OL Oncology Letters 1792-1074 1792-1082 D.A. Spandidos 10.3892/ol.2019.10146 OL-0-0-10146 Articles Analysis of the role of DAMTC in lung adenocarcinoma cells based on the DNA microarrays WangBinliang CaiYuanyuan KongYiming LiXiaobo FuHaiwei ZhangSong ZhangTianwei Respiratory Medicine Department, Taizhou First People's Hospital, Taizhou, Zhejiang 318020, P.R. China Correspondence to: Dr Binliang Wang, Respiratory Medicine Department, Taizhou First People's Hospital, 218 Hengjie Road, Taizhou, Zhejiang 318020, P.R. China, E-mail: 1060342144@qq.com 06 2019 15 03 2019 17 6 4787 4794 04052016 21012019 Copyright: © Wang et al. 2019 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

The present study aimed to investigate the effect of 7, 8-diacetoxy-4-methylcoumarin (DAMTC) on lung adenocarcinoma cells (A549) and analyze the molecular mechanism underlying DAMTC-treated lung adenocarcinoma. Gene expression profile GSE29698 was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) in 3 DAMTC-treated A549 samples were analyzed and compared with 3 DAMTC-untreated samples using the limma package. Gene Ontology (GO) and pathway enrichment analyses of DEGs were performed, followed by the functional annotation and protein-protein interaction (PPI) network construction. Finally, pathway crosstalk analysis was conducted. A total of 500 upregulated and 389 downregulated DEGs were identified. The upregulated and downregulated DEGs were enriched in different GO terms and pathways, including metabolic process, p53 signaling pathway and metabolic pathways. A total of 9 DEGs were determined to have node degrees >16 in the PPI network, including interleukin 6 (IL6), MDM2 oncogene, E3 ubiquitin protein ligase (MDM2), cell division cycle 42 (CDC42) and MYC associated factor X (MAX). Furthermore, numerous DEGs were identified to function as transcription factors and tumor suppressor genes, including histone deacetylase 3 and MAX. Additionally, apoptosis, tight junction, and endocytosis pathway were determined to cross-talk with small cell and non-small cell lung cancer. The DEGs (IL6, MDM2, CDC42 and MAX) involved in different pathways, including the p53 signaling pathway and endocytosis, may be the potential targets for DAMTC in lung adenocarcinoma. The elucidation of the underlying mechanism of the DAMTC effect may make it a potential drug.

 lung adenocarcinoma differentially-expressed genes protein-protein interaction network pathway crosstalk analysis Introduction

Lung cancer remains the leading cause of cancer-associated mortality, annually resulting in >1,000,000 mortalities globally in 2012 (1). Additionally, ~1,200,000 new cases are diagnosed every year and prognoses of lung cancer are poor, as estimated worldwide in 2012 (2). Lung adenocarcinoma is the predominant histological subtype of non-small lung cancer (3). The mean 5-year survival rate of lung adenocarcinoma is <15% in the urban areas of China as estimated in 2015, primarily due to the late-stage detection and a paucity of late-stage treatments (4). The availability of effective anticancer drugs is considered to be the key for improvement in the treatment of lung adenocarcinoma.

7, 8-Diacetoxy-4-methylcoumarin (DAMTC) is a thioderivative of 4-methyl coumarin (5). Coumarins belong to the flavonoid class of plant secondary metabolite, which have been demonstrated to exhibit diverse and beneficial biological activities, including antitumoral, anticoagulant and anti-inflammatory properties (5). Natural coumarins or synthetic analogs have attracted intense interest for their applicability as drugs (6). They have been determined to have a variety of therapeutic applications, including antitumor and anti-human immunodeficiency virus therapy (7), and antioxidant (8) and antibacterial (9) applications. Lacy and O'Kennedy (10) demonstrated that genistein and esculetin could exert the most potent inhibitory effect on cell growth of two cell lines, A549, a lung carcinoma cell line, and MCF-7, a breast carcinoma cell line. Previously, the coumarin derivative DAMTC was indicated to inhibit cellular proliferation and induce apoptosis on human non-small cell lung cancer A549 cells (11). Furthermore, the observations of Goel et al (12) reported that upregulation of the nuclear factor-κB, p53 and Akt pathways, and downregulation of the mitogen activated protein kinase (MAPK) and Cox-2 pathways were involved in the molecular mechanism of apoptosis induction by DAMTC in A549 cells. However, the mechanisms of the anti-proliferative effects of DAMTC in lung adenocarcinoma are incompletely defined, and further insights into the mechanisms are required.

