  1. Drug preparation
    NOTE: Fritillaria thunbergii Miq and Trichosanthes kirilowii Maxim were purchased from Dongzhimen Hospital, affiliated with the Beijing University of Chinese Medicine.
    1. Mix 50 g of Fritillaria thunbergii Miq and 50 g of Trichosanthes kirilowii Maxim together. Soak the mixture in 1 L of distilled water for 20 min, and then decoct the mixture at 100 °C under normal pressure for 1 h.
    2. Filter the decoction to obtain a filtrate with a double layer of sterile medical gauze, and then use 80 µm filter paper to further filter the extract. Repeat the above operation three times.
    3. Mix the filtrate together to obtain an approximately 1.2 L decoction. Place the solution in the flask of a rotary evaporator. Set the rotation speed to 50 rpm with a temperature of 37 °C. Mix and condense the decoctions into an extract in the rotary evaporator for 6 h to yield a viscous liquid.
    4. Turn on the vacuum freeze-drying mechanism to precool the temperature to −40 °C. Put the obtained extract into the material tray, and place it into the cold trap for freezing.
    5. When the cold trap temperature reaches −50 °C and the material has been frozen for 2 h, transfer the frozen materials to a drying rack placed in the cold trap. Turn on the vacuum pump to initiate the lyophilization process. Take out the freeze-dried powder, and store this in the refrigerator at −20 °C for later use.
  2. Cell cultivation
    1. Configure DMEM complete medium with 89% DMEM basic medium, 10% fetal bovine serum, and 1% penicillin-streptomycin.
    2. After removing the A549 cells from liquid nitrogen, incubate the cells in a 37 °C water bath, and stir until the medium in the vial melts.
    3. Add a fourfold volume of DMEM complete medium into the melted cells. Centrifuge the cells (4 °C, 679 x g, 5 min), and discard the supernatant.
    4. Resuspend the precipitated cells with 6 mL of DMEM complete medium, plate them in a T25 culture flask, and incubate the flask in a cell incubator at 37 °C, 5% CO2.
  3. Detection of cell viability
    1. Digest the logarithmic growth-phase A549 cells with 1 mL of 0.25% trypsin for 1 min at 37°C. Add 1 mL of DMEM complete medium to neutralize the trypsin, and gently blow it to promote cell shedding. Centrifuge the mixture to obtain the cell pellet (4 °C, 192 x g, 5 min). Resuspend the obtained cells using DMEM complete medium.
    2. Add the cell suspension to a hemocytometer, and count using an automated cell counter. Dilute it to 5 x 104 cells/mL using DMEM complete medium.
    3. Dissolve 1 g of Trichosanthes-Fritillaria thunbergii water extract in 10 mL of PBS solution, and filter-sterilize it through a 0.22 µm filter. Dilute the mixture to 90 mg/mL, 80 mg/ml, 70 mg/mL, 60 mg/mL, 50 mg/mL, 40 mg/mL, 30 mg/mL, 20 mg/mL, and 10 mg/mL using PBS.
    4. Plate the diluted cells on 96-well plates with 100 µL per well. After cell adherence, add 1 µL of Trichosanthes-Fritillaria thunbergii water extracts of different concentrations to adjust the concentration of each well to 900 µg/mL, 800 µg/mL, 700 µg/mL, 600 µg/mL, 500 µg/mL, 400 µg/mL, 300 µg/mL, 200 µg/mL, and 100 µg/mL.
    5. Discard the original medium after 24 h of culture, and add 100 µL of DMEM basic medium for further incubation for 2 h at 37 °C, 5% CO2.
    6. After the above treatment, add 20 µL of MTS solution (Table of Materials), and incubate the cells for another 1 h at 37 °C, 5% CO2.
    7. Transfer the incubated mixture to a different plate. Measure the absorbance (OD) at a 490 nm wavelength using a microplate reader. Calculate the cell viability using the following formula:
