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

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
<ol>
	<li>Selection of active components
	<ol>
		<li>Open the HERB database (http://herb.ac.cn)19, and use &#34;Gualou&#34; (the Chinese name for Trichosanthes kirilowii Maxim) and &#34;Zhebeimu&#34; (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.</li>
		<li>Determine whether the obtained component is the active component.
		<ol>
			<li>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 &#8805; 30% and DL &#8805; 0.18)20,21.</li>
			<li>If the component has no OB and DL values, input the component into the Swiss ADME database (http://www.swissadme.ch/index.php)22&#160;to obtain the information on each component. Include components with &#8220;high&#8221; GI absorption and at least two &#8220;Yes&#8221; DL values as active components.&#8203;</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Target prediction of the active components
	<ol>
		<li>Open the HERB database (http://herb.ac.cn). Search and copy the canonical SMILES structures of the active components.</li>
		<li>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.</li>
		<li>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 &#60; 0.05, and MaxTC &#62; 0.5).</li>
		<li>Copy all the targets to a spreadsheet, and remove the duplicates to obtain the drug targets.</li>
	</ol>
	</li>
	<li>Prediction of disease targets
	<ol>
		<li>Open the GeneCards database (https://www.genecards.org)24&#160;and Online Mendelian Inheritance in Man database&#160;(OMIM,&#160;https://www.omim.org)25&#160;and use Lung Adenocarcinoma as the keyword to obtain the disease targets of the lung adenocarcinoma.</li>
		<li>Download the spreadsheets of the disease targets. Delete the repeated targets to obtain the LUAD targets.</li>
	</ol>
	</li>
	<li>Construction of a drug-component-disease-target network
	<ol>
		<li>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.</li>
		<li>Open Cytoscape 3.8.0. Click on File in the menu bar, and then select Import &#62; 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.</li>
		<li>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.</li>
	</ol>
	</li>
	<li>Construction of the PPI network and screening of the core proteins
	<ol>
		<li>Open the STRING database (https://cn.string-db.org/)26. Paste the text-format list of the potential targets of&#160;Trichosanthes-Fritillaria thunbergii against LUAD into the List of Names dialog box. Select Homo sapiens in Organisms, and click on the SEARCH &#62; CONTINUE buttons.</li>
		<li>When the results are available, click on Settings, and tick High Confidence (0.700) in Basic Settings &#62; Minimum Required Interaction Score. Tick Hide Disconnected Nodes in the Network in Advanced Settings, and then click on the UPDATE button.</li>
		<li>Click on Exports in the title bar, and download the short tabular text of the PPI relationship in TSV format.</li>
		<li>Open Cytoscape 3.8.0. Click on File &#62; Import &#62; Network from File to import the TSV format file for visual analysis.</li>
		<li>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.</li>
	</ol>
	</li>
	<li>KEGG enrichment analysis
	<ol>
		<li>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&#160;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.</li>
		<li>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.</li>
		<li>Open R software (https://cran.r-project.org/). Type install.package (&#34;ggplot2&#34;) and library (ggplot2) in R for the installation of the ggplot2 R package. Press Enter to run the KEGG visualization program.</li>
	</ol>
	</li>
</ol>
2. Experimental verification
<ol>
	<li>Drug preparation 
	NOTE: Fritillaria thunbergii Miq and Trichosanthes kirilowii Maxim were purchased from Dongzhimen Hospital, affiliated with the Beijing University of Chinese Medicine.
	<ol>
		<li>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 &#176;C under normal pressure for 1 h.</li>
		<li>Filter the decoction to obtain a filtrate with a double layer of sterile medical gauze, and then use 80 &#181;m filter paper to further filter the extract. Repeat the above operation three times.</li>
		<li>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 &#176;C. Mix and condense the decoctions into an extract in the rotary evaporator for 6 h to yield a viscous liquid.</li>
		<li>Turn on the vacuum freeze-drying mechanism to precool the temperature to &#8722;40 &#176;C. Put the obtained extract into the material tray, and place it into the cold trap for freezing.</li>
		<li>When the cold trap temperature reaches &#8722;50 &#176;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 &#8722;20 &#176;C for later use.</li>
	</ol>
	</li>
	<li>Cell cultivation
	<ol>
		<li>Configure DMEM complete medium with 89% DMEM basic medium, 10% fetal bovine serum, and 1% penicillin-streptomycin.</li>
		<li>After removing the A549 cells from liquid nitrogen, incubate the cells in a 37 &#176;C water bath, and stir until the medium in the vial melts.</li>