Previously, Goel et al (1) used the integrated proteomics and transcriptomics approach, and identified that DAMTC could regulate cell motility and cytoskeletal reorganization in lung adenocarcinoma. In the present study, differentially-expressed genes (DEGs) were identified in DAMTC-treated lung adenocarcinoma, compared with DAMTC-untreated controls, using the same gene expression profile. Comprehensive bioinformatics were used to analyze the significant pathways and functions, and to construct the protein-protein interaction (PPI) network, to determine the critical DEGs. Furthermore, the putative interactions between signaling pathways were analyzed. The present study aimed to investigate the potential molecular mechanism underlying DAMTC-induced apoptosis and inhibition of cell motility in lung adenocarcinoma.

 Materials and methods <sec> <title>Microarray data and data preprocessing

The gene expression profile of GSE29698, deposited by Goel et al (1), was downloaded from the Gene Expression Omnibus database in National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/geo/) based on the platform of GPL6884 Illumina HumanWG-6 v3.0 expression beadchip. A total of 6 specimens were applied, including 3 specimens of DAMTC-treated lung adenocarcinoma cells (A549) and another 3 specimens of DAMTC-untreated A549 cell lines as controls.

The gene expression profile data were preprocessed using the limma (13) package in Bioconductor. Following background correction, quantile normalization and probe summarization, the gene expression matrix of specimens was received.

 DEGs screening

Unpaired Student's t-test (13) in limma package was used to identify the DEGs in the DAMTC-treated A549 cell group, compared with the control group. False discovery rate (FDR) (14) was performed for multiple testing correction using the Benjamini and Hochberg method (15). The threshold for the DEGs was set as FDR <0.01 and |log2 FC (fold change) |≥2.

 Functional and pathway enrichment analysis of DEGs

Gene Ontology (GO) (16) categories, including biological process (BP), molecular function (MF) and cellular component (CC), of the selected DEGs were enriched from GO databases using Database for Annotation Visualization and Integrated Discovery (DAVID) (17). Additionally, the pathways of selected DEGs were enriched using DAVID from Kyoto Encyclopedia of Genes and Genomes (KEGG) (18) analysis. P<0.05, as determined by the hypergeometric test (19), was selected as the threshold.

 Functional annotation of DEGs

Identification of tumor-associated genes (TAGs) and understanding their functions can be critical for investigating the roles of genes involved in tumorigenesis. The tumor suppressor gene (TSGene) database (http://bioinfo.mc.vanderbilt.edu/TSGene/) is a comprehensive literature-based database that provides detailed annotations for each TSG. The TAG database (http://www.binfo.ncku.edu.tw/TAG/) is designed to utilize information from well-characterized oncogenes and tumor suppressor genes to accelerate cancer research. According to the data information regarding transcription factors (TFs) from the TRANSFAC database (20), functional enrichment of the DEGs for transcription regulation was assessed. Additionally, the selected DEGs were mapped into the TSGene and TAG database to extract the genes that had transcriptional functions or functioned as TAGs.

 PPI network construction

The PPI network is represented by an undirected graph with nodes indicating the genes and edges indicating the mapped interactions of the proteins encoded by the genes (21). The PPI network of the selected genes was constructed by using data from the Retrieval of Interacting Genes (STRING) database, which is a comprehensive database containing functional associations between proteins that are experimentally derived, as well as associations predicted by comparative genomics and text mining (22). The interaction pairs with the PPI combined score >0.4 were selected in this network, which corresponded to a medium-confidence network (23).

 Pathway crosstalk analysis

If more significant protein interactions are detected between two pathways, these two pathways are probable to influence or interact with each other (cross-talk). Understanding the crosstalk between pathways is momentous for understanding the function of cells and more complex systems (24). In the present study, the KEGG pathway data and protein interaction data based on the PPI network were combined to investigate pathway crosstalk in the DAMTC-treated A549 cells. The detail is included in the studies by Li et al (24) and Liu et al (25).