      Viability (%) = 100 × (OD of the treated sample − OD of the medium)/(OD of the control sample − OD of the medium).
      NOTE: A minimum concentration close to IC50 is defined as a high dose. The concentration at which the cell proliferation begins to be inhibited is defined as a low-dose concentration, and the intermediate value is defined as a medium-dose concentration for subsequent experimental studies. In this study, 400 µg/mL, 600 µg/mL, and 800 µg/mL were used as the low, medium, and high doses.
  4. Drug intervention and sample collection
    NOTE: Cell growth was plotted against time to determine the log phase.
    1. Dilute the logarithmic growth-phase A549 cells to 5 x 105 cells/mL. Add 2 mL of the cell suspension to a 6-well plate, and grow for 12 h.
    2. Repeat step 2.3.3 to obtain 80 mg/mL, 60 mg/mL, and 40 mg/mL PBS dilutions of Trichosanthes-Fritillaria thunbergii water extract. Add 20 µL of the PBS solution, 20 µL of the 40 mg/mL Trichosanthes-Fritillaria thunbergii water extract, 20 µL of the 60 mg/mL Trichosanthes-Fritillaria thunbergii water extract, and 20 µL of the 80 mg/mL Trichosanthes-Fritillaria thunbergii water extract to the blank control group, low concentration group, medium concentration group, and high concentration group, respectively. Discard the supernatant after 24 h of intervention, and clean the cells with PBS three times.
      NOTE: The concentrations of the blank control group, low concentration group, medium concentration group, and high concentration group of Trichosanthes-Fritillaria thunbergii water extract were 0 µg/mL, 400 µg/mL, 600 µg/mL, and 800 µg/mL, respectively.
    3. Add 250 µL of RIPA buffer (containing 1% PMSF and 1% phosphatase inhibitor) to each well, and lyse the cells for 30 min. Collect the lysate for centrifugation (4 °C, 6,714 x g, 10 min), and obtain the supernatant.
  5. Western blotting
    1. Detect the protein concentration of the samples by the BCA method according to the manufacturer's instructions. Use RIPA buffer to adjust the concentration of each sample to be consistent.
    2. Mix 40 µL of the protein sample with 10 µL of 5x loading buffer, and boil for 5 min at 100 °C. Centrifuge (4 °C, 6,714 x g, 10 min) the mixture to obtain the supernatant for subsequent experiments.
    3. Isolate the proteins by gel electrophoresis using 120 V vertical electrophoresis.
    4. Prepare an electrical "sandwich" (sponge - filter paper - gel - PVDF membrane - filter paper - sponge). Soak the transfer apparatus in an ice bath, and transfer the protein onto PVDF membranes at 70 V for 60 min.
    5. Use 100 mL of TBST solution (0.05% [v/v] Tween-20, 10 mM Tris, 150 mM NaCl, pH 7.5) and 5 g of skimmed milk powder to configure 5% milk. Pre-dilute the AKT and p-AKT antibodies with the antibody diluent in a ratio of 1:1,000.
    6. Block the PVDF membrane in 5% skimmed milk powder for 1 h with shaking. After blocking, discard the blocking milk, and add the diluted primary antibody for overnight incubation at 4 °C.
    7. After removing the primary antibody, use TBST solution to wash the PVDF membrane five times for 5 min each.
    8. Pre-dilute the secondary antibody with the antibody diluent in a ratio of 1:5,000. Add the secondary antibody, and incubate at room temperature (RT) for 1.5 h. After removing the secondary antibody, use TBST solution to wash the PVDF membrane five times for 5 min each.
    9. Detect the protein using a chemiluminescence detection system.

