Isorhapontigenin is a novel potential therapeutic agent for lung cancer： evidence from network pharmacology, bioinformatics, molecular docking and in vitro experiments

Isorhapontigenin is a novel potential therapeutic agent for lung cancer： evidence from network pharmacology, bioinformatics, molecular docking and in vitro experiments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Isorhapontigenin is a novel potential therapeutic agent for lung cancer： evidence from network pharmacology, bioinformatics, molecular docking and in vitro experiments Zhiyu Wu, Chengyu Hou, Qiulin Zhu, Zixia Huang, Zesheng Lu, Chunhui Shen, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5882443/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Isorhapontigenin is an effective active ingredient in rheum officinale, which has been reported to have anti-tumor effects. However, its effect and molecular mechanism on non-small cell lung cancer are still unclear. Methods : Firstly, potential therapeutic targets of Isorhapontigenin against non-small cell lung cancer were obtained through network pharmacology analysis. Secondly, bioinformatics analysis was conducted to identify key targets and potential signaling pathway mechanisms based on the obtained potential targets. Then, evaluate the binding ability between Isorhapontigenin and key targets using computer molecular docking strategies. Finally, in vitro cell experiments were conducted to verify the effects and related targets of Isorhapontigenin on non-small cell lung cancer cells. Results : 104 drug targets and 6688 disease targets were acquired from SwissTarget prediction, BATMAN-TCM, STITCH and Genecards databases.79 potential therapeutic targets were identified through analysis based on online Venn website and PPI interaction analysis was performed on these targets to ultimately obtain 55 key targets. GO and KEGG analysis revealed that Isorhapontigenin mainly act on cell proliferation and cycle processes and PI3K/RELA/Cellcyle pathways to against non-small cell lung cancer. Computer molecular docking confirmed that Isorhapontigenin can bind to cell proliferation, cycle related proteins (CCND1, CDK2, PIK3CA, RELA). CCK-8 detection revealed that Isorhapontigenin significantly inhibited the proliferation of PC9 lung cancer cells, Moreover, RT-PCR detection showed that Isorhapontigenin downregulated the expression of CCND1, CDK2, PIK3CA and RELA genes. CCND1, CDK2, PIK3CA and RELA are highly expressed in NSCLC tissues. Overall survival analysis of patients indicated that key genes in the PIK3CA and NF-κBp65 signaling pathway significantly affected overall survival. Conclusion : Our research has found that Isorhapontigenin can effectively against non-small cell lung cancer, and this effect may be achieved by inhibiting cell proliferation and cycle progression mediated by the PIK3CA/NF-KB signaling pathway. Isorhapontigenin is a new potential therapeutic agent for lung cancer. Isorhapontigenin Non-small cell lung cancer network pharmacology bioinformatics molecular docking experimental verification Cell cycle Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction Lung cancer is one of the most common tumors in humans and is a leading cause of death. According to previous study, nearly 1.8 million people are diagnosed with lung cancer every year( 1 ). Non-small cell lung cancer (NSCLC) accounts for the majority of lung cancers( 2 ), however, currently, the treatments for NSCLC such as surgery and some drugs are limited and even have varying degrees of toxic and side effects( 3 ). Therefore, it is necessary to develop new safe and effective potential therapeutic drugs. In recent years, Traditional Chinese Medicine (TCM) has been widely reported to have good anti-cancer effects and has attracted increasing attention from researchers( 4 ). Some active ingredients in TCM such as quercetin, luteolin and kaempferol, have been shown to inhibit the growth of lung cancer cells( 5 – 7 ). Isorhapontigenin, a stilbene compound, can be extracted from rheum officinale. Previous studies have showed that it has a wide range of pharmacological effects, such as anti-inflammatory and antioxidant effects( 8 , 9 ), and some tumor studies have also reported that it has good anti-cancer effects( 10 , 11 ). However, it is still unclear whether it can treat NSCLC and the related molecular mechanisms of action. Network pharmacology is a systematic pharmacology method, which is widely used in the study of TCM and their components' mechanisms of action( 12 ). By utilizing various databases of TCM and chemical components, network pharmacology can quickly identify potential targets and pathways of TCM against diseases and saving researchers or institutions a lot of experimental funds and valuable research time. At the same time, it also provides theoretical basis for subsequent in vitro and in vivo experiments. Molecular docking strategy is a computer simulation method that widely used in new drug development research( 13 ). With the help of molecular docking strategy, the binding possibility of drugs and their targets can be preliminarily determined, laying foundation for determining drug targets. In recent years, with the development of traditional Chinese medicine databases and computational biology, network pharmacology and molecular docking strategies have often been integrated for drug research. In this study, we first used databases to obtain the therapeutic targets of Isorhapontigenin and NSCLC and further obtained the therapeutic targets of Isorhapontigenin through network pharmacology strategies. Then, we conducted bioinformatics analysis of key therapeutic targets, preliminarily validated some key targets through molecular docking strategies, and finally verified the efficacy and mechanism of Isorhapontigenin in treating NSCLC through in vitro cell experiments. The entire research process was shown in Fig. 1 . 2. Materials and Methods 2.1 Collection of Isorhapontigenin and NSCLC targets The “Canonical SMILES” format of Isorhapontigenin was obtained by searching curcumin in the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ) , then it was inputted into the Swiss target prediction database for get potential targets. What is more, BATMAN-TCM ( http://bionet.ncpsb.org.cn/batman-tcm/ ) and STITCH database ( http://stitch-beta.embl.de/ ) were also used to acquire its potential targets. Finally, summarize all targets and eliminate duplicates. The targets related to NSCLC were obtained by searching the human gene database Genecards ( https://www.genecards.org/ ). 2.2 Targets of Isorhapontigenin against NSCLC The online Venn website ( http://bioinformatics.psb.ugent.be/webtools/Venn/ ) was used to obtain potential targets for the treatment of non-small cell lung cancer with isoflavones. The parameter conditions are set according to the default conditions of the website. After uploading drugs and disease targets, the website is run for online analysis to obtain online Venn map and potential target files. 2.3 Construction of PPI network and identification of key targets In order to obtain the protein-protein interaction network (PPI) and core targets, potential therapeutic targets will be imported into the STRING database ( https://www.string-db.org/ ) for protein-protein interaction analysis, with the species set as human and other parameter conditions set as default. Obtain a preliminary protein-protein interaction network file, then use the highest PPI score (confidence from 0.4 to 0.9) and remove disconnected targets in the network, and to obtain PPI of the core target. Finally, running the sorting script that installed in R software4.0.2 to obtain sorting information for different core target based on network connectivity. 2.4 GO and KEGG enrichment analysis The Metascape platform ( https://metascape.org/ ) was used to analyze the final core therapeutic targets. The steps are briefly as follows. First, input the core targets into the gene list column of Metascape, then set the species as “Homo sapiens” with a P-value < 0.01, performing GO and KEGG enrichment analysis based on the core target, download the result image file finally. GO enrichment analysis includes Biological Process (BP), Cellular Component (CC), Molecular Function (MF). 