 Results <sec> <title>Identification of DEGs

For the dataset GSE29698, a total of 889 DEGs were identified in DAMTC-treated A549 cell groups, compared with DAMTC-untreated controls, including 500 upregulated genes and 389 downregulated genes. The heat-map of the DEGs is depicted in Fig. 1. The results demonstrated that the DEGs expression pattern could significantly distinguish the DAMTC-treated A549 cell samples from controls.

 GO and pathway enrichment analysis of the DEGs

Functional and pathway enrichment analysis indicated that upregulated and downregulated DEGs in DAMTC-treated A549 cell groups were significantly enriched in different GO terms and KEGG pathways (Tables I and II). GO analysis demonstrated enrichment in upregulated DEGs involved in BP, including transcription, DNA-dependent and regulation of cellular metabolic process. Significant CC ontology terms of upregulated DEGs were associated with intracellular part. The majority of significant MF ontology terms of upregulated DEGs were associated with binding function and enzyme activity, including DNA binding and phosphatase activity. Furthermore, significant BP ontology terms of downregulated DEGs were associated with tissue morphogenesis, and morphogenesis of an epithelium. CC ontology terms of downregulated DEGs were also associated with intracellular part. Significant MF ontology terms of downregulated DEGs were associated with organic cyclic compound binding, and RNA polymerase II transcription corepressor activity (Table I).

Additionally, upregulated DEGs were enriched in different pathways, including the p53 signaling pathway, and fructose and mannose metabolism pathway. Downregulated DEGs were also identified to be involved in different pathways, including ubiquitin mediated proteolysis, and metabolic pathways (Table II).

 Function annotation of DEGs

The expression change of TFs, TSGs and other TAGs in DAMTC-treated A549 cell groups were observed (Table III). The upregulated functional genes included 34 TFs and 26 TSGs. Subsequently, MAX was identified to be a TF, while tumor protein p53 inducible nuclear protein 1 and bone morphogenetic protein 2 were identified to be TSGs. Additionally, histone deacetylase 3 (HDAC3) was identified as both a TF and a TSG. Additionally, the downregulated functional genes included 27 TFs and 18 TSGs. Among them, sex determining region Y-box 4 and signal transducer and activator of transcription 1 (STAT1) were identified to be TFs.

 PPI network and the pathway crosstalk analysis

Based on the STRING database, the PPI network was constructed (Fig. 2). The results demonstrated that a total of 9 genes had a node degree >16, including interleukin 6 (IL6; degree, 37), MDM2 oncogene, E3 ubiquitin protein ligase (MDM2; degree, 27), STAT1 (degree, 23), ataxia telangiectasia mutated (degree, 20), HDAC3 (degree, 19), cell division cycle 42 (CDC42; degree, 18), cytochrome c, somatic (degree, 17), bone morphogenetic protein 4 (degree, 17) and MAX (degree, 17).

The result of pathway crosstalk analysis is depicted in Fig. 3. The majority of the significant pathways were determined to have crosstalk with small cell lung cancer and non-small cell lung cancer. The apoptosis, tight junction and endocytosis pathways were determined to be cross-talking with small cell lung cancer and non-small cell lung cancer. Additionally, the bacterial invasion of epithelial cells, adherens junction, shigellosis and mechanistic target of rapamycin kinase signaling pathways had cross-talk with non-small cell lung cancer. In addition, small cell lung cancer and non-small cell lung cancer were also determined to have cross-talk.

 Discussion

The worldwide incidence rate of lung cancer is high (26). It is important to develop novel, more effective treatment modalities for dealing with this disease. DAMTC is a potential inducer of apoptosis and an inhibitor of cell growth (1). Thus, identifying the molecular mechanism underlying the effects of DAMTC is imperative. In the present study, microarray analysis demonstrated that 500 upregulated DEGs and 389 downregulated DEGs were identified in DAMTC-treated A549 cells, compared with DAMTC-untreated controls. Among the DEGs, IL6, which was downregulated, was a hub protein with the highest degree in the PPI network. The upregulated DEGs MDM2, CDC42 and MAX, which were also hub genes, had different functions and were involved in different pathways, including the p53 signaling and endocytosis pathways. Additionally, the apoptosis, tight junction, and endocytosis pathways were determined to cross-talk with small cell lung cancer and non-small cell lung cancer.