		<li>Add a fourfold volume of DMEM complete medium into the melted cells. Centrifuge the cells (4 &#176;C, 679 x g, 5 min), and discard the supernatant.</li>
		<li>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 &#176;C, 5% CO2.</li>
	</ol>
	</li>
	<li>Detection of cell viability
	<ol>
		<li>Digest the logarithmic growth-phase A549 cells with 1 mL of 0.25% trypsin for 1 min at 37&#176;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 &#176;C, 192 x g, 5 min). Resuspend the obtained cells using DMEM complete medium.</li>
		<li>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.</li>
		<li>Dissolve 1 g of Trichosanthes-Fritillaria thunbergii water extract in 10 mL of PBS solution, and filter-sterilize it through a 0.22 &#181;m 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.</li>
		<li>Plate the diluted cells on 96-well plates with 100 &#181;L per well. After cell adherence, add 1 &#181;L of Trichosanthes-Fritillaria thunbergii water extracts of different concentrations to adjust the concentration of each well to 900 &#181;g/mL, 800 &#181;g/mL, 700 &#181;g/mL, 600 &#181;g/mL, 500 &#181;g/mL, 400 &#181;g/mL, 300 &#181;g/mL, 200 &#181;g/mL, and 100 &#181;g/mL.</li>
		<li>Discard the original medium after 24 h of culture, and add 100 &#181;L of DMEM basic medium for further incubation for 2 h at 37 &#176;C, 5% CO2.</li>
		<li>After the above treatment, add 20 &#181;L of MTS solution (Table of Materials), and incubate the cells for another 1 h at 37 &#176;C, 5% CO2.</li>
		<li>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 &#215; (OD of the treated sample &#8722; OD of the medium)/(OD of the control sample &#8722; 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 &#181;g/mL, 600 &#181;g/mL, and 800 &#181;g/mL were used as the low, medium, and high doses.</li>
	</ol>
	</li>
	<li>Drug intervention and sample collection 
	NOTE: Cell growth was plotted against time to determine the log phase.
	<ol>
		<li>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.</li>
		<li>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 &#181;L of the PBS solution, 20 &#181;L of the 40 mg/mL Trichosanthes-Fritillaria thunbergii water extract, 20 &#181;L of the 60 mg/mL Trichosanthes-Fritillaria thunbergii water extract, and 20 &#181;L 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 &#181;g/mL, 400 &#181;g/mL, 600 &#181;g/mL, and 800 &#181;g/mL, respectively.</li>
		<li>Add 250 &#181;L 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 &#176;C, 6,714 x g, 10 min), and obtain the supernatant.</li>
	</ol>
	</li>
	<li>Western blotting
	<ol>
		<li>Detect the protein concentration of the samples by the BCA method according to the manufacturer&#39;s instructions. Use RIPA buffer to adjust the concentration of each sample to be consistent.</li>
		<li>Mix 40 &#181;L of the protein sample with 10 &#181;L of 5x loading buffer, and boil for 5 min at 100 &#176;C. Centrifuge (4 &#176;C, 6,714 x g, 10 min) the mixture to obtain the supernatant for subsequent experiments.</li>
		<li>Isolate the proteins by gel electrophoresis using 120 V vertical electrophoresis.</li>
		<li>Prepare an electrical &#34;sandwich&#34; (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.</li>
		<li>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.</li>
		<li>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 &#176;C.</li>
		<li>After removing the primary antibody, use TBST solution to wash the PVDF membrane five times for 5 min each.</li>
		<li>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.</li>
		<li>Detect the protein using a chemiluminescence detection system.</li>
	</ol>
	</li>
</ol>
3. Molecular docking
<ol>
	<li>Open the UniProt database (https://www.uniprot.org)28. Type the target symbols into the search box, and click on the&#160;SEARCH button. In the Structure subheading, click on Download for the 3D structures of the protein targets.</li>
	<li>Open the RCSB PDB database (https://www.rcsb.org/pdb/home/sitemap.do)29.&#160;Type the name of the protein macromolecules into the search box. Download the protein macromolecule structures in PDB format.</li>
	<li>Download the installation package of UCSF Chimera 1.16 software (https://vina.scripps.edu/)&#160;and AutoDock Vina (https://vina.scripps.edu/). Install and open the UCSF Chimera 1.16 software. Click on&#160;File &#62; Open to display the receptor protein.</li>
	<li>Click on Tools &#62; Structure Editing &#62; 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.</li>
	<li>Click on File &#62; Open to display the mol2-format ligand in the UCSF Chimera 1.16 software. In Tools &#62; Surface/Binding Analysis &#62; 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.</li>
	<li>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.</li>
</ol>
4. Statistical analysis
<ol>
	<li>Open SPSS 26.0 statistical software. Click on File &#62; Import Data for data loading.</li>
	<li>Present the experimental data as mean &#177; standard deviation.</li>
	<li>Compare multiple groups using a one-way ANOVA. 
	NOTE: P &#60; 0.05 was considered statistically significant in this work.</li>
</ol>