2.5 Computer Molecular Docking Select core proteins from the core targets and key signaling pathways for computer molecular docking to preliminarily validate the core functional targets of Isorhapontigenin. As mentioned above, Retrieve Isorhapontigenin in the Pubchem database and download small molecule ligand 3D structures (3D Conformers) with SDF format, then using open babel 3.1.1 to convert the SDF format of small molecule ligands to mol2 format. Obtain the protein 3D structure in the RCSBPDB database ( https://www.uniprot.org/ ) , the limiting condition was set to human protein and small molecule ligand information in the structural complex. After downloading the PDB format, remove water molecules and small molecule ligands using Pymol 2.4.0 software and save the PDB format as the receptor protein. Perform hydrogenation and other processing on protein receptors and small molecule ligands in Autodock 1.5.6 software, save them as PDBQT format files, set appropriate docking boxes and parameters, use Autodock Vina 1.1.2 for molecular docking, save the results, and select some results for visual analysis in Pymol. If the binding energy score is less than or equal − 5, it indicates that the ligand and receptor can bind well. 2.6 Cell experiments 2.6.1 CCK-8 assay Isorhapontigenin was purchased from Alfa Biotechnology Co.Ltd (Chengdu,China), purity ≥ 98%. It was dissolved in DMSO (Sigma,USA) to a concentration of 100mM/L mother liquor for later use. PC9 lung cancer cells were purchased from Noble Biological Products Co.Ltd (nobcell 0415,Hangzhou, China). After the cells were fully grown, they were seeded and cultured in a 96-well cell culture plate with approximately 8000 cells per well. After 24 hours of cell adhesion, they were observed under microscope. Then different concentrations of drugs were used to intervene in the cells for 24 and 48 hours. After intervention, the culture medium was discarded and 110ul culture medium (containing 10ul of CCK-8 solution, fetal bovine serum free) was added to each well. CCK-8 reagent was purchased from Suzhou Xinsaimei Biotechnology Co., Ltd. After incubating CCK-8 for 1 hour, the OD value was detected at 450nm by microplate reader (Thermo Fisher,USA), the drug concentration that meets the IC50 condition is considered for subsequent cell experiments. 2.6.2 Real time fluorescence quantitative PCR assay According to the CCK-8 results, the cell inoculation and intervention for PCR experiments were performed. After cell intervention, the culture medium was discarded and the cells were washed twice with PBS(Gibco, USA).Total RNA was extracted using the trizol method, the purity and concentration of RNA were detected by a spectrophotometer(Thermo Fisher,USA),. RNA with a purity of at least 1.7 was used for subsequent experiments. The obtained RNA was reverse transcribed into cDNA using reverse transcription kit (Takara,Japan), and then diluted to a certain extent for PCR amplification. The relevant primer sequences are as shown in the Table 1 . Table 1 primer sequences Primers Forward Reverse Human GAPDH CCAGCAAGAGCACAAGAGGA TGAGGAGGGGAGATTCAGTGT Human CCND1 TGAGGGACGCTTTGTCTGTC CTTCTGCTGGAAACATGCCG Human CDK2 GACACGCTGCTGGATGTCA GAGGACCCGATGAGAATGGC Human PIK3CA AAGAGCCCCGAGCGTTT ACTAGGATTCTTGGGGGCAT Human RELA CCTTCCAAGAAGAGCAGCGTG CTGCCAGAGTTTCGGTTCAC 2.7 Validation of Key Targets in HPA Database The protein expression and distribution of CCND1, CDK2, PIK3CA and RELA in normal lung and NSCLC tissues were searched in the HPA database( https://www.proteinatlas.org/ ). 2.8 Gene Expression Level Analysis Expression levels of the selected key genes are analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA) database ( http://gepia2.cancer-pku.cn/#survival ) . The “Gene Expression Profile” option is selected, with a Log 2 FC cutoff of “1” and a p-value cutoff of “0.01,” choosing the disease type as “NSCLC” and selecting “Match TCGA normal and GTEx data” for Matched Normal data. The expression levels of the CCND1, CDK2, PIK3CA and RELA genes in NSCLC tissues compared to normal tissues are obtained and presented as Box Plots. 2.9 Overall Survival Analysis of Patients The overall survival of NSCLC patients is analyzed using the GEPIA database and the Kaplan-Meier Plotter (KM Plotter) database ( https://kmplot.com/analysis/ ). Key genes are retrieved separately in both GEPIA and KM Plotter databases, with high and low expression rates set at 50% and the disease type selected as “NSCLC”. Ultimately, survival curves for key genes in the CCND1, CDK2, PIK3CA and RELA signaling pathway among NSCLC patients are plotted. 2.10 Statistical analysis All experimental data were analyzed using Graphpad Prism 9 software, analysis of variance was performed between multiple groups. Independent sample t-test was performed between two groups, P < 0.05 was considered significant. All experiments were repeated at least three times. 3. Results 3.1 Collection of Targets for Isorhapontigenin and NSCLC We searched for potential targets of Isorhapontigenin from SwissTarget prediction, BATMAN-TCM, and STITCH databases, and obtained a total of 104 drug-related targets after deduplication. As shown in Fig. 2 , the 2D structure of Isorhapontigenin from PubChem database. 6688 NSCLC related targets were obtained from the Genecards database. 3.2 Potential targets and PPI network of Isorhapontigenin against NSCLC With the help of the online Venn analysis website, drug and disease-related targets were analyzed, as shown in Fig. 3 , a total of 79 potential targets were obtained by taking intersection. The 79 target proteins were imported into the STRING database to obtain protein- protein interaction network, as shown in Fig. 4 (A) . 3.3 key targets and PPI network of Isorhapontigenin against NSCLC In order to further screen the core target proteins, the STRING database function module was used to set the highest confidence level (0.9) and hide the disconnected targets. Finally, 55 key target proteins were obtained and their PPI network were plotted, as shown in Fig. 4 (B) . Based on network connectivity, R software was performed to sort targets in network, as shown in Fig. 5 , the key targets such as SRC, STAT3, PIK3CA, ESR1, IGF1R, CDK1, CDK 2, BCL2, RELA were finally identified. 3.4 GO enrichment analysis of key targets GO enrichment analysis was performed by Metascape platform, as shown in Fig. 6 . The biological processes involved in the enrichment of 55 key genes are as follows: response to xenobiotic stimulus, oxidative stress, regulation of inflammatory response, cell population proliferation, etc. Gene products are mainly enriched in nuclear envelope, cell body, membrane raft, receptor complex, etc. Molecular functions mainly focus on protein kinase activity, heme binding, protein domain specific binding, protein tyrosine kinase activity, etc. 3.5 KEGG enrichment analysis of key targets KEGG enrichment analysis was also based on the Metascape platform. As shown in Fig. 7 , KEGG enrichment analysis, found that the 55 potential key targets of Isorhapontigenin against NSCLC were mainly enriched in the cancer pathway, PI3K/AKT signaling pathway, cell cycle pathway, NF-κB signaling pathway, etc. 3.6 Computer Molecular Docking Analysis Select PIK3CA, CDK2, RELA and cell proliferation protein CCND1 as targets for molecular docking as they play important roles in PPI network, KEGG enrichment analysis results and cell proliferation cycle phenotype. As shown in Table 2 , the binding energy scores suggest that all four proteins can bind well with Isorhapontigenin especially the PIK3CA and CDK2 protein showing the most significant binding ability. The specific docking visualization results of the four targets are shown in Fig. 8 . Table 2 binding energy of PIK3CA, CDK2, RELA, CCND1 and Isorhapontigenin drug protein ID resolution ratio ligand binding energy(kj/mol) RMSD Isorhapontigenin CCND1 6P8E 2.3A / -6.3 0.744 Isorhapontigenin CDK2 3PXQ 1.9A / -8.0 0.692 Isorhapontigenin PIK3CA 7R9V 2.69A / -8.6 0.683 Isorhapontigenin RELA 9BDV 1.9A / -7 0.81 3.7 Isorhapontigenin significantly inhibits the proliferation of PC9 NSCLC cells As shown in Fig. 9 , after intervention of PC9 cells with different concentrations of Isorhapontigenin for 24 and 48 hours, cell proliferation was all inhibited, which was time and concentration-dependent. The most significant inhibition was observed at 100µM/L concentration, and half inhibition was achieved at 48 hours. Therefore,100µM/L concentration and intervention of PC9 cells for 48 hours as the intervention condition for the subsequent experiments. 