IL6 encodes a multifunctional cytokine, which can function in inflammation and the maturation of B cells (27). Hodge et al (28) demonstrated that the release of various survival factors, including IL6, serve to block apoptosis in cancer cells during the inflammatory process, keeping them alive in toxic environments. Additionally, studies have demonstrated that drugs designed to restore apoptosis exhibit potential to effectively treat numerous cancer types, including inhibitors of apoptosis, protein inhibitors and MDM2 antagonists (29,30). Furthermore, in keeping with the previous study, our study identified that IL6 was downregulated in DAMTC-treated A549 groups and was involved in the pathways in cancer (Table I). Thus, it was indicated that DAMTC may induce cancer cell apoptosis through targeting IL6 in lung adenocarcinoma.

Additionally, the other upregulated DEG MAX was enriched in the pathways in cancer and identified to be a TAG in the present study. MAX is a member of the basic helix-loop-helix leucine zipper family of TFs and could interact with other family members, including v-myc avian myelocytomatosis viral oncogene homolog (MYC), MAX interactor 1, dimerization protein and mothers against decapentaplegic (MAD), to form homodimers or heterodimers (31). The study by Grandori et al (32) demonstrated that the MYC/MAX/MAD network was comprised of a group of TFs controlling cell cycle differentiation, progression and death. Additionally, Zhu et al (33) reported that levels of MAD1 rapidly decrease upon mitogen stimulation, and this degradation could be regulated by the MAPK pathway. Furthermore, Goel et al (34) reported that DAMTC could induce apoptosis through the mitochondrial pathway by modulating the MAPK pathway. Thus, it was indicated that DAMTC may induce cell apoptosis by upregulating the expression of MAX and modulating the MAPK pathway in lung adenocarcinoma.

Furthermore, pathway crosstalk analysis in the present study demonstrated that apoptosis, tight junction and endocytosis had cross-talks with non-small and small lung cancer. Additionally, the present study also revealed that MDM2 and CDC42, which were another two hub genes in the PPI network, were identified to be involved in different pathways, including the p53 signaling and endocytosis pathways. MDM2 encodes a nuclear-localized E3 ubiquitin ligase that mediates ubiquitination of p53 (35). Oren (36) demonstrated that proteins encoded by one or more p53 target genes could serve an essential role in causing p53-mediated apoptosis. This was consistent with the observations by Goel et al (12), demonstrating that DAMTC could induce apoptosis by modulating the p53 pathway in A549 cells. For the other DEG, CDC42 is a small GTPase of the Rho-subfamily, which can regulate signaling pathways that control numerous cellular functions, including cell endocytosis, migration and cell cycle progression (37). Georgiou et al (38) reported that CDC42 could act together with par-6 family cell polarity regulator and protein kinase C in the regulation of Arp2/3-mediated endocytosis to control local adherens junction stability, modulating actin filament dynamics (38). Additionally, Mosesson et al (39) revealed that aberrant endocytosis of transmembrane proteins could contribute to malignant transformation. Furthermore, Goel et al (34) demonstrated that DAMTC could inhibit cell motility in A549 cells. Collectively, it was confirmed that DAMTC could induce cancer cell apoptosis by modulating the p53 pathway. In addition, it was indicated that DAMTC may inhibit cell motility through targeting CDC42 and affecting the endocytosis pathway in lung adenocarcinoma.

In conclusion, the present study determined a network of genes, including IL6, MDM2, CDC42 and MAX, targeted by DAMTC that participated in different pathways were involved in the mechanism of DAMTC-treated lung adenocarcinoma. DAMTC may induce cell apoptosis by targeting IL6 and MAX, which was involved in the MAPK pathway in lung adenocarcinoma. Additionally, DAMTC may inhibit cell motility through targeting CDC42 and affecting the endocytosis pathway in lung adenocarcinoma. These activities may contribute to the anti-carcinogenic action of DAMTC. Due to the relatively small number of samples in the present study, further studies using a larger sample size to validate and determine the role of the potential molecular mechanism identified are required. Ongoing studies may emphasize the potential of DAMTC as an anticancer therapeutic.

 Acknowledgements

Not applicable.

 Funding

Not applicable.

 Availability of data and materials

The datasets used and/or analyzed during the present study are available on reasonable request from the corresponding author.