The authors have no conflicts of interest to declare.

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 se...

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 &#34;lung accumulation&#34; and &#34;pulmonary petrous&#34;. 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.

<table><tbody><tr><td>0.25% trypsin-EDTA</td><td>Gibco</td><td>R001100</td><td></td></tr><tr><td>A549&amp;nbsp;cell line</td><td>Procell</td><td>CL-0016</td><td></td></tr><tr><td>AKT antibody</td><td>CST</td><td>4691S</td><td></td></tr><tr><td>BCA Protein Assay Kit</td><td>Solarbio</td><td>PC0020</td><td></td></tr><tr><td>Chemiluminescence detection system</td><td>Shanghai Qinxiang Scientific Instrument Factory</td><td>ChemiScope 6100</td><td></td></tr><tr><td>Dulbecco&#039;s modified eagle medium (DMEM)</td><td>Solarbio</td><td>11995</td><td></td></tr><tr><td>Enhanced chemiluminescence (ECL) kit&amp;nbsp;</td><td>ABclonal</td><td>RM00021</td><td></td></tr><tr><td>Fetal bovine serum</td><td>ScienCell</td><td>0025</td><td></td></tr><tr><td>HRP Goat Anti-Rabbit IgG (H+L)</td><td>ABclonal</td><td>AS014</td><td></td></tr><tr><td>MTS assay kit</td><td>Promega</td><td>G3580</td><td></td></tr><tr><td>p-AKT antibody</td><td>CST</td><td>6040S</td><td></td></tr><tr><td>Penicillin streptomycin</td><td>Gibco</td><td>C14-15070-063</td><td></td></tr><tr><td>Phenylmethanesulfonyl fluoride (PMSF)</td><td>Solarbio</td><td>P0100</td><td></td></tr><tr><td>Phosphatase inhibitor</td><td>Beyotime</td><td>P1081</td><td></td></tr><tr><td>Phosphate buffered saline (PBS)</td><td>Solarbio</td><td>P1020</td><td></td></tr><tr><td>Polyvinylidene difluoride (PVDF) membranes</td><td>Millipore</td><td>ISEQ00010</td><td></td></tr><tr><td>RIPA lysis solution</td><td>Solarbio</td><td>R0010</td><td></td></tr><tr><td>Rotary evaporator</td><td>Shanghai Yarong Biochemical Instrument Factory</td><td>RE52CS-1</td><td></td></tr><tr><td>Vacuum freeze-drying mechanism</td><td>Ningbo Scientz Biotechnology</td><td>SCIENTZ-10</td><td></td></tr><tr><td>&amp;beta;-Actin antibody</td><td>ABclonal</td><td>AC026</td><td></td></tr></tbody></table>

network pharmacology prediction and experimental validation of trichosanthes-fritillaria thunbergii action mechanism against lung adenocarcinoma

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.

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...

Watch this Scientific Journal Video about Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma at JoVE.com

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

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.

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

We aimed to study the mechanism of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma (LUAD) based on network pharmacology and ...

network-pharmacology-prediction-experimental-validation-trichosanthes

Research

JoVE Journal

Medicine

1.2K Views.  Dongzhimen Hospital affiliated to Beijing University of Chinese Medicine. Traditionele Chinese geneeskunde, We zullen het hebben over de kenmerken van multicomponent van het doelwit. Dat is het basisvak voor netwerk- en biologisch onderzoek. We hebben een demonstratie gedaan van de technologie die de drempel zal verlagen.Network Pharmacological is systematische analytische technologie, bouwt de tools van interactie netwerk werk van multifactoren. Het kan effectief de enkele route van traditionele Chinese geneeskunde voorspellen in De gegevens van Network Pha...