3.8 Isorhapontigenin may induce PC9 cell proliferation inhibition and cell cycle arrest by inhibiting the PI3K/ NF-κB signaling pathway As shown in Fig. 10 , after 48 hours of drug intervention in PC9 cells, the expression levels of cell proliferation and cycle key genes (CCND1, CDK2) were significantly downregulated, and the expression levels of key genes in the PI3K/NF-κB signaling pathway (PIK3CA, RELA) of NSCLC cell proliferation were also significantly downregulated. 3.9 Validation of Key Targets in HPA Database HPA database showed that compared with normal lung tissue, the expression levels of CCND1, CDK2, PIK3CA and RELA in NSCLC tissue were significantly increased (Fig. 11 ). 3.10 Expression Differences and Survival Analysis To investigate the expression levels of key genes in the PIK3CA and NF-kB p65 (encoded by the RELA gene) signaling pathways in NSCLC patients and their impact on overall survival, we analyzed the expression levels of CCND1, CDK2, PIK3CA, and RELA in NSCLC tissues and normal lung tissues using the GEPIA and Kaplan-Meier databases. We further examined the overall survival of NSCLC patients under high and low expression conditions (50% each) for CCND1, CDK2, PIK3CA, and RELA (Fig. 12 – 13 ). The results showed that, compared to normal tissues, the expression of CCND1, CDK2, PIK3CA, and RELA was higher in NSCLC tissues. Additionally, NSCLC patients with lower expression levels of CCND1, CDK2, PIK3CA, and RELA exhibited longer overall survival compared to the control group. This suggests that these targets may play a critical role in extending the overall survival of NSCLC patients. 4. Discussion NSCLC has influenced a large number of patients and poses a great threat to them, seriously reduce the quality of life of patients and increasing the burden of social medical care. Up to now, there is still a shortage of effective drugs for treating NSCLC. In recent years, TCM has played an increasingly important role in non-surgical treatment of tumors, and large-scale clinical studies on some TCM prescriptions have gradually been carried out, providing tremendous assistance for drug therapy research of cancer( 14 ). Isorhapontigenin is an effective ingredient in the TCM (rheum officinale). In ancient China, rheum officinale was widely used in internal and external diseases( 15 , 16 ). Many modern pharmacological studies have also shown that rheum officinale extract and its many active ingredients have significant anti-cancer effects( 17 , 18 ). Therefore, in this study, we utilized network pharmacology, bioinformatics analysis, computer molecular docking strategies and in vitro cell experiments to explore the efficacy and mechanism of Isorhapontigenin in NSCLC. The research methods that integrating network pharmacology, bioinformatics and computer molecular docking strategies have significant advantages for new drug development. Through network pharmacology related databases, we successfully identified 104 potential targets of Isorhapontigenin and 6688 NSCLC related targets, and ultimately identified 55 key therapeutic targets. Many of them, such as STAT3, ESR1, SRC, EGFR, have been reported to play important roles in NSCLC( 19 – 22 ).Based on the results of network pharmacology and bioinformatics, we conducted computer molecular docking before cell experiments. We found that Isorhapontigenin could bind well to cell proliferation and cycle related proteins (CCND1, CDK2), as well as important signaling pathway proteins PIK3CA and NF-κBp65 (RELA gene codes). These indicates that Isorhapontigenin is highly likely to inhibit the proliferation of NSCL cells, induce cell cycle arrest and thus exert anti-tumor effects. The specific mechanism may involve the inactivation of the PI3K/NF-κB signaling pathway. To verify the above findings, we conducted in vitro experiments. The CCK-8 results showed that Isorhapontigenin could effectively inhibit the proliferation of PC9 lung cancer cells in a time/dose-dependent manner. The RT-PCR results showed that Isorhapontigenin significantly downregulated the expression levels of CCND1, CDK2, PIK3CA and RELA genes. As described in the network pharmacology analysis above, CDK2, PIK3CA and RELA are key targets of Isorhapontigenin in the anti-NSCLC treatment, and they have good docking binding abilities. Undoubtedly, CDK2 and CCND1 are cell cycle kinases and cyclins, respectively( 23 , 24 ). They regulate the cell cycle process and play an indispensable role in the growth process of tumors. PIK3CA is a subunit of the PI3K/AKT pathway protein PI3K, which is crucial for the activation of the PI3/AKT pathway( 25 ). RELA Gene, coding NF-κBp65 protein( 26 ), which is a downstream protein of the PI3K/AKT pathway and a core protein of the NF-κB pathway, regulating tumor inflammatory microenvironment and cell growth( 27 ). Many studies have reported the important role of the PI3K/NF-κB signaling in NSCLC( 28 – 31 ). Most importantly, CCK-8 and PCR experiments confirmed the above dry experiment analysis results. The pathological results provided by the HPA database were consistent with our study that CCND1, CDK2, PIK3CA and RELA were highly expressed in NSCLC tissues. Furthermore, Our survival analysis results showed that low expression of CCND1, CDK2, PIK3CA and RELA, potentially extending the overall survival of NSCLC patients. Overall, our study has found that Isorhapontigenin can effectively inhibit NSCLC for the first time, it may mainly through the PI3K/NF-κB signaling mechanism. Our research indicates that Isorhapontigenin is a novel potential therapeutic agent for the treatment of NSCLC. Of course, inevitably, this study also has certain limitations. For instance, the experimental part did not reveal the specific mechanism in depth, which needs further research and improvement in the future. Abbreviations NSCLC Non-small cell lung cancer TCM Traditional Chinese Medicine PPI protein-protein interaction network BP Biological Process CC Cellular Component MF Molecular Function GEPIA Gene Expression Profiling Interactive Analysis KM Plotter Kaplan-Meier Plotter Declarations Ethics approval and consent to participate We confirm that all experiments followed the Helsinki Declaration. Since the in vitro experiments were conducted on cell lines, the ETHICS Committee did not have to approve this study. Clinical trial number Not applicable. Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article /supplementary material. Conflicts of interest The authors have no conflicts of interest to declare. Funding This work was financially supported by the National Traditional Chinese Medicine Inheritance and Innovation Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, China (Grant No.2022ZD08); Administration of Traditional Chinese Medicine of Guangdong Province, China (Grant No.20241105); and Science and Technology Planning Project of Guangdong Province, China (Grant No.20221402). Author contributions Jiyong Wang and Yanfen Kang designed, guided and supervised the project. Zhiyu Wu and Chengyu Hou wrote the primary manuscript. Jiyong Wang and Yanfen Kang revised the manuscript. Chengyu Hou, Qiulin Zhu, Zixia Huang conducted the experiments and Network pharmacology analysis. Zesheng Lu conducted molecular docking work and draw pictures, Chunhui Shen, Yanzhong Liu, Zhenhui Wang provide valuable suggestions and assistance for the experiment, bioinformatics analysis and manuscript writing. 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Cancer-Associated Fibroblast-Derived Exosomal Microrna-20a Suppresses the Pten/Pi3k-Akt Pathway to Promote the Progression and Chemoresistance of Non-Small Cell Lung Cancer. Clin Transl Med (2022) 12(7):e989. doi: 10.1002/ctm2.989. Liu W, Wang H, Bai F, Ding L, Huang Y, Lu C, et al. Il-6 Promotes Metastasis of Non-Small-Cell Lung Cancer by up-Regulating Tim-4 Via Nf-Κb. Cell Prolif (2020) 53(3):e12776. Epub 20200205. doi: 10.1111/cpr.12776. Zhao J, Wang X, Mi Z, Jiang X, Sun L, Zheng B, et al. Stat3/Mir-135b/Nf-Κb Axis Confers Aggressiveness and Unfavorable Prognosis in Non-Small-Cell Lung Cancer. Cell Death Dis (2021) 12(5):493. Epub 20210514. doi: 10.1038/s41419-021-03773-x. Fumarola C, Bonelli MA, Petronini PG, Alfieri RR. Targeting Pi3k/Akt/Mtor Pathway in Non Small Cell Lung Cancer. Biochem Pharmacol (2014) 90(3):197-207. Epub 20140524. doi: 10.1016/j.bcp.2014.05.011. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1DataAvailabilitystatement.docx Supplementary information Additional file 1 : Data Availability statement Additionalfile2Theaccessaddressofthepublicdatabases.docx Additional file 2 : The access address of the public databases Additionalfile3Theoriginaldataandsupplementaryinformationofthisstudy.docx Additional file 3 : The original data and supplementary information of this study Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5882443","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":409563914,"identity":"46d2a9ea-f1d1-46c6-996e-53d2404283ef","order_by":0,"name":"Zhiyu Wu","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhiyu","middleName":"","lastName":"Wu","suffix":""},{"id":409563915,"identity":"d5379348-2f4f-4b52-a522-afe4223544ab","order_by":1,"name":"Chengyu Hou","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chengyu","middleName":"","lastName":"Hou","suffix":""},{"id":409563916,"identity":"7461491e-4565-4617-ab51-7981f236dbb0","order_by":2,"name":"Qiulin Zhu","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qiulin","middleName":"","lastName":"Zhu","suffix":""},{"id":409563917,"identity":"889afb2d-95df-405f-a696-26217fa56d7a","order_by":3,"name":"Zixia Huang","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zixia","middleName":"","lastName":"Huang","suffix":""},{"id":409563918,"identity":"b5c1f185-5b6a-42c7-b783-9bf8f1aae857","order_by":4,"name":"Zesheng Lu","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zesheng","middleName":"","lastName":"Lu","suffix":""},{"id":409563919,"identity":"d63dd649-7270-44ce-ba75-25ca56c4a314","order_by":5,"name":"Chunhui Shen","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chunhui","middleName":"","lastName":"Shen","suffix":""},{"id":409563920,"identity":"356686a1-f1e0-48a6-8368-4039d5d336f7","order_by":6,"name":"Zhenhui Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhenhui","middleName":"","lastName":"Wang","suffix":""},{"id":409563921,"identity":"5bcadb08-b88b-4226-9fee-18df443f9910","order_by":7,"name":"Yanzhong Liu","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yanzhong","middleName":"","lastName":"Liu","suffix":""},{"id":409563922,"identity":"bc09eb44-09d2-4955-ac03-4602e29c66ba","order_by":8,"name":"Yanfen Kang","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"prefix":"","firstName":"Yanfen","middleName":"","lastName":"Kang","suffix":""},{"id":409563923,"identity":"e8494224-235d-45ca-b96c-1e5eee47c310","order_by":9,"name":"JiYong Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACNv7mAwc+8LDV7z/ffPBBwg8bwlr4JI4lPpwhw8fYcONYssHDnjTCWuQYcoyNeWzkGBsO5KhJPmA7TITDGI6lSfDkmDEzNpxhq0jgOczA396dgF8Lc/MxCYkzaWzMzL3HbiRYpDNInDm7gbAthj3HeNgYzqXdSOCxZjCQyCWkJcdMIvHffwkeIKMggY2ZKC3GBgd42AwkgFoYEticidACCuQGHrYEA4ljyRKJPWk8BP0i39984PAfkBb+5oMff/ywkeNv78WvBQPwkKZ8FIyCUTAKRgFWAAA3rkjZ2Ag+OAAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"JiYong","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-01-22 16:23:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5882443/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5882443/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75409546,"identity":"e84e8d14-06cc-4419-92d6-1f46b012a223","added_by":"auto","created_at":"2025-02-04 09:03:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":274224,"visible":true,"origin":"","legend":"\u003cp\u003eThe workflow diagram of this study.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5882443/v1/637f3f67d81e7d6b3aa1ec74.png"},{"id":75411609,"identity":"c57edf16-3cb3-49d9-874b-f53926e97855","added_by":"auto","created_at":"2025-02-04 09:11:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10679,"visible":true,"origin":"","legend":"\u003cp\u003ethe 2D structure of Isorhapontigenin, CAS number 32507-66-7.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5882443/v1/915fba835b6ba46b8c6d336e.png"},{"id":75409548,"identity":"41edc945-0ecc-455b-a87c-6f0d61db60a5","added_by":"auto","created_at":"2025-02-04 09:03:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":39794,"visible":true,"origin":"","legend":"\u003cp\u003e79 potential targets of Isorhapontigenin against NSCLC were identified by online Venn website.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5882443/v1/91e736b64048c2243c53606c.png"},{"id":75409568,"identity":"d1e2debd-dcb5-40ae-9d23-bc67fd22b52a","added_by":"auto","created_at":"2025-02-04 09:03:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":427805,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003ePPI network of 79 targets protein that Isorhapontigenin against NSCLC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B) \u003c/strong\u003ePPI network of key targets protein that Isorhapontigenin against NSCLC(hide disconnected targets)\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5882443/v1/b064f561909548893071b3d2.png"},{"id":75409557,"identity":"a88e3e09-babe-4aa3-bbef-838bf874cfdc","added_by":"auto","created_at":"2025-02-04 09:03:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":123734,"visible":true,"origin":"","legend":"\u003cp\u003ethe rank of key targets protein that Isorhapontigenin against NSCLC based on network connectivity.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-5882443/v1/21f43e1ff207995aaf575a45.png"},{"id":75409550,"identity":"a82ce148-e284-4f77-a64f-2077138fc93b","added_by":"auto","created_at":"2025-02-04 09:03:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":232583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eGO enrichment analysis of 55 key targets (BP).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B) \u003c/strong\u003eGO enrichment analysis of 55 key targets (CC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C)\u003c/strong\u003eGO enrichment analysis of 55 key targets (MF).\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-5882443/v1/0b6cbd9774eff2245794421e.png"},{"id":75409547,"identity":"03cb6a7b-a839-4fed-95a1-3a23961fb761","added_by":"auto","created_at":"2025-02-04 09:03:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":163950,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG enrichment analysis of 55 key targets.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-5882443/v1/5ab6c942480e922542a7a141.png"},{"id":75409577,"identity":"b1ac49c0-8286-40f2-a489-98300456d216","added_by":"auto","created_at":"2025-02-04 09:03:57","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":256266,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eSpecific docking visualization of Isorhapontigenin and CCND1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B) \u003c/strong\u003eSpecific docking visualization of Isorhapontigenin and CDK2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C) \u003c/strong\u003eSpecific docking visualization of Isorhapontigenin and PIK3CA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(D) \u003c/strong\u003eSpecific docking visualization of Isorhapontigenin and RELA.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-5882443/v1/5470c58c47ea50e687901ee1.png"},{"id":75409563,"identity":"35ce0f9b-e8c4-4bf1-90af-20c1214517bd","added_by":"auto","created_at":"2025-02-04 09:03:57","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":44762,"visible":true,"origin":"","legend":"\u003cp\u003eIsorhapontigenin significantly inhibits the proliferation of PC9 cells (n=4).\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-5882443/v1/3a3fb1b67917d4bd60b2bf17.png"},{"id":75411614,"identity":"9116e65f-498c-4546-911a-d9f6b0faf8fb","added_by":"auto","created_at":"2025-02-04 09:11:57","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":73097,"visible":true,"origin":"","legend":"\u003cp\u003eIsorhapontigenin significantly downregulates the expression levels of cell. cycle related genes CCND1, CDK2, and key pathway genes PIK3CA and RELA(n=3)\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-5882443/v1/8244bdba564869c89aff5f21.png"},{"id":75409598,"identity":"41f0f9ca-001c-48ed-80fb-c59bc72788d3","added_by":"auto","created_at":"2025-02-04 09:03:58","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":983897,"visible":true,"origin":"","legend":"\u003cp\u003eProtein expression levels of key genes in the HPA database (200um).\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-5882443/v1/e8a995396d9087e6584427bf.png"},{"id":75411608,"identity":"1d93e3ad-519d-4d1a-9ee1-bc6c81d991c4","added_by":"auto","created_at":"2025-02-04 09:11:56","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":106495,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the Expression Levels of key genes in the PIK3CA and NF-kB p65 (encoded by the RELA gene) signaling pathways in NSCLC Tissues and Normal Tissues Using the GEPIA Database.