银杏叶治疗胶质母细胞瘤机制研究（英文）_杨甫.pdf

南 开 大 学 学 报（自然科学版）Acta Scientiarum Naturalium Universitatis NankaiensisVol.561Feb.2023第56卷第1期2023年2月Article ID:0465-7942(2023)01-0026-09Research on the Mechanism of Ginkgo Folium to Treat GlioblastomaYang Fu1,2,Wang Liuli3,Li Jinxing2,Sun Aigang2,Fu Yao4,Wang Zengyong4,Wang Mingguang2(1.Guangzhou University of Chinese Medicine,Guangzhou 510006,China;2.Department of Neurosurgery,Linyi Peoples Hospital,Linyin 371300,China;3.Lanshan Maternal and Child Health Hospital,Linyi 276003,China;4.Central Laboratory,Linyi People s Hospital,Linyin 371300,China)Abstract:Glioblastoma(GBM)is a malignant tumor with a high incidence rate and high invasion in hu-man central nervous system tumors.However,the causes of the occurrence and development of glioblasto-ma are not clear.Although Ginkgo Folium(GF)extract can reduce the growth of U-87 glioma cell linesin nude mice,the action mechanism remains unclear.In this study,network pharmacology,molecular dock-ing,and cell experiment were utilized to explore the underlying molecular mechanism of GF in treatingGBM.The effective ingredients and action targets of GF were obtained from the TCMSP database.STRING database was applied for protein interaction analysis and Cytoscape software was used to buildthe compound-target-disease network and PPI network.The R language was adopted as gene ontology(GO)analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis.AutoDock soft-ware was used for molecular docking verification.A cell experiment was employed to verify the inhibitionof GBM by the effective compounds in GF.The results showed that 27 potential effective components and112 potential targets of GF were selected.7 435 targets of GBM were screened from six databases,and98 overlapping targets were obtained.In active component-target network,quercetin,kaempferol,stigmas-terol,and luteolin were the important active components.In PPI network,ESR1,RELA,FOS,CCD1,EGFR,AR,HIF1A,MAPK8,NCOA1,and MDM2 were the core targets.GO and KEGG analysesshowed that the main pathways of GF in treating GBM involved PI3K-Akt,MAPK,and TNF signalingpathway.The results of molecular docking verified the good binding activity of the key pharmacodynamicmolecules with the core targets.Cell experiments showed that quercetin(QUE),luteolin(LUT),andkaempferol(KAE)can inhibit cell viability in GBM cell lines U251.It can be concluded GF is the resultof multi-component,multi-target,and multi-pathway interaction in the treatment of GBM,which provid-ed a theoretical basis for the clinical application of GF in the treatment of GBM.Keywords:glioblastoma;ginkgo folium;mechanism;network pharmacology;molecular docking technologyCLC number:O177Document code:A0IntroductionGlioma,originating from the neuroepithelium,is the most common central nervous system tumorin adults and accounts for 40%-50%of brain tumors.GBM(glioblastoma)is a highly malignant tumor,accounting for 70%of intracranial malignancies1.The median survival of GBM patients is only 12-15months,with the majority dying within 2 years and the 5-years survival rate less than 5%2-3.Untilnow surgical resection combined with postoperative chemoradiotherapy is the main treatment for glioma.Received date:2022-04-08Foundation item:Supported by the Doctoral Innovation Project of Linyi Peoples Hospital(2018LYBS12)Biography:Yang Fu(1986-),male,native place:Shandong Linyi,doctoral students,attending doctor.Corresponding author:Wang Mingguang(1975-),male,native place:Shandong Weihai,Deputy Chief Doctors,direction of research:brain tumor and verve spine.E-mail:杨 甫等：银杏叶治疗胶质母细胞瘤机制研究第1期杨 甫等：银杏叶治疗胶质母细胞瘤机制研究 27 Due to the characteristics of invasive growth and progressive malignancy of gliomas,radiation therapyand chemotherapy can only