3. Molecular docking

  1. Open the UniProt database (https://www.uniprot.org)28. Type the target symbols into the search box, and click on the SEARCH button. In the Structure subheading, click on Download for the 3D structures of the protein targets.
  2. Open the RCSB PDB database (https://www.rcsb.org/pdb/home/sitemap.do)29. Type the name of the protein macromolecules into the search box. Download the protein macromolecule structures in PDB format.
  3. Download the installation package of UCSF Chimera 1.16 software (https://vina.scripps.edu/) and AutoDock Vina (https://vina.scripps.edu/). Install and open the UCSF Chimera 1.16 software. Click on File > Open to display the receptor protein.
  4. Click on Tools > Structure Editing > Dock Prep, and tick Delete Solvent, Add Hydrogens, and Add Charges in the popover to remove water and add hydrogenation and balance charges. Click on Write Mol2 File to save the receptor protein in mol2 format. Perform the same step on the ligand.
  5. Click on File > Open to display the mol2-format ligand in the UCSF Chimera 1.16 software. In Tools > Surface/Binding Analysis > AutoDock Vina, set the Receptor bar to the name of the receptor protein and the Ligand bar to the name of the ligand. Enter a value in the box behind Center and Size to adjust the newly developed space, making it possible to fully encompass the ligand and the receptor.
  6. Click on OK for the molecular docking virtual screening to obtain the optimal location for ligand binding to the receptor. Record the binding energy value at the optimal position.

4. Statistical analysis

  1. Open SPSS 26.0 statistical software. Click on File > Import Data for data loading.
  2. Present the experimental data as mean ± standard deviation.
  3. Compare multiple groups using a one-way ANOVA.
    NOTE: P < 0.05 was considered statistically significant in this work.

Subscription Required. Please recommend JoVE to your librarian.

Representative Results

A total of 31 Trichosanthes-Fritillaria thunbergii-related active components were identified, including 21 Trichosanthes and 10 Fritillaria thunbergia components, as well as 144 corresponding targets. Overall, 9,049 and 67 LUAD-related genes were extracted from the GeneCards database and the OMIM database, respectively. After deleting duplicated genes, 9,057 genes related to LUAD were identified. The intersection of the LUAD-related genes and Trichosanthes-Fritillaria thunbergii active component-related targets was conducted to obtain potential therapeutic targets. The drug-component-disease-target interaction network of Trichosanthes-Fritillaria thunbergii against LUAD is shown in Figure 1A. In the interaction network, the top ten active components were kaempferol, hydroxygenkwanin, genistein, diosmetin and β-sitosterol, palmitoleicacid, mandenol, hexadecanoic acid, carproic acid, and capric acid, which were identified as the key active components of the action of Trichosanthes-Fritillaria thunbergii in treating LUAD (Figure 1B). The PPI network included 122 functional proteins and 210 interaction relationships, and the visualization results are shown in Figure 2A. The top ten core proteins by degree (parameter used for visualization analysis in Cytoscape software) in descending order included ESR1, VEGFA, PPARA, CYP3A4, AR, APP, FGF2, CREB1, and CYP1A1, which are mainly involved in neovascularization, cell proliferation, apoptosis, and cell membrane transport30,31,32,33,34,35,36,37,38,39(Figure 2B). Of the top 20 pathways ranked by KEGG, the PI3K/AKT signaling pathway40, Rap1 signaling pathway41, phospholipase D signaling pathway42, and MAPK1 signaling pathway43 are closely associated with lung cancer, among which the PI3K/AKT pathway ranked the first and, thus, was used for subsequent verification (Figure 3).

The experiments indicated that Trichosanthes-Fritillaria thunbergii extracts at concentrations over 400 µg/mL could inhibit cell proliferation, and the inhibition effect on A549 cells at concentrations up to 800 µg/mL was close to the half inhibitory concentration (IC50) (Figure 4). Thus, 400 µg/mL, 600 µg/mL, and 800 µg/mL were used as the low, medium, and high doses for the subsequent experiments. The intervention of Trichosanthes-Fritillaria thunbergii extracts caused no significant change in AKT protein expression in each group; however, the expression of p-AKT (Ser473) was inhibited and showed a dose-dependent effect (Figure 5). The key components of Trichosanthes-Fritillaria thunbergii in LUAD treatment were molecularly docked with the key proteins of the PI3K/AKT pathway, and the results suggested the binding energies of diosmetin and kaempferol with AKT1 were less than −7, indicating strong binding activity44 (Figure 6).