 Authors' contributions

BW conceived and designed the study. YC and YK acquired the data. XL and HF analyzed and interpreted the data. SZ and YK performed statistical analysis. BW drafted the manuscript. YC and TZ revised the manuscript for important intellectual content. All authors read and approved the final manuscript.

 Ethics approval and consent to participate

Not applicable.

 Patient consent for publication

Not applicable.

 Competing interests

The authors declare that they have no competing interests.

 References GoelAChhabraRAhmadSPrasadAParmarVGhoshBSainiNDAMTC regulates cytoskeletal reorganization and cell motility in human lung adenocarcinoma cell line: An integrated proteomics and transcriptomics approachCell Death Dis3e402201210.1038/cddis.2012.14123059821 SiegelRDeSantisCVirgoKSteinKMariottoASmithTCooperDGanslerTLerroCFedewaSCancer treatment and survivorship statistics, 2012CA Cancer J Clin62220241201210.3322/caac.2013822700443 KaisermannMCTrajmanAMadiKEvolving features of lung adenocarcinoma in Rio de Janeiro, BrazilOncol Rep8189192200111115596 ImielinskiMBergerAHHammermanPSHernandezBPughTJHodisEChoJSuhJCapellettiMSivachenkoAMapping the hallmarks of lung adenocarcinoma with massively parallel sequencingCell15011071120201210.1016/j.cell.2012.08.02922980975 BorgesFRoleiraFMilhazesNSantanaLUriarteESimple coumarins and analogues in medicinal chemistry: Occurrence, synthesis and biological activityCurr Med Chem12887916200510.2174/092986705350731515853704 MusaMACooperwoodJSKhanMOA review of coumarin derivatives in pharmacotherapy of breast cancerCurr Med Chem1526642679200810.2174/09298670878624287718991629 KostovaIRalevaSGenovaPArgirovaRStructure-activity relationships of synthetic coumarins as HIV-1 inhibitorsBioinorg Chem Appl68274200617497014 KontogiorgisCAHadjipavlou-LitinaDJSynthesis and biological evaluation of novel coumarin derivatives with a 7-azomethine linkageBioorg Med Chem Lett14611614200410.1016/j.bmcl.2004.01.07414741253 AppendinoGMercalliEFuzzatiNArnoldiLStavriMGibbonsSBalleroMMaxiaAAntimycobacterial coumarins from the sardinian giant fennel (Ferula communis)J Nat Prod6721082110200410.1021/np049706n15620264 LacyAO'KennedyRStudies on coumarins and coumarin-related compounds to determine their therapeutic role in the treatment of cancerCurr Pharm Des1037973811200410.2174/138161204338269315579072 GoelAPrasadAParmarVGhoshBSainiNAntiproliferative effects of 7, 8 diacetoxy 4 methyl coumarin and 7, 8 diacetoxy 4 methyl thiocoumarin in human lung adenocarcinoma cell lineClin Cancer Res13C38C382007 GoelAPrasadAKParmarVSGhoshBSainiNApoptogenic effect of 7, 8-diacetoxy-4-methylcoumarin and 7, 8-diacetoxy-4-methylthiocoumarin in human lung adenocarcinoma cell line: Role of NF-kappaB, Akt, ROS and MAP kinase pathwayChem Biol Interact179363374200910.1016/j.cbi.2008.10.06019061872 SmythGKLimma: Linear models for microarray data. In: Bioinformatics and Computational Biology Solutions Using R and BioconductorGentlemanRCareyVJHuberWIrizarryRADudoitSSpringerNew York, NY3974202005 Reiner-BenaimAFDR Control by the BH Procedure for two-sided correlated tests with implications to gene expression data analysisBiom J49107126200710.1002/bimj.20051031317342953 BenjaminiYHochbergYControlling the false discovery rate: A practical and powerful approach to multiple testingJ R Stat Soc Series B (Methodological)57289300199510.1111/j.2517-6161.1995.tb02031.x AshburnerMBallCABlakeJABotsteinDButlerHCherryJMDavisAPDolinskiKDwightSSEppigJTGene ontology: Tool for the unification of biology. The Gene Ontology ConsortiumNat Genet252529200010.1038/7555610802651 Huang daWShermanBTLempickiRASystematic and integrative analysis of large gene lists using DAVID bioinformatics resourcesNat Protoc44457200910.1038/nprot.2008.21119131956 