eliminate tumor cells or delay recurrence to a certain extent,residual tumorcells can cause a relapse in a short period4,and radiotherapy can also be damaging to patients to a certainextent5-6.Some patients develop drug resistance,which reduces the effectiveness of chemotherapy drugs,and the results achieved with individually targeted therapies are unsatisfactory7.Therefore,it is extremelyimportant to investigate new effective drugs for GBM treatment.Natural products have shown a wide range of pharmacological or biological activities,makingthem a treasure trove for research and the search for active drugs against GBM.Ginkgo Folium(GF)extract and its components have long been reported to be used for the treatment of cancerthousands of years ago in China.GF has achieved good effects in the treatment of hepatocellular car-cinoma8,lung cancer9,prostate cancer10,and other diseases.Studies have found that GF extractcan promote apoptosis of U87 glioma cells11,but there is no relevant study on the role of GF inGBM,and there is no in-depth study on the mechanism of GF inhibiting tumor cells.Network pharmacology can help explore the systemic effects of TCMs by combining the meth-ods of biology,pharmacology,and bioinformatics methods.These technologies help us to access thepotential mechanisms of action of TCM in treatment and ultimately develop new drugs.This studyaims to explore GFs pharmaceutical potential and systematically assess GFs therapeutic targets andmechanisms in GBM via network pharmacology.1MaterialsandMethods1.1 Screening ofActive CompoundsAll constituents of GF were searched and collected from the Traditional Chinese Medicine Sys-tems Pharmacology Database and Analysis Platform(TCMSP,https:/ drug-likeness(DL)indices recommended by TCMSP were employed to verifydrug ability of each candidate.OB refers to the extent and rate at which a drugs active ingredientor active part is absorbed and available at the drug action site12.High OB seems more likely to bea drug-like ingredient.The DL index was used to evaluate the chemical suitability of compounds13.Active compounds were acquired by the following criteria:OB30%and DL0.1814and candidatecompounds should be screened for subsequent analysis.1.2 Predicting Drug TargetsThe targets of active compounds of GF were predicted by the TCMSP database.1.3 Mining GF-and GBM-associated targets and target-genesProtein targets associated with GBM were provided by the GeneCards,PharmGkb,OMIM,TTD,DrugBank,and DisGeNET databases with“glioblastoma”as the keywords.After the removalof repeated targets,Venny2.1.0 was used to screen the intersection of drug targets and disease tar-gets to obtain the potential targets of GF in the treatment of GBM.1.4 Building active component-target networkThe potential targets were imported into Cytoscape to construct the components-target-network.1.5 Building PPI network and Screening of core target proteinsThe PPI network was built using STRING(https:/string-db.org/)and visualized by Cytoscapesoftware.The network nodes and edges denote proteins and protein-protein associations,respectively.Thecore targets were screened by topological scoring.28 南 开 大 学 学 报（自然科学版）第56卷1.5 Gene Ontology(GO)and pathway enrichment analysisFirst,potential target gene names were transformed into entrezID by the R package“org.Hs.eg.db,version 3.8,”which helps to exclude errors caused by capitalization or abbreviations of the target name.Then,GO biological functions and the KEGG pathway enrichment analysis were visualized with the Rpackages“DOSE,”“clusterProfiler,”and“pathview,”for which the p-value was 0.05 for further analysis.1.6 Molecular DockingThe top 10 important targets with high network connection degrees were selected for moleculardocking