Figure 1
Figure 1: Diagram of the drug-component-disease-target network. (A) Blue represents the disease; yellow represents the drug; red represents the component; green represents the target. Abbreviations: GL = Trichosanthes; ZBM = Fritillaria thunbergia; LUAD = lung adenocarcinoma. (B) Top ten active ingredients in the network ordered by degree in descending order. Please click here to view a larger version of this figure.

Figure 2
Figure 2: PPI network of Trichosanthes-Fritillaria thunbergii in the treatment of LUAD. (A) Visualization results of the PPI network. The darker the node, the more central the protein is in the network. (B) Top ten targets in the network by degree. Please click here to view a larger version of this figure.

Figure 3
Figure 3: KEGG pathway enrichment of Trichosanthes-Fritillaria thunbergii targets against LUAD. (A) The top 20 KEGG pathways are ranked according to the P-values in ascending order. (B) Map of the PI3K/AKT signaling pathway. Please click here to view a larger version of this figure.

Figure 4
Figure 4: Effects of different concentrations of Trichosanthes-Fritillaria thunbergii extract on A549 cell proliferation (n = 3). Trichosanthes-Fritillaria thunbergii extracts at concentrations over 400 µg/mL could inhibit cell proliferation. The inhibition effect on A549 cells at concentrations up to 800 µg/mL was close to the half inhibitory concentration (IC50). Abbreviation: GL-ZBM = Trichosanthes-Fritillaria thunbergii. Please click here to view a larger version of this figure.

Figure 5
Figure 5: Effects of different concentrations of Trichosanthes-Fritillaria thunbergii extract on AKT protein expression and phosphorylation levels (n = 3). There was no significant change in AKT protein expression in each group, while the protein expression of p-AKT (Ser473) in the medium and high dose groups was significantly down-regulated, and the difference was statistically significant when compared with the control group (*P < 0.05 versus control group). Abbreviation: GL-ZBM = Trichosanthes-Fritillaria thunbergii. Please click here to view a larger version of this figure.

Figure 6
Figure 6: Molecular docking of the related components with core proteins. (A) Heatmap of the binding energy of key components of Trichosanthes-Fritillaria thunbergii for LUAD treatment molecularly docked with key proteins of the PI3K/AKT pathway. (B) Molecular docking diagram of Diosmetin and AKT1 proteins. (C) Molecular docking diagram of kaempferol and AKT1 proteins.The pink lines represent hydrogen bonds, the gray structures represent drug compositions, and the colored structure represents AKT1. Please click here to view a larger version of this figure.

Subscription Required. Please recommend JoVE to your librarian.

Discussion

Generally, a complete network pharmacology study includes the identification of active components from databases, the acquisition of targets corresponding to active components and diseases, the construction of a drug-component-disease-target network, and the prediction of core targets and pathways. The association between active components and core proteins (molecular docking) is preliminarily predicted by computer technology, and the final verification is conducted using an experiment.

The selection of relevant databases is the most critical part of network pharmacology, as this determines the quality of the research. The HERB database integrates information from several TCM databases, such as Syµmap, TCMID, TCMSP, and TCM-ID, and is the most comprehensive TCM and ingredient database at present. Therefore, in this study, the HERB database was used for the screening of the active components19.

In this study, 31 active components and 144 intersection targets of Trichosanthes-Fritillaria thunbergii in treating LUAD were identified through related databases. By constructing a drug-component-disease-target network, we explored ten key active components through topological analysis. The core targets of Trichosanthes-Fritillaria thunbergii in the treatment of LUAD were screened out through PPI analysis.