KanehisaMGotoSKEGG: Kyoto encyclopedia of genes and genomesNucleic Acids Res282730200010.1093/nar/28.1.2710592173 DuPFengGFlatowJSongJHolkoMKibbeWALinSMFrom disease ontology to disease-ontology lite: Statistical methods to adapt a general-purpose ontology for the test of gene-ontology associationsBioinformatics25i63i68200910.1093/bioinformatics/btp19319478018 MatysVFrickeEGeffersRGösslingEHaubrockMHehlRHornischerKKarasDKelAEKel-MargoulisOVTRANSFAC: Transcriptional regulation, from patterns to profilesNucleic Acids Res31374378200310.1093/nar/gkg10812520026 KöhlerSBauerSHornDRobinsonPNWalking the interactome for prioritization of candidate disease genesAm J Hum Genet82949958200810.1016/j.ajhg.2008.02.01318371930 FranceschiniASzklarczykDFrankildSKuhnMSimonovicMRothALinJMinguezPBorkPvon MeringCJensenLJSTRING v9. 1: Protein-protein interaction networks, with increased coverage and integrationNucleic Acids Res41(Database Issue)D808D815201310.1093/nar/gks109423203871 Von MeringCHuynenMJaeggiDSchmidtSBorkPSnelBSTRING: A database of predicted functional associations between proteinsNucleic Acids Res31258261200310.1093/nar/gkg03412519996 LiYAgarwalPRajagopalanDA global pathway crosstalk networkBioinformatics2414421447200810.1093/bioinformatics/btn20018434343 LiuZPWangYZhangXSChenLIdentifying dysfunctional crosstalk of pathways in various regions of Alzheimer's disease brainsBMC Syst Biol4Suppl 2S11201010.1186/1752-0509-4-S2-S1120840725 CooperSSpiroSGSmall cell lung cancer: Treatment reviewRespirology11241248200610.1111/j.1440-1843.2006.00850.x16635081 AtreyaRMudterJFinottoSMüllbergJJostockTWirtzSSchützMBartschBHoltmannMBeckerCBlockade of interleukin 6 trans signaling suppresses T-cell resistance against apoptosis in chronic intestinal inflammation: Evidence in crohn disease and experimental colitis in vivoNat Med6583588200010.1038/7506810802717 HodgeDRHurtEMFarrarWLThe role of IL-6 and STAT3 in inflammation and cancerEur J Cancer4125022512200510.1016/j.ejca.2005.08.01616199153 FesikSWPromoting apoptosis as a strategy for cancer drug discoveryNat Rev Cancer5876885200510.1038/nrc177616239906 NicholsonDWFrom bench to clinic with apoptosis-based therapeutic agentsNature407810816200010.1038/3503774711048733 LüscherBFunction and regulation of the transcription factors of the Myc/Max/Mad networkGene277114200110.1016/S0378-1119(01)00697-711602341 GrandoriCCowleySMJamesLPEisenmanRNThe Myc/Max/Mad network and the transcriptional control of cell behaviorAnnu Rev Cell Dev Biol16653699200010.1146/annurev.cellbio.16.1.65311031250 ZhuJBlenisJYuanJActivation of PI3K/Akt and MAPK pathways regulates Myc-mediated transcription by phosphorylating and promoting the degradation of Mad1Proc Natl Acad Sci USA10565846589200810.1073/pnas.080278510518451027 GoelAChhabraRAhmadSPrasadAKParmarVSGhoshBSainiNDAMTC regulates cytoskeletal reorganization and cell motility in human lung adenocarcinoma cell line: An integrated proteomics and transcriptomics approachCell Death Dis3e402201210.1038/cddis.2012.14123059821 StevensonLFSparksAAllende-VegaNXirodimasDPLaneDPSavilleMKThe deubiquitinating enzyme USP2a regulates the p53 pathway by targeting Mdm2EMBO J26976986200710.1038/sj.emboj.760156717290220 OrenMDecision making by p53: Life, death and cancerCell Death Differ10431442200310.1038/sj.cdd.440118312719720 PulaGPooleAWCritical roles for the actin cytoskeleton and cdc42 in regulating platelet integrin alpha2beta1Platelets19199210200810.1080/0953710070177730318432521 GeorgiouMMarinariEBurdenJBaumBCdc42, Par6 and aPKC regulate Arp2/3-mediated endocytosis to control local adherens junction stabilityCurr Biol1816311638200810.1016/j.cub.2008.09.02918976918 MosessonYMillsGBYardenYDerailed endocytosis: An emerging feature of cancerNat Rev Cancer8835850200810.1038/nrc252118948996