\u003c/p\u003e","description":"","filename":"Fig12.png","url":"https://assets-eu.researchsquare.com/files/rs-5882443/v1/1daaabeaf997f59981c42c64.png"},{"id":75409596,"identity":"e2c34b0e-8083-4b04-98f0-dbb13454bc46","added_by":"auto","created_at":"2025-02-04 09:03:58","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":224138,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of overall survival in NSCLC patients based on key genes CCND1, CDK2, PIK3CA, and RELA using the GEPIA database and Kaplan-Meier Plotter (KM Plotter) database.\u003c/p\u003e","description":"","filename":"Fig13.png","url":"https://assets-eu.researchsquare.com/files/rs-5882443/v1/0bd4c2e1ddc07e0755985d0e.png"},{"id":76041952,"identity":"d2d3c84c-97fe-4c6b-80ad-9ccb3a2fa033","added_by":"auto","created_at":"2025-02-11 17:16:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4030076,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5882443/v1/9faba56b-a241-4bda-94d7-204bff3dc9bf.pdf"},{"id":75411605,"identity":"0885c898-921c-4efe-94aa-b30c7bc74648","added_by":"auto","created_at":"2025-02-04 09:11:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10844,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional file 1 : Data Availability statement\u003c/p\u003e","description":"","filename":"Additionalfile1DataAvailabilitystatement.docx","url":"https://assets-eu.researchsquare.com/files/rs-5882443/v1/28eaeb4c4dc63f9cd77bed45.docx"},{"id":75409559,"identity":"45e2876c-5e8c-47bb-bbe5-d33ebc603c85","added_by":"auto","created_at":"2025-02-04 09:03:56","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15397,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2 : The access address of the public databases\u003c/p\u003e","description":"","filename":"Additionalfile2Theaccessaddressofthepublicdatabases.docx","url":"https://assets-eu.researchsquare.com/files/rs-5882443/v1/6950eabbf791b78da1375b63.docx"},{"id":75409565,"identity":"17c0f887-9731-4441-8f58-dad58a73d1c8","added_by":"auto","created_at":"2025-02-04 09:03:57","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14685,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 3 : The original data and supplementary information of this study\u003c/p\u003e","description":"","filename":"Additionalfile3Theoriginaldataandsupplementaryinformationofthisstudy.docx","url":"https://assets-eu.researchsquare.com/files/rs-5882443/v1/73f8f6f66a365aadd221d9f6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Isorhapontigenin is a novel potential therapeutic agent for lung cancer： evidence from network pharmacology, bioinformatics, molecular docking and in vitro experiments","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung cancer is one of the most common tumors in humans and is a leading cause of death. According to previous study, nearly 1.8\u0026nbsp;million people are diagnosed with lung cancer every year(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Non-small cell lung cancer (NSCLC) accounts for the majority of lung cancers(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), however, currently, the treatments for NSCLC such as surgery and some drugs are limited and even have varying degrees of toxic and side effects(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Therefore, it is necessary to develop new safe and effective potential therapeutic drugs.\u003c/p\u003e \u003cp\u003eIn recent years, Traditional Chinese Medicine (TCM) has been widely reported to have good anti-cancer effects and has attracted increasing attention from researchers(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Some active ingredients in TCM such as quercetin, luteolin and kaempferol, have been shown to inhibit the growth of lung cancer cells(\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Isorhapontigenin, a stilbene compound, can be extracted from rheum officinale. Previous studies have showed that it has a wide range of pharmacological effects, such as anti-inflammatory and antioxidant effects(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), and some tumor studies have also reported that it has good anti-cancer effects(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, it is still unclear whether it can treat NSCLC and the related molecular mechanisms of action.\u003c/p\u003e \u003cp\u003eNetwork pharmacology is a systematic pharmacology method, which is widely used in the study of TCM and their components' mechanisms of action(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). By utilizing various databases of TCM and chemical components, network pharmacology can quickly identify potential targets and pathways of TCM against diseases and saving researchers or institutions a lot of experimental funds and valuable research time. At the same time, it also provides theoretical basis for subsequent in vitro and in vivo experiments. Molecular docking strategy is a computer simulation method that widely used in new drug development research(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). With the help of molecular docking strategy, the binding possibility of drugs and their targets can be preliminarily determined, laying foundation for determining drug targets. In recent years, with the development of traditional Chinese medicine databases and computational biology, network pharmacology and molecular docking strategies have often been integrated for drug research.\u003c/p\u003e \u003cp\u003eIn this study, we first used databases to obtain the therapeutic targets of Isorhapontigenin and NSCLC and further obtained the therapeutic targets of Isorhapontigenin through network pharmacology strategies. Then, we conducted bioinformatics analysis of key therapeutic targets, preliminarily validated some key targets through molecular docking strategies, and finally verified the efficacy and mechanism of Isorhapontigenin in treating NSCLC through in vitro cell experiments. The entire research process was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Collection of Isorhapontigenin and NSCLC targets\u003c/h2\u003e \u003cp\u003eThe \u0026ldquo;Canonical SMILES\u0026rdquo; format of Isorhapontigenin was obtained by searching curcumin in the PubChem database \u003cem\u003e(\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e, then it was inputted into the Swiss target prediction database for get potential targets. What is more, BATMAN-TCM \u003cem\u003e(\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bionet.ncpsb.org.cn/batman-tcm/\u003c/span\u003e\u003cspan address=\"http://bionet.ncpsb.org.cn/batman-tcm/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e and STITCH database \u003cem\u003e(\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://stitch-beta.embl.de/\u003c/span\u003e\u003cspan address=\"http://stitch-beta.embl.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e were also used to acquire its potential targets. Finally, summarize all targets and eliminate duplicates. The targets related to NSCLC were obtained by searching the human gene database Genecards \u003cem\u003e(\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Targets of Isorhapontigenin against NSCLC\u003c/h2\u003e \u003cp\u003eThe online Venn website\u003cem\u003e(\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.psb.ugent.be/webtools/Venn/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.psb.ugent.be/webtools/Venn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e was used to obtain potential targets for the treatment of non-small cell lung cancer with isoflavones. The parameter conditions are set according to the default conditions of the website. After uploading drugs and disease targets, the website is run for online analysis to obtain online Venn map and potential target files.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Construction of PPI network and identification of key targets\u003c/h2\u003e \u003cp\u003eIn order to obtain the protein-protein interaction network (PPI) and core targets, potential therapeutic targets will be imported into the STRING database \u003cem\u003e(\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.string-db.org/\u003c/span\u003e\u003cspan address=\"https://www.