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

The Establishment of a Lung Colonization Assay for Circulating Tumor Cell Visualization in Lung Tissues

Metastasis is the major cause of cancer death. The role of circulating tumor cells (CTCs) in promoting cancer metastasis, in which lung colonization by CTCs critically contributes to early lung metastatic processes, has been vigorously investigated. As such, animal models are the only approach that captures the full systemic process of metastasis. Given that problems occur in previous experimental designs for examining the contributions of CTCs to blood vessel extravasation, we established an in vivo lung colonization assay in which a long-term-fluorescence cell-tracer, carboxyfluorescein succinimidyl ester (CFSE), was used to label suspended tumor cells and lung perfusion was performed to clear non-specifically trapped CTCs prior to lung removal, confocal imaging, and quantification. Polymeric fibronectin (polyFN) assembled on CTC surfaces has been found to mediate lung colonization in the final establishment of metastatic tumor tissues. Here, to specifically test the requirement of polyFN assembly on CTCs for lung colonization and extravasation, we performed short term lung colonization assays in which suspended Lewis lung carcinoma cells (LLCs) stably expressing FN-shRNA (shFN) or scramble-shRNA (shScr) and pre-labeled with 20 &#956;M of CFSE were intravenously inoculated into C57BL/6 mice. We successfully demonstrated that the abilities of shFN LLC cells to colonize the mouse lungs were significantly diminished in comparison to shScr LLC cells. Therefore, this short-term methodology may be widely applied to specifically demonstrate the ability of CTCs within the circulation to colonize the lungs.

An animal model is needed to decipher the role of circulating tumor cells (CTCs) in promoting lung colonization during cancer metastasis. Here, we established and successfully performed an in vivo assay to specifically test the requirement of polymeric fibronectin (polyFN) assembly on CTCs for lung colonization.

Metastasis is the major cause of cancer death. The role of circulating tumor cells (CTCs) in promoting cancer metastasis, in which lung colonization by ...

Cancer Research

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer

A hallmark of advanced tumors is a switch to aerobic glycolysis that is readily measured by [18F]-2-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) imaging. Co-mutations in the KRAS proto-oncogene and the LKB1 tumor suppressor gene are frequent events in lung cancer that drive hypermetabolic, glycolytic tumor growth. A critical pathway regulating the growth and metabolism of these tumors is the mechanistic target of the rapamycin (mTOR) pathway, which can be effectively targeted using selective catalytic mTOR kinase inhibitors. The mTOR inhibitor MLN0128 suppresses glycolysis in mice bearing tumors with Kras and Lkb1 co-mutations, referred to as KL mice. The therapy response in KL mice is first measured by 18F-FDG PET and computed tomography (CT) imaging before and after the delivery of MLN0128. By utilizing 18F-FDG PET/CT, researchers are able to measure dynamic changes in the glucose metabolism in genetically engineered mouse models (GEMMs) of lung cancer following a therapeutic intervention with targeted therapies. This is followed by ex vivo autoradiography and a quantitative immunohistochemical (qIHC) analysis using morphometric software. The use of qIHC enables the detection and quantification of distinct changes in the biomarker profiles following treatment as well as the characterization of distinct tumor pathologies. The coupling of PET imaging to quantitative histology is an effective strategy to identify metabolic and therapeutic responses in vivo in mouse models of disease.

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer

In this protocol, we describe how to utilize [18F]-2-fluoro-2-deoxy-D-glucose positron emission tomography and computed tomography (18F-FDG PET/CT) imaging to measure the tumor metabolic response to the targeted therapy MLN0128 in a Kras/Lkb1 mutant mouse model of lung cancer and coupled imaging with high resolution ex vivo autoradiography and quantitative histology.

A hallmark of advanced tumors is a switch to aerobic glycolysis that is readily measured by [18F]-2-fluoro-2-deoxy-D-glucose positron emission tomography ...