Heat-map of differentially-expressed genes. For each gene (row), red indicates a higher expression and green a reduced expression relative to the mean level of expression of the gene across the samples (columns), while black indicates genes not differentially expressed. DAMTC, 7, 8-diacetoxy-4-methylcoumarin.

Protein-protein interaction network of differentially-expressed genes. The red nodes indicate upregulated genes and green nodes represent downregulated genes.

Pathway crosstalk analysis of 7, 8-diacetoxy-4-methylcoumarin-treated lung adenocarcinoma. The pink nodes represent the Kyoto Encyclopedia of Genes and Genomes pathway. The width of solid line indicates the strength of the crosstalk.

The enriched GO terms of differentially-expressed genes in 7, 8-diacetoxy-4-methylcoumarin-treated lung cancer groups.

Expression changes Category GO-ID Name Count P-value
Upregulated BP GO:0006351 Transcription, DNA-dependent 153 3.30×10−11
GO:0031323 Regulation of cellular metabolic process 194 7.80×10−11
GO:0060255 Regulation of macromolecule metabolic process 187 8.24×10−11
GO:0051252 Regulation of RNA metabolic process 143 1.56×10−10
GO:0019222 Regulation of metabolic process 207 2.08×10−10
CC GO:0005622 Intracellular 387 2.45×10−10
GO:0044424 Intracellular part 382 1.08×10−9
GO:0005634 Nucleus 219 2.68×10−9
GO:0043227 Membrane-bounded organelle 320 5.19×10−9
GO:0043231 Intracellular membrane-bounded organelle 319 5.71×10−9
MF GO:0003677 DNA binding 98 3.25×10−5
GO:0016791 Phosphatase activity 18 4.00×10−4
GO:0000988 Protein binding transcription factor activity 28 8.00×10−4
GO:0005515 Protein binding 244 11.00×10−3
GO:0005488 Binding 362 13.00×10−3
Downregulated BP GO:0048729 Tissue morphogenesis 28 2.61×10−6
GO:0002009 Morphogenesis of an epithelium 24 3.72×10−6
GO:0044237 Cellular metabolic process 221 7.50×10−5
GO:0044238 Primary metabolic process 224 8.50×10−5
GO:0048736 Appendage development 12 8.77×10−5
CC GO:0044424 Intracellular part 291 3.52×10−8
GO:0043231 Intracellular membrane-bounded organelle 243 1.83×10−7
GO:0005622 Intracellular 291 2.11×10−7
GO:0043227 Membrane-bounded organelle 243 2.69×10−7
GO:0043229 Intracellular organelle 257 2.01×10−6
MF GO:0097159 Organic cyclic compound binding 154 2.31×10−6
GO:1901363 Heterocyclic compound binding 152 2.77×10−6
GO:0001191 RNA polymerase II transcription factor binding transcription factor activity involved in negative regulation of transcription 6 2.35×10−5
GO:0001106 RNA polymerase II transcription corepressor activity 5 3.99×10−5
GO:0003714 Transcription corepressor activity 14 8.22×10−5

GO, gene ontology; CC, cellular component; BP, biological process; MF, molecular function.

The enriched pathways of differentially-expressed genes in 7, 8-diacetoxy-4-methylcoumarin-treated lung cancer groups.