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e for protein-protein interaction analysis, with the species set as human and other parameter conditions set as default. Obtain a preliminary protein-protein interaction network file, then use the highest PPI score (confidence from 0.4 to 0.9) and remove disconnected targets in the network, and to obtain PPI of the core target. Finally, running the sorting script that installed in R software4.0.2 to obtain sorting information for different core target based on network connectivity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 GO and KEGG enrichment analysis\u003c/h2\u003e \u003cp\u003eThe Metascape platform\u003cem\u003e(\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metascape.org/\u003c/span\u003e\u003cspan address=\"https://metascape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e was used to analyze the final core therapeutic targets. The steps are briefly as follows. First, input the core targets into the gene list column of Metascape, then set the species as \u0026ldquo;Homo sapiens\u0026rdquo; with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01, performing GO and KEGG enrichment analysis based on the core target, download the result image file finally. GO enrichment analysis includes Biological Process (BP), Cellular Component (CC), Molecular Function (MF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Computer Molecular Docking\u003c/h2\u003e \u003cp\u003eSelect core proteins from the core targets and key signaling pathways for computer molecular docking to preliminarily validate the core functional targets of Isorhapontigenin. As mentioned above, Retrieve Isorhapontigenin in the Pubchem database and download small molecule ligand 3D structures (3D Conformers) with SDF format, then using open babel 3.1.1 to convert the SDF format of small molecule ligands to mol2 format. Obtain the protein 3D structure in the RCSBPDB database \u003cem\u003e(\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e, the limiting condition was set to human protein and small molecule ligand information in the structural complex. After downloading the PDB format, remove water molecules and small molecule ligands using Pymol 2.4.0 software and save the PDB format as the receptor protein. Perform hydrogenation and other processing on protein receptors and small molecule ligands in Autodock 1.5.6 software, save them as PDBQT format files, set appropriate docking boxes and parameters, use Autodock Vina 1.1.2 for molecular docking, save the results, and select some results for visual analysis in Pymol. If the binding energy score is less than or equal \u0026minus;\u0026thinsp;5, it indicates that the ligand and receptor can bind well.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Cell experiments\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1 CCK-8 assay\u003c/h2\u003e \u003cp\u003eIsorhapontigenin was purchased from Alfa Biotechnology Co.Ltd (Chengdu,China), purity\u0026thinsp;\u0026ge;\u0026thinsp;98%. It was dissolved in DMSO (Sigma,USA) to a concentration of 100mM/L mother liquor for later use. PC9 lung cancer cells were purchased from Noble Biological Products Co.Ltd (nobcell 0415,Hangzhou, China). After the cells were fully grown, they were seeded and cultured in a 96-well cell culture plate with approximately 8000 cells per well. After 24 hours of cell adhesion, they were observed under microscope. Then different concentrations of drugs were used to intervene in the cells for 24 and 48 hours. After intervention, the culture medium was discarded and 110ul culture medium (containing 10ul of CCK-8 solution, fetal bovine serum free) was added to each well. CCK-8 reagent was purchased from Suzhou Xinsaimei Biotechnology Co., Ltd. After incubating CCK-8 for 1 hour, the OD value was detected at 450nm by microplate reader (Thermo Fisher,USA), the drug concentration that meets the IC50 condition is considered for subsequent cell experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.6.2 Real time fluorescence quantitative PCR assay\u003c/h2\u003e \u003cp\u003eAccording to the CCK-8 results, the cell inoculation and intervention for PCR experiments were performed. After cell intervention, the culture medium was discarded and the cells were washed twice with PBS(Gibco, USA).Total RNA was extracted using the trizol method, the purity and concentration of RNA were detected by a spectrophotometer(Thermo Fisher,USA),. RNA with a purity of at least 1.7 was used for subsequent experiments. The obtained RNA was reverse transcribed into cDNA using reverse transcription kit (Takara,Japan), and then diluted to a certain extent for PCR amplification. The relevant primer sequences are as shown in the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eprimer sequences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman GAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCAGCAAGAGCACAAGAGGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGAGGAGGGGAGATTCAGTGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman CCND1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGAGGGACGCTTTGTCTGTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTTCTGCTGGAAACATGCCG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman CDK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGACACGCTGCTGGATGTCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGAGGACCCGATGAGAATGGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman PIK3CA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAGAGCCCCGAGCGTTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACTAGGATTCTTGGGGGCAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman RELA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCTTCCAAGAAGAGCAGCGTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTGCCAGAGTTTCGGTTCAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Validation of Key Targets in HPA Database\u003c/h2\u003e \u003cp\u003eThe protein expression and distribution of CCND1, CDK2, PIK3CA and RELA in normal lung and NSCLC tissues were searched in the HPA database(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Gene Expression Level Analysis\u003c/h2\u003e \u003cp\u003eExpression levels of the selected key genes are analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA) database \u003cem\u003e(\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/#survival\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/#survival\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e) .\u003c/em\u003e The \u0026ldquo;Gene Expression Profile\u0026rdquo; option is selected, with a Log\u003csub\u003e2\u003c/sub\u003e FC cutoff of \u0026ldquo;1\u0026rdquo; and a p-value cutoff of \u0026ldquo;0.01,\u0026rdquo; choosing the disease type as \u0026ldquo;NSCLC\u0026rdquo; and selecting \u0026ldquo;Match TCGA normal and GTEx data\u0026rdquo; for Matched Normal data. The expression levels of the CCND1, CDK2, PIK3CA and RELA genes in NSCLC tissues compared to normal tissues are obtained and presented as Box Plots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Overall Survival Analysis of Patients\u003c/h2\u003e \u003cp\u003eThe overall survival of NSCLC patients is analyzed using the GEPIA database and the Kaplan-Meier Plotter (KM Plotter) database \u003cem\u003e(\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kmplot.com/analysis/\u003c/span\u003e\u003cspan address=\"https://kmplot.com/analysis/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e Key genes are retrieved separately in both GEPIA and KM Plotter databases, with high and low expression rates set at 50% and the disease type selected as \u0026ldquo;NSCLC\u0026rdquo;. Ultimately, survival curves for key genes in the CCND1, CDK2, PIK3CA and RELA signaling pathway among NSCLC patients are plotted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll experimental data were analyzed using Graphpad Prism 9 software, analysis of variance was performed between multiple groups. Independent sample t-test was performed between two groups, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. All experiments were repeated at least three times.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Collection of Targets for Isorhapontigenin and NSCLC\u003c/h2\u003e \u003cp\u003eWe searched for potential targets of Isorhapontigenin from SwissTarget prediction, BATMAN-TCM, and STITCH databases, and obtained a total of 104 drug-related targets after deduplication. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the 2D structure of Isorhapontigenin from PubChem database. 6688 NSCLC related targets were obtained from the Genecards database.