Orthotopic Transplantation of Syngeneic Lung Adenocarcinoma Cells to Study PD-L1 Expression

The use of mouse models is indispensable for studying the pathophysiology of various diseases. With respect to lung cancer, several models are available, including genetically engineered models as well as transplantation models. However, genetically engineered mouse models are time-consuming and expensive, whereas some orthotopic transplantation models are difficult to reproduce. Here, a non-invasive intratracheal delivery method of lung tumor cells as an alternative orthotopic transplantation model is described. The use of mouse lung adenocarcinoma cells and syngeneic graft recipients allows studying tumorigenesis under the presence of a fully active immune system. Furthermore, genetic manipulations of tumor cells before transplantation makes this model an attractive time-saving approach to study the impact of genetic factors on tumor growth and tumor cell gene expression profiles under physiological conditions. Using this model, we show that lung adenocarcinoma cells express increased levels of the T-cell suppressor programmed death-ligand 1 (PD-L1) when grown in their natural environment as compared to cultivation in vitro.

Here we describe a minimally invasive syngeneic orthotopic transplantation model of mouse lung adenocarcinoma cells as a time- and cost-reducing model to study non-small cell lung cancer.

The use of mouse models is indispensable for studying the pathophysiology of various diseases. With respect to lung cancer, several models are available, ...

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

In the present study, we combined an in vitro 3D lung tumor model with an in silico model to optimize predictions of drug response based on a specific mutational background. The model is generated on a decellularized porcine scaffold that reproduces tissue-specific characteristics regarding extracellular matrix composition and architecture including the basement membrane. We standardized a protocol that allows artificial tumor tissue generation within 14 days including three days of drug treatment. Our article provides several detailed descriptions of 3D read-out screening techniques like the determination of the proliferation index Ki67 staining&#39;s, apoptosis from supernatants by M30-ELISA and assessment of epithelial to mesenchymal transition (EMT), which are helpful tools for evaluating the effectiveness of therapeutic compounds. We could show compared to 2D culture a reduction of proliferation in our 3D tumor model that is related to the clinical situation. Despite of this lower proliferation, the model predicted EGFR-targeted drug responses correctly according to the biomarker status as shown by comparison of the lung carcinoma cell lines HCC827 (EGFR -mutated, KRAS wild-type) and A549 (EGFR wild-type, KRAS-mutated) treated with the tyrosine-kinase inhibitor (TKI) gefitinib. To investigate drug responses of more advanced tumor cells, we induced EMT by long-term treatment with TGF-beta-1 as assessed by vimentin/pan-cytokeratin immunofluorescence staining. A flow-bioreactor was employed to adjust culture to physiological conditions, which improved tissue generation. Furthermore, we show the integration of drug responses upon gefitinib treatment or TGF-beta-1 stimulation - apoptosis, proliferation index and EMT - into a Boolean in silico model. Additionally, we explain how drug responses of tumor cells with a specific mutational background and counterstrategies against resistance can be predicted. We are confident that our 3D in vitro approach especially with its in silico expansion provides an additional value for preclinical drug testing in more realistic conditions than in 2D cell culture.

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

We present a three-dimensional (3D) lung cancer model based on a biological collagen scaffold to study sensitivity towards non-small-cell-lung-cancer-(NSCLC)-targeted therapies. We demonstrate different read-out techniques to determine the proliferation index, apoptosis and epithelial-mesenchymal transition (EMT) status. Collected data are integrated into an in silico model for prediction of drug sensitivity.

In the present study, we combined an in vitro 3D lung tumor model with an in silico model to optimize predictions of drug response based on a specific ...