Among the top 20 pathways from the KEGG results, the PI3K/AKT signaling pathway, MAPK1 signaling pathway, Rap1 signaling pathway, and PPAR signaling pathway are closely related to tumors according to the literature40,41,42. MAPK1, a classic cancer-related signaling pathway, is an important regulator of cell growth and differentiation. When activated, this signaling pathway leads to uncontrolled cell proliferation, cell cycle extension, and tumor occurrence and development45. The Rap1 signaling pathway is an important regulator of the NF-κB signaling pathway and MAPK1 signaling pathway and is closely related to cell adhesion in lung cancer46. Additionally, it has been confirmed that inhibiting the Rap1 signaling pathway can improve tumor metastasis in lung carcinoma47. The PPAR signaling pathway is associated with cell proliferation, energy homeostasis, tumorigenesis, and metabolic disorders48. Studies have confirmed its function in promoting tumor angiogenesis and tumor growth49.

The PI3K/AKT signaling pathway mainly affects the metabolism, proliferation, apoptosis, and vascularization of tumors through the phosphorylation and activation of AKT. Previous studies have shown that the PI3K/AKT signaling pathway plays a regulatory role in the MAPK1 signaling pathway, Rap1 signaling pathway, and PPAR signaling pathway50,51,52. Moreover, the KEGG prediction results showed that the PI3K/AKT signaling pathway was most closely related to the action of Trichosanthes-Fritillaria thunbergii against LUAD, so it was selected for subsequent experimental verification.

The PPI-predicted core targets, VEGFA and CREB1 in the PI3K/AKT signaling pathway, as well as the key proteins PI3K and AKT1 in the PI3K/AKT signaling pathway, were included for the molecular docking. In a previous study, a docking binding energy less than −7 was considered significant44. The results of this study suggested that the binding energy of kaempferol and diosmetin with AKT1 exceeded this threshold, suggesting that the effects of these two components on AKT may be the key to the action of Trichosanthes-Fritillaria thunbergii against LUAD.

In further experimental verification, we investigated the effect of Trichosanthes-Fritillaria thunbergii extract on the phosphorylation level of AKT. The results showed that different concentrations of Trichosanthes-Fritillaria thunbergii extract had no significant effect on the expression of the AKT protein, but Trichosanthes-Fritillaria thunbergii extract could significantly inhibit the phosphorylation of AKT protein in a dose-dependent manner. These results suggest that the inhibition of the PI3K/AKT pathway is the key mechanism of action of Trichosanthes-Fritillaria thunbergii against LUAD.

In conclusion, through network pharmacology and experimental validation, this study has verified that the PI3K/AKT signaling pathway plays a vital role in the action of Trichosanthes-Fritillaria thunbergii in the treatment of LUAD.

There are still some shortcomings in this study. The study only included high-bioavailability active components without discussing the possible biotransformation of active molecules in the colon, intestinal cells, and liver, which is not comprehensive enough. In addition, the effect of Trichosanthes-Fritillaria thunbergii on the proliferation and PI3K/AKT pathway of lung adenocarcinoma cells was only experimentally verified in vitro. This study provides a theoretical basis for developing new drugs and expanding clinical applications. In the future, further experimental validations of these prediction results will be performed to support assessments of potential clinical applications.

Subscription Required. Please recommend JoVE to your librarian.

Disclosures

The authors have no conflicts of interest to declare.

Acknowledgments

This study was supported by the Innovation Training Program of Beijing University of Chinese Medicine (No: 202110026036).

Materials

Name Company Catalog Number Comments
0.25% trypsin-EDTA Gibco R001100
A549 cell line Procell CL-0016
AKT antibody CST 4691S
BCA Protein Assay Kit Solarbio PC0020
Chemiluminescence detection system Shanghai Qinxiang Scientific Instrument Factory ChemiScope 6100
Dulbecco's modified eagle medium (DMEM) Solarbio 11995
Enhanced chemiluminescence (ECL) kit  ABclonal RM00021
Fetal bovine serum ScienCell 0025
HRP Goat Anti-Rabbit IgG (H+L) ABclonal AS014
MTS assay kit Promega G3580
p-AKT antibody CST 6040S
Penicillin streptomycin Gibco C14-15070-063
Phenylmethanesulfonyl fluoride (PMSF) Solarbio P0100
Phosphatase inhibitor Beyotime P1081
Phosphate buffered saline (PBS) Solarbio P1020
Polyvinylidene difluoride (PVDF) membranes Millipore ISEQ00010
RIPA lysis solution Solarbio R0010
Rotary evaporator Shanghai Yarong Biochemical Instrument Factory RE52CS-1
Vacuum freeze-drying mechanism Ningbo Scientz Biotechnology SCIENTZ-10
β-Actin antibody ABclonal AC026