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Potential targets and PPI network of Isorhapontigenin against NSCLC\u003c/h2\u003e \u003cp\u003eWith the help of the online Venn analysis website, drug and disease-related targets were analyzed, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, a total of 79 potential targets were obtained by taking intersection. The 79 target proteins were imported into the STRING database to obtain protein- protein interaction network, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e(A)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.3 key targets and PPI network of Isorhapontigenin against NSCLC\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn order to further screen the core target proteins, the STRING database function module was used to set the highest confidence level (0.9) and hide the disconnected targets. Finally, 55 key target proteins were obtained and their PPI network were plotted, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e(B)\u003c/b\u003e. Based on network connectivity, R software was performed to sort targets in network, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the key targets such as SRC, STAT3, PIK3CA, ESR1, IGF1R, CDK1, CDK 2, BCL2, RELA were finally identified.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4 GO enrichment analysis of key targets\u003c/h2\u003e \u003cp\u003eGO enrichment analysis was performed by Metascape platform, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The biological processes involved in the enrichment of 55 key genes are as follows: response to xenobiotic stimulus, oxidative stress, regulation of inflammatory response, cell population proliferation, etc. Gene products are mainly enriched in nuclear envelope, cell body, membrane raft, receptor complex, etc. Molecular functions mainly focus on protein kinase activity, heme binding, protein domain specific binding, protein tyrosine kinase activity, etc.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5 KEGG enrichment analysis of key targets\u003c/h2\u003e \u003cp\u003eKEGG enrichment analysis was also based on the Metascape platform. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, KEGG enrichment analysis, found that the 55 potential key targets of Isorhapontigenin against NSCLC were mainly enriched in the cancer pathway, PI3K/AKT signaling pathway, cell cycle pathway, NF-κB signaling pathway, etc.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Computer Molecular Docking Analysis\u003c/h2\u003e \u003cp\u003eSelect PIK3CA, CDK2, RELA and cell proliferation protein CCND1 as targets for molecular docking as they play important roles in PPI network, KEGG enrichment analysis results and cell proliferation cycle phenotype. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the binding energy scores suggest that all four proteins can bind well with Isorhapontigenin especially the PIK3CA and CDK2 protein showing the most significant binding ability. The specific docking visualization results of the four targets are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ebinding energy of PIK3CA, CDK2, RELA, CCND1 and Isorhapontigenin\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003edrug\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprotein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eresolution ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eligand\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ebinding energy(kj/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRMSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsorhapontigenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCND1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6P8E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsorhapontigenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3PXQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsorhapontigenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePIK3CA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7R9V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.69A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsorhapontigenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRELA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9BDV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Isorhapontigenin significantly inhibits the proliferation of PC9 NSCLC cells\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, after intervention of PC9 cells with different concentrations of Isorhapontigenin for 24 and 48 hours, cell proliferation was all inhibited, which was time and concentration-dependent. The most significant inhibition was observed at 100\u0026micro;M/L concentration, and half inhibition was achieved at 48 hours. Therefore,100\u0026micro;M/L concentration and intervention of PC9 cells for 48 hours as the intervention condition for the subsequent experiments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.8 Isorhapontigenin may induce PC9 cell proliferation inhibition and cell cycle arrest by inhibiting the PI3K/ NF-κB signaling pathway\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, after 48 hours of drug intervention in PC9 cells, the expression levels of cell proliferation and cycle key genes (CCND1, CDK2) were significantly downregulated, and the expression levels of key genes in the PI3K/NF-κB signaling pathway (PIK3CA, RELA) of NSCLC cell proliferation were also significantly downregulated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Validation of Key Targets in HPA Database\u003c/h2\u003e \u003cp\u003eHPA database showed that compared with normal lung tissue, the expression levels of CCND1, CDK2, PIK3CA and RELA in NSCLC tissue were significantly increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Expression Differences and Survival Analysis\u003c/h2\u003e \u003cp\u003eTo investigate the expression levels of key genes in the PIK3CA and NF-kB p65 (encoded by the RELA gene) signaling pathways in NSCLC patients and their impact on overall survival, we analyzed the expression levels of CCND1, CDK2, PIK3CA, and RELA in NSCLC tissues and normal lung tissues using the GEPIA and Kaplan-Meier databases. We further examined the overall survival of NSCLC patients under high and low expression conditions (50% each) for CCND1, CDK2, PIK3CA, and RELA (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). The results showed that, compared to normal tissues, the expression of CCND1, CDK2, PIK3CA, and RELA was higher in NSCLC tissues. Additionally, NSCLC patients with lower expression levels of CCND1, CDK2, PIK3CA, and RELA exhibited longer overall survival compared to the control group. This suggests that these targets may play a critical role in extending the overall survival of NSCLC patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eNSCLC has influenced a large number of patients and poses a great threat to them, seriously reduce the quality of life of patients and increasing the burden of social medical care. Up to now, there is still a shortage of effective drugs for treating NSCLC. In recent years, TCM has played an increasingly important role in non-surgical treatment of tumors, and large-scale clinical studies on some TCM prescriptions have gradually been carried out, providing tremendous assistance for drug therapy research of cancer(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIsorhapontigenin is an effective ingredient in the TCM (rheum officinale). In ancient China, rheum officinale was widely used in internal and external diseases(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Many modern pharmacological studies have also shown that rheum officinale extract and its many active ingredients have significant anti-cancer effects(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Therefore, in this study, we utilized network pharmacology, bioinformatics analysis, computer molecular docking strategies and in vitro cell experiments to explore the efficacy and mechanism of Isorhapontigenin in NSCLC.