Bioengineering

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Hyperlipidemia has become a leading risk factor for cardiovascular diseases and liver injury worldwide. Fructus Phyllanthi (FP) is an effective drug against hyperlipidemia in Traditional Chinese Medicine (TCM) and Indian Medicine theories, however the potential mechanism requires further exploration. The present research aims to reveal the mechanism of FP against hyperlipidemia based on an integrated strategy combining network pharmacology prediction with metabolomics validation. A high-fat diet (HFD)-induced mice model was established by evaluating the plasma lipid levels, including total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Network pharmacology was applied to find out the active ingredients of FP and potential targets against hyperlipidemia. Metabolomics of plasma and liver were performed to identify differential metabolites and their corresponding pathways among the normal group, model group, and intervention group. The relationship between network pharmacology and metabolomics was further constructed to obtain a comprehensive view of the process of FP against hyperlipidemia. The obtained key target proteins were verified by molecular docking. These results reflected that FP improved the plasma lipid levels and liver injury of hyperlipidemia induced by a HFD. Gallic acid, quercetin, and beta-sitosterol in FP were demonstrated as the key active compounds. A total of 16 and six potential differential metabolites in plasma and liver, respectively, were found to be involved in the therapeutic effects of FP against hyperlipidemia by metabolomics. Further, integration analysis indicated that the intervention effects were associated with CYP1A1, AChE, and MGAM, as well as the adjustment of L-kynurenine, corticosterone, acetylcholine, and raffinose, mainly involving tryptophan metabolism pathway. Molecular docking ensured that the above ingredients acting on hyperlipidemia-related protein targets played a key role in lowering lipids. In summary, this research provided a new possibility for preventing and treating hyperlipidemia.

The present protocol describes an integrated strategy for exploring the key targets and mechanisms of Fructus Phyllanthi against hyperlipidemia based on network pharmacology prediction and metabolomics verification.

Hyperlipidemia has become a leading risk factor for cardiovascular diseases and liver injury worldwide. Fructus Phyllanthi (FP) is an effective drug ...

Salidroside's Mechanism in Suppressing MCF-7 Cell Migration and Proliferation

Exploring the Pharmacological Action and Molecular Mechanism of Salidroside in Inhibiting MCF-7 Cell Proliferation and Migration

Salidroside (Sal) contains anti-carcinogenic, anti-hypoxic, and anti-inflammatory pharmacological activities. However, its underlying anti-breast cancer mechanisms have been only incompletely elucidated. Hence, this protocol intended to decode the potential of Sal in regulating the PI3K-AKT-HIF-1&#945;-FoxO1 pathway in the malignant proliferation of human breast cancer MCF-7 cells. First, the pharmacological activity of Sal against MCF-7 was evaluated by CCK-8 and cell scratch assays. Moreover, the resistance of MCF-7 cells was measured by migration and Matrigel invasion assays. For cell apoptosis and cycle assays, MCF-7 cells were processed in steps with annexin V-FITC/PI and cell cycle-staining detection kits for flow cytometry analyses, respectively. The levels of reactive oxygen species (ROS) and Ca2+ were examined by DCFH-DA and Fluo-4 AM immunofluorescence staining. The activities of Na+-K+-ATPase and Ca2+-ATPase were determined using the corresponding commercial kits. The protein and gene expression levels in apoptosis and the PI3K-AKT-HIF-1&#945;-FoxO1 pathway were further determined using western blot and qRT-PCR analyses, respectively. We found that Sal treatment significantly restricted the proliferation, migration, and invasion of MCF-7 cells with dose-dependent effects. Meanwhile, Sal administration also dramatically forced MCF-7 cells to undergo apoptosis and cell cycle arrest. The immunofluorescence tests showed that Sal observably stimulated ROS and Ca2+ production in MCF-7 cells. Further data confirmed that Sal promoted the expression levels of pro-apoptotic proteins, Bax, Bim, cleaved caspase-9/7/3, and their corresponding genes. Consistently, Sal intervention prominently reduced the expression of the Bcl-2, p-PI3K/PI3K, p-AKT/AKT, mTOR, HIF-1&#945;, and FoxO1 proteins and their corresponding genes. In conclusion, Sal can be used as a potential herb-derived compound for treating breast cancer, as it may reduce the malignant proliferation, migration, and invasion of MCF-7 cells by inhibiting the PI3K-AKT-HIF-1&#945;-FoxO1 pathway.

Author Spotlight: Exploring Salidroside's Molecular Mechanisms in Breast Cancer Treatment 

The present protocol describesa comprehensive strategy for evaluating the pharmacological action and mechanism of salidroside in inhibiting MCF-7 cell proliferation and migration.

Salidroside (Sal) contains anti-carcinogenic, anti-hypoxic, and anti-inflammatory pharmacological activities. However, its underlying anti-breast cancer ...