DOWNLOAD MATERIALS LIST

References

  1. Thai, A. A., Solomon, B. J., Sequist, L. V., Gainor, J. F., Heist, R. S. Lung cancer. The Lancet. 398 (10299), 535-554 (2021).
  2. Sinha, A., et al. Early-stage lung adenocarcinoma MDM2 genomic amplification predicts clinical outcome and response to targeted therapy. Cancers. 14 (3), 708 (2022).
  3. Howlader, N., et al. The effect of advances in lung-cancer treatment on population mortality. The New England Journal of Medicine. 383 (7), 640-649 (2020).
  4. Hirsch, F. R., et al. Lung cancer: Current therapies and new targeted treatments. The Lancet. 389 (10066), 299-311 (2017).
  5. Liu, J., et al. Comprehensive treatment with Chinese medicine in patients with advanced non-small cell lung cancer: A multicenter, prospective, cohort study. Chinese Journal of Integrative Medicine. 23 (10), 733-739 (2016).
  6. Xiao, Z. W., et al. Comprehensive TCM treatments combined with chemotherapy for advanced non-small cell lung cancer: A randomized, controlled trial. Medicine. 100 (18), 25690 (2021).
  7. Li, Y., et al. Effectiveness of traditional Chinese medicine on chemoradiotherapy induced leukaemia in patients with lung cancer: A meta-analysis. Journal of Traditional Chinese Medicine. 38 (5), 661-667 (2018).
  8. Yuan, F., et al. Therapeutic effect and apoptosis mechanism of lung-tonifying and expectorant decoction on lung cancer rats with Qi deficiency and blood stasis. Asian Pacific Journal of Tropical Medicine. 8 (11), 983-988 (2015).
  9. Zhang, Y. L., Liang, Y. E., He, C. W. Anticancer activities and mechanisms of heat-clearing and detoxicating traditional Chinese herbal medicine. Chinese Medicine. 12, 20 (2017).
  10. Wang, T. B., et al. Exploring the rules of application of RONG Yuan-ming in the treatment of non-small cell lung cancer. Guiding Journal of Traditional Chinese Medicine and Pharmacy. 25 (14), 22-25 (2019).
  11. Chen, T. T., Wang, Y., Tian, T. Medication regularity and mechanism of traditional Chinese medicine in treating lung cancer. Chinese Journal of Experimental Traditional Medical Formulae. 24 (11), 206-210 (2018).
  12. Shen, C. J. Analysis of the rule of Chinese medicine in treating lung cancer. Journal of Shandong University of Traditional Chinese Medicine. 35 (2), 127-129 (2011).
  13. Yang, X. Y., et al. Evidence-based complementary and alternative medicine bioinformatics approach through network pharmacology and molecular docking to determine the molecular mechanisms of Erjing pill in Alzheimer's disease. Experimental and Therapeutic Medicine. 22 (5), 1252 (2021).
  14. Chen, G. Y., et al. Network pharmacology analysis and experimental validation to investigate the mechanism of total flavonoids of Rhizoma Drynariae in treating rheumatoid arthritis. Drug Design Development and Therapy. 16, 1743-1766 (2022).
  15. Chen, G. Y., et al. Integrating network pharmacology and experimental validation to explore the key mechanism of Gubitong recipe in the treatment of osteoarthritis. Computational and Mathematical Methods in Medicine. 2022, 7858925 (2022).
  16. Xie, G. G., et al. A network pharmacology analysis to explore the effect of Astragali Radix-Radix Angelica Sinensis on traumatic brain injury. BioMed Research International. 2018, 3951783 (2018).