analysis using Autodock Vina.The smaller the binding energy(affinity)was,the more sta-ble the interaction between the target protein and the active ingredient was.1.7 Cell cultureThe human glioma U251 cells were cultured with DMEM medium(Gibco,Inc)supplementedwith 10%FBS and 1%penicillin-streptomycin(Gibco,Inc)in an incubator at 37 and 5%CO2for 24 h.These cells are obtained from our laboratory.1.8 Cell ViabilityAssayCCK-8(Bestbio,Inc)was used to measure the cell viability of U251 cells treated with quercetin,kaempferol,and luteolin,(98%purity)respectively.U251 cells were seeded into 96 well plates duringlogarithmic growth at a density of 2103cells per well,and cultured for at least 24 h to adhere.Then,the cells were treated with different concentrations of drugs.The drugs are quercetin(0,5,10,25,and50 mol/L),luteolin(0,5,10,25,and 50 mol/L),and kaempferol(0,5,10,25,and 50 mol/L),respectively.Following treatment for 48 h at 37,10 L CCK 8 reagent was added to the cellsfollowed by incubation for 4 h at 37.Then the absorbance was measured at a wavelength of 450 nmusing a Bio-Rad ELISA microplate reader(Bio Rad Laboratories,Inc.,Hercules,CA,USA).Theviability rate was calculated as follows:survival rate=(OD450experimental group-OD450blank group)/(OD450control group-OD450blank group)100%.Three independent experiments were performed.Allreagents were purchased from Sigma unless otherwise stated.2Results2.1 ScreeningActive Components of GFA total of 307 compounds were collected from the TCMSP database,with OB30%,DL 0.18,and 27 potential effective components were obtained(Table 1).2.2 Drug Target PredictionA total of 112 potential targets were selected.2.3 Screening of Disease TargetsThe keyword“glioblastoma”was used to search the reportedtumor-relatedgenesinthesixdiseasedatabasesGeneCards,PharmGkb,OMIM,TTD,DrugBank,and DisGeNET databases.Atotal of 7 435 genes were obtained after the deletion of duplicates(Fig.1).A total of 98 overlapping genes wereiltered as candidatetargets.Venn diagram data are shown in Fig.2.2.4Analysis of theActive Component-Target NetworkThe 98 potential targets were analyzed by Cytoscape to constructthe active component-target interaction network(Fig.3).DrugBank719712DisGeNETTTDPharmGkbOMIMGeneCards21103110113022 27213065175Fig.1 The Venn diagram of7 435 potential targets第1期杨 甫等：银杏叶治疗胶质母细胞瘤机制研究 29 The result included 118 nodes and 338 edges.Different componentsindicateddifferenttargets.Amongthem,quercetin,luteolin,andkaempferol might be the important active components in the network.2.5 PPI NetworkAnalysisA total of 98 nodes and 199 edges were involved in the PPI network(Fig.4A).The bar chart of the top 10 target proteins was drawn based onthe degree value(Fig.4B).Among them,ESR1,RELA,FOS,CCD1,EGFR,AR,HIF1A,MAPK8,NCOA1,and MDM2 degree values were40,34,32,26,24,22,22,22,22,and 20respectively,which were the core nodes of thenetwork,suggesting that GF might play a sig-nificant role in the protection of GBM throughthem.2.6 GO and KEGG Pathway Enrichment98potential targets were analyzed using theBioconductor platform,and the GO terms(BP,CC,and MF)and KEGG signaling pathwayswere selected.Targets in the BP were closelyrelated to the cellular response to chemical stressand response to oxidative stress and regulation ofthe apoptotic signaling pathway.In the CC,GFaffected the transcription regulator complex,RNApolymerase II transcription regulator complex,andpeptidasecomplex.AttheMFlevel,drugcomponentsof GF were DNA-binding transcription factor binding,RNA polymerase II-specific DNA-binding transcription factor binding,and nuclear receptor activity(Fig.5).Mol IDMOL011578MOL002680MOL011586MOL011587MOL011588MOL011589MOL011594MOL011597MOL011604MOL001490MOL001494MOL001558MOL002881MOL003044Molecule