Network Pharmacology and GEO Dataset Analysis of SDG in Anus Eczema Treatment

The Efficacy and Underlying Pathway Mechanisms of ShiDuGao Treatment for Anus Eczema Based on GEO Datasets and Network Pharmacology

Anus eczema is a chronic and recurrent inflammatory skin disease affecting the area around the anus. While the lesions primarily occur in the anal and perianal skin, they can also extend to the perineum or genitalia. ShiDuGao (SDG) has been found to possess significant reparative properties against anal pruritus, exudation control, moisture reduction, and skin repair. However, the genetic targets and pharmacological mechanisms of SDG on anal eczema have yet to be comprehensively elucidated and discussed. Consequently, this study employed a network pharmacological approach and utilized gene expression omnibus (GEO) datasets to investigate gene targets. Additionally, a protein-protein interaction network (PPI) was established, resulting in the identification of 149 targets, of which 59 were deemed hub genes, within the "drug-target-disease" interaction network.
The gene function of SDG in the treatment of perianal eczema was assessed through the utilization of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis. Subsequently, the anti-perianal eczema function and potential pathway of SDG, as identified in network pharmacological analysis, were validated using molecular docking methodology. The biological processes associated with SDG-targeted genes and proteins in the treatment of anus eczema primarily encompass cytokine-mediated responses, inflammatory responses, and responses to lipopolysaccharide, among others. The results of the pathway enrichment and functional annotation analyses suggest that SDG plays a crucial role in preventing and managing anal eczema by regulating the Shigellosis and herpes simplex virus 1 infection pathways. Network pharmacology and GEO database analysis confirms the multi-target nature of SDG in treating anal eczema, specifically by modulating TNF, MAPK14, and CASP3, which are crucial hub targets in the TNF and MAPK signaling pathways. These findings provide a clear direction for further investigation into SDG's therapeutic mechanism for anal eczema while highlighting its potential as an effective treatment approach for this debilitating condition.

Author Spotlight: Exploring ShiDuGao's Multi-Target Approach in Anus Eczema Treatment

This investigative effort sought to elucidate the mechanism of topical drug administration using a synergistic integration of network pharmacology and gene expression omnibus (GEO) datasets. This article evaluated the feasibility, target, and mechanism of ShiDuGao (SDG) in treating anus eczema.

Anus eczema is a chronic and recurrent inflammatory skin disease affecting the area around the anus. While the lesions primarily occur in the anal and ...

Impact of Sanleng Jiashen Formula on Orthotopic Hepatocellular Carcinoma in Mice

Establishment of Hepatocarcinoma in BALB/c-nu Mice and Investigation of the Therapeutic Effect of the Sanleng Jiashen Formula

The lethality of liver cancer and the resistance to chemical drugs have forced the search for effective prescriptions of traditional herbs for liver cancer. Animal models that are repeatable, easy to manipulate, and highly mimic the pathophysiological processes of liver cancer are the prerequisite for the successful screening of effective drug candidates. Meanwhile, reliable drug efficacy evaluation indicators and means are also the guarantee of anti-liver cancer drug research and development.
Sanleng Jiashen formula, a representative prescription of traditional Chinese medicine containing Sparganium stoloni erum, Buch. -Ham. (Sanleng), Panax ginseng C. A. Mey. (ginseng), Rheum officinale Baill. (rhubarb), and Ligusticum chuanxiong Hort. (Chuanxiong), is prescribed to nourish the liver and clear heat, remove toxins, and promote blood circulation to treat liver cancer. This experimental protocol describes the preparation of lyophilized Sanleng Jiashen formula and the establishment process of in-situ liver cancer in BALB/c-nu mice. Histopathological staining, immunohistochemical detection of cancer markers, in vivo imaging of mice, and chick embryo chorioallantoic membrane test were used to explore the inhibition and anti-angiogenesis effect of Sanleng Jiashen formula on malignant proliferation of liver cancer tissue. The data show that the Sanleng Jiashen formula can effectively resist the malignant proliferation of liver cancer tissue, which is manifested by reduced tumor mass volume, improved pathological damage, and lower levels of the cancer marker ki67.
The superior inhibition of angiogenesis also suggests that the Sanleng Jiashen formula may have the potential to treat and prevent the progression and deterioration of liver cancer. The whole experimental scheme shows a comprehensive process of traditional Chinese medicine components in the treatment of mouse liver cancer, which provides a reference for the establishment and optimization of a liver cancer model, as well as the research and development of drugs to prevent and treat liver cancer.

Here, we present the step-by-step protocol to investigate the therapeutic and vascular inhibitory effects of the Sanleng Jiashen formula on BALB/c-nu mice with orthotopic hepatocellular carcinoma.

