OL Oncology Letters 1792-1074 1792-1082 D.A. Spandidos 10.3892/ol.2017.7083 OL-0-0-7083 Articles Identification of potentially critical differentially methylated genes in nasopharyngeal carcinoma: A comprehensive analysis of methylation profiling and gene expression profiling HuiLian ZhangJingru DingXiaoxu GuoXing JangXuejun Department of Otolaryngology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China Correspondence to: Dr Lian Hui, Department of Otolaryngology, The First Hospital of China Medical University, 155 NanjingBei Street, Shenyang, Liaoning 110001, P.R. China, E-mail: lhui@cmu.edu.cn 12 2017 27 09 2017 14 6 7171 7178 29062016 17082017 Copyright: © Hui et al. 2017 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 identify potentially critical differentially methylated genes associated with the progression of nasopharyngeal carcinoma (NPC). Methylation profiling data of GSE62336 deposited in the Gene Expression Omnibus database were used to identify differentially methylated regions (DMRs) and differentially methylated CpG islands (DMIs). Concurrently, differentially expressed genes (DEGs) were identified using a meta-analysis of three gene expression datasets (GSE53819, GSE13597 and GSE12452). Subsequently, methylated DEGs were identified by comparing DMRs and DEGs. Furthermore, functional associations of these methylated DEGs were analyzed via constructing a functional network using GeneMANIA prediction server. In total, 1,676 hypermethylated genes, 28 hypomethylated genes, 17 DMIs and 2,983 DEGs (1,655 upregulated and 1,328 downregulated) were identified. Among these DEGs, 135 downregulated genes were hypermethylated; of these, dual specificity phosphatase 6 (DUSP6) and tenascin XB (TNXB) contained DMIs. In the functional network, 154 genes and 1,651 association pairs were included. DUSP6 was predicted to exhibit genetic interactions with other hypermethylated DEGs such as malic enzyme 3 and ST3 β-galactoside α-2,3-sialyltransferase 5; TNXB was predicted to be co-expressed with a set of hypermethylated DEGs, including EPH receptor B6, aldehyde dehydrogenase 1 family, member L1 and glutathione peroxidase 3. The hypermethylated DEGs may be involved in the progression of NPC, and they may become novel therapeutic targets for NPC.

 nasopharyngeal carcinoma methylation differentially expressed gene functional network Introduction

Nasopharyngeal carcinoma (NPC), a malignant tumor arising from the epithelium of the nasopharynx, is the most prevalent in southern China (1). In Hong Kong, the incidence of NPC is as high as 0.02–0.03% in males and 0.01–0.02% in females (2). Although the classic treatment of high-dose radiotherapy plus adjunctive chemotherapy is able to achieve a 5-year survival rate of 80%, recurrence and metastasis may occur, which are the primary causes of mortality (3). Therefore, it is necessary to identify molecular biomarkers for NPC prognosis and targeted therapy.

Aberrant DNA methylation usually leads to the occurrence of tumors. CpG island promoter hypermethylation and global DNA hypomethylation are the characteristics of the cancer epigenome (4,5). In NPC, aberrant methylation has been considered as the most frequent event for gene silencing: For example, a previous study identified that abnormal methylation on chromosome 6p occurs in 76.9% of patients with early-stage NPC, based on the comparative methylome analysis (6). Furthermore, tumor-suppressor genes such as protocadherin 8 (PCDH8), FEZ family zinc finger 2 (FEZF2) and argininosuccinate synthetase (ASS1) have been previously demonstrated to be frequently methylated in NPC, which promotes NPC cell migration, and are associated with poorer clinical outcomes (7–9).

With the exception of DNA methylation, the differential expression of genes is also frequently detected in NPC: A previous study demonstrated that the overexpression of branched-chain-amino-acid aminotransferase cytosolic (BCAT1) protein in NPC at different pathological stages and BCAT1 deficiency reduced tumor cell proliferation and decreased cell migration and invasion abilities (10). Recently, upregulation of Kelch domain containing 4 (KLHDC4) was demonstrated to result in poor overall and metastasis-free survival rates, and the deletion of KLHDC4 significantly induced the spontaneous apoptosis of NPC cells (11). However, the molecular mechanisms of NPC remain incompletely understood. There have been no studies that have comprehensively analyzed differentially expressed methylated genes using array data from multiple platforms.