nameBilobalideFlavoxanthinginkgolide Bginkgolide Cginkgolide JGinkgolide MIsogoycyrolLuteolin-4 glucosideSyringetinbis(2S)-2-ethylhexylbenzene-1,2-dicarboxylateMandenolsesaminDiosmetinChryseriolOB/%84.4260.4144.3848.3344.8449.0940.3641.9736.8243.594256.5531.1435.85DL0.360.560.730.730.740.750.830.790.370.350.190.830.270.27Mol IDMOL000354MOL000358MOL000422MOL000449MOL000492MOL005573MOL000006MOL007179MOL009278MOL000096MOL000098MOL002883MOL005043Molecule nameisorhamnetinbeta-sitosterolkaempferolStigmasterol(+)-catechinGenkwaninluteolinLinolenic acid ethyl esterLaricitrin(-)-catechinquercetinEthyl oleate(NF)campest-5-en-3beta-olOB/%49.636.9141.8843.8354.8337.1336.1646.135.3849.6846.4332.437.58DL0.310.750.240.760.240.240.250.20.340.240.280.190.71Table 1 The main active compounds of GFDrugDisease14987 337Fig.2 Venn diagram of the overlapping targets of GF and GBMFig.3 Network model of bioactive ingredient-target 30 南 开 大 学 学 报（自然科学版）第56卷A total of 138 KEGG pathways were mainly involved,including PI3K-Akt,MAPK,and TNF signalingpathway(Fig.6).2.7 Molecular DockingThe top 10 targets in the PPI network and 2.4 active component-target network were analyzed formolecular docking with the three active compounds of GF.It was found that the three active componentsof GF had a good binding affinity with the core target of its interaction(Fig.7).Through analysis of theactive component-target network,three active compounds and their targets and the binding site ofcompounds-targets are shown in Fig.8.2.8 Quercetin(QUE),Luteolin(LUT),and Kaempferol(KAE)Inhibit Cell Viability in GBM Cell Lines U251As shown in Fig.9,all concentrations of QUE,LUT,and KAE inhibited the rate of proliferationof U251 cells,and the inhibitory effects of QUE,LUT,and KAE were both time and concentration-dependent,respectively.In addition,the inhibition effects of QUE and LUT are higher than that of KAE.AB20222222222426323440MDM2NCOA1MAPK8HIF1AAREGFRCCD1FOSRELAESR1402030100Fig.4 The PPI network diagram(A)and the top 10 intersecting targets bar graph with degree values in the PPI network(B)Cellular response to chemical stressResponse to ketoneResponse to oxidative stressCellular response to oxidative stressGland developmentResponse to radiationResponse to xenobiotic stimulusResponse to peptideResponse to reactive oxygen speciesCellular response to xenobiotic stimulusMembrane raftMembrane microdomainTransciption regulator complexRNA polymerase transciption regulator complexVesicle lumenPeptidase complexEndocytic vesicle membraneMitochondrial unter membraneEndocytic vesicle membraneNuclear envelopeDNA-binding transciption factor bindingRNA polymerase-specific DNA-binding transciption factor bindingTransciption coregulator bindingNuclear receptor activityLigand-activated transciption factor activityTransciption coactivator bindingUbiquitin-like protein ligase bindingDNA-binding transciption activator activity,RNA polymerase-specificDNA-binding transciption activator activityoxidoreductase activity,acting on paired donors,with incorporation or reduction of molecular oxygenCount0 510152025BPMFCCqvalue0.002 50.005 00.007 50.010.012 5Lipid and atherosclerosisChemical carcinogenesis-receptor activationPI3K-Akt signaling pathwayHuman cytomegalovirus infectionHepatitis BChemical carcinogenesis-reactive oxygen speciesMAPK signaling pathwayMicro-RNAs in cancerFluid shear stress and atherosclerosisProteoglycans in cancerProstate cancerApoptosisEpstein-Barr virus infectionAGE-RAGE signaling pathwayin diabetic complicationsTNF signaling pathwayAlcoholic liver diseaseHepatocellular cacinomaMeaslesBreast cancerHepatitis CColorectal cancerEndocrine resistanceThyroid hormone signaling pathwayp53 signaling pathwayPancreatic cancerBladder cancer