\u003c/p\u003e \u003cp\u003eThe research methods that integrating network pharmacology, bioinformatics and computer molecular docking strategies have significant advantages for new drug development. Through network pharmacology related databases, we successfully identified 104 potential targets of Isorhapontigenin and 6688 NSCLC related targets, and ultimately identified 55 key therapeutic targets. Many of them, such as STAT3, ESR1, SRC, EGFR, have been reported to play important roles in NSCLC(\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).Based on the results of network pharmacology and bioinformatics, we conducted computer molecular docking before cell experiments. We found that Isorhapontigenin could bind well to cell proliferation and cycle related proteins (CCND1, CDK2), as well as important signaling pathway proteins PIK3CA and NF-κBp65 (RELA gene codes). These indicates that Isorhapontigenin is highly likely to inhibit the proliferation of NSCL cells, induce cell cycle arrest and thus exert anti-tumor effects. The specific mechanism may involve the inactivation of the PI3K/NF-κB signaling pathway. To verify the above findings, we conducted in vitro experiments. The CCK-8 results showed that Isorhapontigenin could effectively inhibit the proliferation of PC9 lung cancer cells in a time/dose-dependent manner. The RT-PCR results showed that Isorhapontigenin significantly downregulated the expression levels of CCND1, CDK2, PIK3CA and RELA genes. As described in the network pharmacology analysis above, CDK2, PIK3CA and RELA are key targets of Isorhapontigenin in the anti-NSCLC treatment, and they have good docking binding abilities. Undoubtedly, CDK2 and CCND1 are cell cycle kinases and cyclins, respectively(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). They regulate the cell cycle process and play an indispensable role in the growth process of tumors. PIK3CA is a subunit of the PI3K/AKT pathway protein PI3K, which is crucial for the activation of the PI3/AKT pathway(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). RELA Gene, coding NF-κBp65 protein(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), which is a downstream protein of the PI3K/AKT pathway and a core protein of the NF-κB pathway, regulating tumor inflammatory microenvironment and cell growth(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Many studies have reported the important role of the PI3K/NF-κB signaling in NSCLC(\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Most importantly, CCK-8 and PCR experiments confirmed the above dry experiment analysis results. The pathological results provided by the HPA database were consistent with our study that CCND1, CDK2, PIK3CA and RELA were highly expressed in NSCLC tissues. Furthermore, Our survival analysis results showed that low expression of CCND1, CDK2, PIK3CA and RELA, potentially extending the overall survival of NSCLC patients.\u003c/p\u003e \u003cp\u003eOverall, our study has found that Isorhapontigenin can effectively inhibit NSCLC for the first time, it may mainly through the PI3K/NF-κB signaling mechanism. Our research indicates that Isorhapontigenin is a novel potential therapeutic agent for the treatment of NSCLC. Of course, inevitably, this study also has certain limitations. For instance, the experimental part did not reveal the specific mechanism in depth, which needs further research and improvement in the future.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eNSCLC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNon-small cell lung cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTCM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTraditional Chinese Medicine\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eprotein-protein interaction network\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBiological Process\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCellular Component\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMolecular Function\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGEPIA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Expression Profiling Interactive Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKM Plotter\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier Plotter\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe confirm that all experiments followed the Helsinki Declaration. Since the in vitro experiments were conducted on cell lines, the ETHICS Committee did not have to approve this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article /supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the National Traditional Chinese Medicine Inheritance and Innovation Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, China (Grant No.2022ZD08); Administration of Traditional Chinese Medicine of Guangdong Province, China (Grant No.20241105); and Science and Technology Planning Project of Guangdong Province, China (Grant No.20221402).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJiyong Wang and Yanfen Kang designed, guided and supervised the project. Zhiyu Wu and Chengyu Hou wrote the primary manuscript. Jiyong Wang and Yanfen Kang revised the manuscript. Chengyu Hou, Qiulin Zhu, Zixia Huang conducted the experiments and Network pharmacology analysis. Zesheng Lu conducted molecular docking work and draw pictures, Chunhui Shen, Yanzhong Liu, Zhenhui Wang provide valuable suggestions and assistance for the experiment, bioinformatics analysis and manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the distinguished investigators for generously publishing their research data on Isorhapontigenin and lung cancer. The authors thank J-WY and Y-FK for their enthusiastic help and selfless assistance in initiating this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHirsch FR, Scagliotti GV, Mulshine JL, Kwon R, Curran WJ, Jr., Wu YL, et al. 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Epub 20140524. doi: 10.1016/j.bcp.2014.05.011.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Isorhapontigenin, Non-small cell lung cancer, network pharmacology, bioinformatics, molecular docking, experimental verification, Cell cycle","lastPublishedDoi":"10.21203/rs.3.rs-5882443/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5882443/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e: Isorhapontigenin is an effective active ingredient in rheum officinale, which has been reported to have anti-tumor effects. However, its effect and molecular mechanism on non-small cell lung cancer are still unclear.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e: Firstly, potential therapeutic targets of Isorhapontigenin against non-small cell lung cancer were obtained through network pharmacology analysis. Secondly, bioinformatics analysis was conducted to identify key targets and potential signaling pathway mechanisms based on the obtained potential targets. Then, evaluate the binding ability between Isorhapontigenin and key targets using computer molecular docking strategies. Finally, in vitro cell experiments were conducted to verify the effects and related targets of Isorhapontigenin on non-small cell lung cancer cells.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e: 104 drug targets and 6688 disease targets were acquired from SwissTarget prediction, BATMAN-TCM, STITCH and Genecards databases.79 potential therapeutic targets were identified through analysis based on online Venn website and PPI interaction analysis was performed on these targets to ultimately obtain 55 key targets. GO and KEGG analysis revealed that Isorhapontigenin mainly act on cell proliferation and cycle processes and PI3K/RELA/Cellcyle pathways to against non-small cell lung cancer. Computer molecular docking confirmed that Isorhapontigenin can bind to cell proliferation, cycle related proteins (CCND1, CDK2, PIK3CA, RELA). CCK-8 detection revealed that Isorhapontigenin significantly inhibited the proliferation of PC9 lung cancer cells, Moreover, RT-PCR detection showed that Isorhapontigenin downregulated the expression of CCND1, CDK2, PIK3CA and RELA genes. CCND1, CDK2, PIK3CA and RELA are highly expressed in NSCLC tissues. Overall survival analysis of patients indicated that key genes in the PIK3CA and NF-κBp65 signaling pathway significantly affected overall survival.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e: Our research has found that Isorhapontigenin can effectively against non-small cell lung cancer, and this effect may be achieved by inhibiting cell proliferation and cycle progression mediated by the PIK3CA/NF-KB signaling pathway. Isorhapontigenin is a new potential therapeutic agent for lung cancer.\u003c/p\u003e","manuscriptTitle":"Isorhapontigenin is a novel potential therapeutic agent for lung cancer： evidence from network pharmacology, bioinformatics, molecular docking and in vitro experiments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-04 09:03:50","doi":"10.21203/rs.3.rs-5882443/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fc4df719-56db-42f1-b1f1-7e3fb5d17537","owner":[],"postedDate":"February 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-08T09:09:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-04 09:03:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5882443","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5882443","identity":"rs-5882443","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}