  17. Chen, G. Y., et al. Prediction of Rhizoma Drynariae targets in the treatment of osteoarthritis based on network pharmacology and experimental verification. Evidence Based Complementary and Alternative Medicine. 2021, 5233462 (2021).
  18. World Federation of Chinese Medicine Societies. Network pharmacology evaluation methodology guidance. World Chinese Medicine. 16 (4), 527-532 (2021).
  19. Fang, S. S., et al. A high-throughput experiment- and reference-guided database of traditional Chinese medicine. Nucleic Acids Research. 49, 1197-1206 (2021).
  20. Chen, G. Y., et al. Network pharmacology-based strategy to investigate the mechanisms of Cibotium barometz in treating osteoarthritis. Evidence-Based Complementary and Alternative Medicine. 2022, 1826299 (2022).
  21. Yu, J. H., et al. ZiYinHuaTan recipe inhibits cell proliferation and promotes apoptosis in gastric cancer by suppressing PI3K/AKT pathway. BioMed Research International. 2020, 2018162 (2020).
  22. Daina, A., Michielin, O., Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports. 7, 42717 (2017).
  23. Keiser, M. J., et al. Relating protein pharmacology by ligand chemistry. Nature Biotechnology. 25 (2), 197-206 (2007).
  24. Safran, M., et al. GeneCards Version 3: The human gene integrator. Database. 2010, (2010).
  25. Amberger, J. S., Hamosh, A. Searching Online Mendelian Inheritance in Man (OMIM): A knowledgebase of human genes and genetic phenotypes. Current Protocols in Bioinformatics. 58, 1-12 (2017).
  26. Mering, C. V., et al. STRING: Known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Research. 33, 433-437 (2005).
  27. Zhou, Y. Y., et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nature Communications. 10, 1523 (2019).
  28. Pundir, S., et al. UniProt protein knowledgebase. Methods in Molecular Biology. 1558, 41-55 (2017).
  29. Burley, S. K., et al. Protein data bank (PDB): The single global macromolecular structure archive. Methods in Molecular Biology. 1607, 627-641 (2017).
  30. Welsh, L. C., Welsh, M. VEGFA and tumour angiogenesis. Journal of Internal Medicine. 273 (2), 114-127 (2013).
  31. Hsu, L. H., Chu, N. M., Kao, S. H. Estrogen, estrogen receptor and lung cancer. International Journal of Molecular Sciences. 18 (8), 1713 (2017).
  32. Atmaca, A., et al. SNAI2/SLUG and estrogen receptor mRNA expression are inversely correlated and prognostic of patient outcome in metastatic non-small cell lung cancer. BMC Cancer. 15, 300 (2015).
  33. Lakshmi, S. P., Reddy, A. T., Banno, A., Reddy, R. C. PPAR agonists for the prevention and treatment of lung cancer. PPAR Research. 2017, 8252796 (2017).
  34. Oguro, A., Sakamoto, K., Funae, Y., Imaoka, S. Overexpression of CYP3A4, but not of CYP2D6, promotes hypoxic response and cell growth of Hep3B cells. Drug Metabolism and Pharmacokinetics. 26 (4), 407-415 (2011).
  35. Jamroze, A., Chatta, G., Tang, D. G. Androgen receptor (AR) heterogeneity in prostate cancer and therapy resistance. Cancer Letters. 518, 1-9 (2021).
  36. Wu, Y. I., et al. Regulation of global gene expression and cell proliferation by APP. Scientific Reports. 6, 22460 (2016).
  37. Sedlář, A., et al. Growth factors VEGF-A 165 and FGF-2 as multifunctional biomolecules governing cell adhesion and proliferation. International Journal of Molecular Sciences. 22 (4), 1843 (2021).