Expression changes KEGG-ID Name n P-value Genes
Upregulated 04115 p53 signaling pathway 9 0.0001 APAF1, CCNG2, CYCS, GADD45A, MDM2, PPM1D, RPRM, SESN1 and SESN3
00051 Fructose and mannose metabolism 5 0.0032 ALDOC, C12orf5, HK2, MTMR1 and PFKFB4
05219 Bladder cancer 5 0.0062 FGFR3, MAP2K1, MDM2, PGF and RASSF1
04722 Neurotrophin signaling pathway 9 0.0095 CDC42, GAB1, MAP2K1, MAP3K3, MAPK7, NFKBIE, NTF3, PDK1 and ZNF274
05200 Pathways in cancer 17 0.0103 BMP2, CCNA1, CDC42, CYCS, EGLN1, FGF18, FGFR3, FZD1, FZD4, FZD7, MAP2K1, MAX, MDM2, PGF, PIAS3, RASSF1 and RASSF5
04130 SNARE interactions in vesicular transport 4 0.0180 BNIP1, STX11, STX7 and VAMP1
04144 Endocytosis 11 0.0265 CDC42, CXCR4, DNM2, FGFR3, GIT2, HLA-E, MDM2, RAB11FIP1, SH3GL2, SH3GL3 and VPS37B
05211 Renal cell carcinoma 5 0.0472 CDC42, EGLN1, GAB1, MAP2K1 and PGF
Downregulated 05217 Basal cell carcinoma 6 0.0008 BMP4, FZD2, GLI2, STK36, TCF7L2 and WNT7B
00130 Ubiquinone and other terpenoid-quinone biosynthesis 2 0.0079 COQ5 and COQ7
01100 Metabolic pathways 33 0.0130 ACSS1, AK2, ATP6V1E2, B3GALT6, BAAT, BCAT1, COQ5, COQ7, CYP2R1, DPAGT1, GGPS1, GPT2, HKDC1, HLCS, OXSM, PAFAH2, PCK2, PFAS, PIGM, PIGV, PIGW, PIK3C2B, PLD1, PMM2, POLG2, POLR1B, POLR3H, PSPH, SEPHS2, SLC33A1, ST3GAL5, ST6GAL1 and SYNJ2
00563 Glycosylphosphatidyli-nositol-anchor biosynthesis 3 0.0132 PIGM, PIGV and PIGW
04120 Ubiquitin mediated proteolysis 7 0.0185 ANAPC13, BIRC6, FBXO4, ITCH, KLHL9, UBE2J1 and UBE2Q1
04340 Hedgehog signaling pathway 4 0.0255 BMP4, GLI2, STK36 and WNT7B
05200 Pathways in cancer 12 0.0300 BMP4, DAPK1, FZD2, GLI2, IL6, PLD1, RARB, STAT1, STK36, TCF7L2, TRAF5 and WNT7B

n, number of genes enriched in the corresponding pathway; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Functional annotation of differentially-expressed genes in 7, 8-diacetoxy-4-methylcoumarin-treated lung cancer groups.

Expression changes TF TSG Other TAG
Upregulated ATF2, BCL6, CDK7, ERCC6, EZH2, FOXD1, GATA2, GATA4, GMEB1, GTF2B, HAND1, HDAC3, HEY2, HOXD10, HOXD11, ING1, IRF7, IRF8, IRX5, ISL1, MAFB, MAFF, MAX, MEF2A, NFIL3, PAX9, PLAGL1, RFX2, RORA, SOX8, TMF1, VDR, ZNF10 and ZNF133 APAF1, ARID3B, BCL10, BIK, BMP2, BRMS1, CAMTA1, CDKN1C, CDKN2D, DDX3X, EGLN1, FBXW7, HDAC3, ING1, IRF8, LATS2, NDRG1, PHF6, PLAGL1, PLEKHO1, PPP1R3C, RASSF1, RASSF5, SLC9A3R1, TMEFF2 and TP53INP1 CHRM3, FZD7, LGALS8, MAFB, MAP1A, MAX, PIAS3 and RGS2
Downregulated AKNA, CEBPG, FOXL2, FOXQ1, GATA3, GLI2, GTF2E1, HOXB5, HOXB8, HOXC8, MEIS1, MEIS2, MSC, NFIA, NFIB,NPAS2, NR2E3, NR2F2, NR5A2, PBX1, PRDM1, SMAD6, SOX4, STAT1, TBX18, TCF7L2 and XBP1 ABLIM3, ARID1A, ASXL1, CDH4, COL4A3, DAB2, DAPK1,DFNA5, DKK1, KCNRG, KRIT1, MN1, MTUS1, NRCAM, PLK2, PRDM1, RARB and TCF7L2 ATM, PMS1, RHOB, SSPN and TFAP2A

TF, transcription factor; TSG, tumor suppression genes; TAG, tumor-associated gene.