The lethality of liver cancer and the resistance to chemical drugs have forced the search for effective prescriptions of traditional herbs for liver ...

Key Molecular Targets Linking Primary Sjogren's Syndrome and Lung Adenocarcinoma

Primary Sjogren's Syndrome Associated with Lung Adenocarcinoma: Probing the Potential Common Pathogenic Mechanisms and Experimental Verification

This study aimed to probe the potential common pathogenic mechanisms linking primary Sjogren's syndrome (pSS) and lung adenocarcinoma (LUAD) through bioinformatics analysis and experimental verification. The relevant genes associated with pSS and LUAD were retrieved from the Gene Expression Omnibus (GEO) database and Genecard database. Subsequently, differentially expressed genes (DEGs) associated with pSS and LUAD were screened as pSS-LUAD-DEGs. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were performed to elucidate the significant biological functions of pSS-LUAD-DEGs. Core targets were identified by constructing the protein-protein interaction (PPI) network, further assessing hub gene diagnostic accuracy through Receiver Operating Characteristic (ROC) curve analyses. In this study, NOD/Ltj mice served as pSS animal models and were stimulated with particulate matter 2.5 (PM2.5) to generate an inflammatory reaction. Quantitative real-time polymerase chain reaction (qPCR), enzyme-linked immunosorbent assay (ELISA), and western blotting were employed for relevant molecular biology experiment verification. The results revealed through KEGG and GO enrichment analyses indicate that inflammation plays a critical role in linking pSS and LUAD. IL6, CCNA2, JAK2, IL1B, ASPM, CCNB2, NUSAP1, and CEP55 were determined as key targets of pSS-LUAD. BALB/c mice and NOD/Ltj mice exhibited enhanced expression of inflammatory cytokines IL-6 and IL-1&#946; in lung tissues following 21 days of stimulation with PM2.5, activating the JAK2/STAT3 signaling pathway and up-regulating the expression of tumor-associated genes CCNA2, CCNB2, and CEP55, with NOD/Ltj mice exhibiting more pronounced changes than BALB/c mice. This protocol demonstrates that carcinogenesis induced by the pulmonary inflammatory microenvironment may be a key reason for the high incidence of LUAD in pSS patients. Additionally, blocking-related mechanisms may help prevent the occurrence of LUAD in pSS patients.

Author Spotlight: Exploring the Role of Inflammation in the Co-occurrence of Primary Sjogren's Syndrome and Lung Adenocarcinoma

This manuscript describes the operational procedures and precautions to probe the potential common pathogenic mechanisms linking primary Sjogren's syndrome and lung adenocarcinoma through bioinformatics analysis and experimental verification.

This study aimed to probe the potential common pathogenic mechanisms linking primary Sjogren's syndrome (pSS) and lung adenocarcinoma (LUAD) through ...

Monitoring Pulmonary Nodule Progression Using Micro-Computed Tomography and Blood Sampling in a Mouse Model

Micro-computed tomography (micro-CT) is a real-time, intuitive, sensitive, and minimally invasive technique for monitoring changes from pulmonary nodules (PN) to lung cancer (LC). The integration of submandibular vein blood sampling enables rapid, stable, and straightforward detection of imaging and key target alterations during the progression of PN to LC. In this study, we administered a dosage of 100 mg/kg of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone in A/J mice to develop a lung adenocarcinoma model. Disease progression in the experimental animals was then monitored through submandibular vein blood sampling and micro-CT assay. Experimental results showed the presence of nodular foci in the lungs of some animals by the 10th week, with the development of lung adenocarcinoma images becoming evident by the 21st week. In conclusion, micro-CT can effectively observe pathological changes in the lungs of mice and, when combined with submandibular vein blood sampling, can dynamically monitor changes in blood, protein, and targets. This method provides a highly specific, simple, and sensitive approach for drug screening, pharmacokinetic testing, toxicological experiments, and safety studies.

This protocol describes an efficient, simple, and minimally invasive method for studying pulmonary nodules. Submaxillary vein blood collection and micro-CT imaging are used as investigative techniques.

Micro-computed tomography (micro-CT) is a real-time, intuitive, sensitive, and minimally invasive technique for monitoring changes from pulmonary nodules ...

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

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