  38. Guo, L. H., Yin, M., Wang, Y. X. CREB1, a direct target of miR-122, promotes cell proliferation and invasion in bladder cancer. Oncology Letters. 16 (3), 3842-3848 (2018).
  39. Wang, D. D., et al. Induction of CYP1A1 increases gefitinib-induced oxidative stress and apoptosis in A549 cells. Toxicology In Vitro. 44, 36-43 (2017).
  40. Tan, A. C. Targeting the PI3K/Akt/mTOR pathway in non-small cell lung cancer (NSCLC). Thoracic Cancer. 11 (3), 511-518 (2020).
  41. Jin, X., et al. RBM10 inhibits cell proliferation of lung adenocarcinoma via RAP1/AKT/CREB signalling pathway. Journal of Cellular and Molecular Medicine. 23 (6), 3897-3904 (2019).
  42. Henkels, K. M., et al. Phospholipase D (PLD) drives cell invasion, tumor growth and metastasis in a human breast cancer xenograph model. Oncogene. 32 (49), 5551-5562 (2013).
  43. Zhang, Z. Y., et al. CircRNA_101237 promotes NSCLC progression via the miRNA-490-3p/MAPK1 axis. Scientific Reports. 10, 490-493 (2020).
  44. Gao, T. X., et al. Exploring the mechanism of Fu-Zi Decoction in treatment of chronic heart failure based on network pharmacology and molecular docking technology. Journal of Chinese Pharmaceutical Sciences. 30 (09), 705-715 (2021).
  45. Wang, B., et al. PP4C facilitates lung cancer proliferation and inhibits apoptosis via activating MAPK/ERK pathway. Pathology, Research and Practice. 216 (5), 152910 (2020).
  46. Moon, M. Y., et al. Rap1 regulates hepatic stellate cell migration through the modulation of RhoA activity in response to TGF-β1. International Journal of Molecular Medicine. 44 (2), 491-502 (2019).
  47. Kan, J., et al. He-Chan Pian inhibits the metastasis of non-small cell lung cancer via the miR-205-5p-mediated regulation of the GREM1/Rap1 signaling pathway. Phytomedicine. 94, 153821 (2022).
  48. Sidrat, T., et al. Role of Wnt signaling during in-vitro bovine blastocyst development and maturation in synergism with PPARδ signaling. Cells. 9 (4), 923 (2020).
  49. Wagner, N., Wagner, K. D. PPAR beta/delta and the hallmarks of cancer. Cells. 9 (5), 1133 (2020).
  50. Miriam, M., et al. PI3K/AKT signaling pathway and cancer: An updated review. Annals of Medicine. 46 (6), 372-383 (2014).
  51. Ma, X. L., et al. CD73 promotes hepatocellular carcinoma progression and metastasis via activating PI3K/AKT signaling by inducing Rap1-mediated membrane localization of P110β and predicts poor prognosis. Journal of Hematology & Oncology. 12 (1), 37 (2019).
  52. Li, T., et al. Pomegranate flower extract bidirectionally regulates the proliferation, differentiation and apoptosis of 3T3-L1 cells through regulation of PPARγ expression mediated by PI3K-AKT signaling pathway. Biomedicine & Pharmacotherapy. 131, 110769 (2020).
This article has been published
Video Coming Soon
PDF DOI DOWNLOAD MATERIALS LIST

Cite this Article

Zhao, X. y., Yang, Y. y., Feng, J. l., Feng, C. l. Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma. J. Vis. Exp. (193), e64847, doi:10.3791/64847 (2023).More

Zhao, X. y., Yang, Y. y., Feng, J. l., Feng, C. l. Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma. J. Vis. Exp. (193), e64847, doi:10.3791/64847 (2023).

Less
Copy Citation Download Citation Reprints and Permissions
View Video

Get cutting-edge science videos from JoVE sent straight to your inbox every month.

Waiting X
Simple Hit Counter