In the present study, methylation profiling data in the dataset GSE62336 (6), sourced from the Gene Expression Omnibus (GEO) database, were used to identify differentially methylated regions (DMRs) and differentially methylated CpG islands (DMIs). Concurrently, compared with differentially expressed genes (DEGs) that were identified using a meta-analysis of three gene expression datasets (GSE53819, GSE13597 and GSE12452) (12–14), differentially methylated genes were identified. Additionally, a functional network consisting of the differentially methylated genes was constructed to reveal the potential functional associations of those genes. These results may provide novel information for the study of molecular mechanisms underlying NPC and provide potential therapeutic targets for NPC.

 Materials and methods <sec> <title>Data acquisition

Methylation profiling data from the dataset GSE62336 (6) were downloaded from GEO (http://www.ncbi.nlm.nih.gov/geo/) (15). A total of 25 primary NPC tumors and non-tumor counterparts were included in this dataset, and the data were produced on the platform of Illumina HumanMethylation450 BeadChip (GPL13534, HumanMethylation450_15017482) (Illumina, Inc., San Diego, CA, USA).

Three gene expression profiling datasets (GSE53819, GSE13597 and GSE12452) were obtained from GEO. In the GSE53819 dataset (13), 18 primary NPC tumors and 18 non-cancerous nasopharyngeal tissues were included; the median ages were 46 years (range, 19–77 years) for patients with NPC, and 45 years (range, 18–78 years) for the non-cancerous cohort; almost one third of patients were females; all samples were collected prior to any anticancer treatment. Data in the GSE53819 dataset were produced on the platform of Agilent-014850 Whole Human Genome Microarray 4×44 K G4112F (Probe Name version; GPL6480; Agilent Technologies, Inc., Santa Clara CA, USA). The GSE13597 dataset (14) contained data of 25 histologically confirmed undifferentiated NPC tissues and 3 non-malignant nasopharyngeal controls, which were produced on the platform of [HG-U133A] Affymetrix Human Genome U133A Array (GPL96) (Affymetrix, Inc., Santa Clara, CA, USA). Additionally, the GSE12452 dataset (12) contained 31 NPC tumor samples and 10 normal healthy nasopharyngeal tissues, data of which were produced on the platform of [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array (GPL570) (Affymetrix, Inc.).

 Data preprocessing

The downloaded raw data were preprocessed. For the methylation data in GSE62336, documents of normalized average β-value were downloaded. The β-mixture quantile normalization method (BMIQ) (16) was utilized to preprocess β-values.

Due to different platforms being used for the three gene expression profiling datasets, two different methods were utilized for data preprocessing. For the gene expression data in the GSE13597 and GSE12452 datasets, raw gene expression data were preprocessed using the method of robust microarray analysis in Affy package (version 1.46.1; https://www.bioconductor.org/packages/3.1/bioc/html/affy.html) in R (17). The preprocessing steps included background correction, quantile normalization and calculation of expression. By contrast, Limma (version 3.24.15; https://www.bioconductor.org/packages/3.1/bioc/html/limma.html) package (18) in R was applied to preprocess raw data in the GSE53819 dataset. The preprocessing steps included background correction, normalization between arrays and concentration of microarray data. Following this, an annotation file of the platform corresponding to each dataset was used for the transformation of probe identities into gene symbols. If one probe corresponded to multiple genes, the expression value of this probe would be removed. However, if multiple probes corresponded to a certain gene, the mean expression value was defined as the final expression value of the gene.

 Prediction of DMRs and DMIs

DMRs between NPC and normal samples were predicted using COHCAP package (version 1.6.0; http://www.bioconductor.org/pacages/3.1/bioc/html/COHCAP.html) (19) in R. Briefly, based on the β-value file of CpG site probes, Δβ, P-value and adjusted P-value of NPC and normal samples were calculated by COHCAP. Only regions with |Δβ|>0.1 and adjusted P<0.05 were identified as DMRs.

Furthermore, the COHCAP package was also utilized to predict DMIs. CpG island statistics were calculated by averaging β-values among samples per site and comparing the average β-values across groups. If the number of DMRs in the CpG island was >4, this CpG island was identified as a DMI.

 Identification of DEGs using meta-analysis

MetaDE package (version 1.1.6; http://www.bioconductor.org/packages/2.11/bioc/html/metahdep.html) (20) in R was applied to integrate DEGs in the three gene expression profiling datasets. Briefly, a heterogeneity test for each gene under different platforms was firstly performed to evaluate whether each gene was homogeneous and unbiased. If the parameter tau2=0 and Qpval>0.05 (tau2 is used to estimate amount of heterogeneity; Qpval represents P-value of Qval test statistics; and Q is a statistical magnitude in statistics), the gene was homogeneous and unbiased. Then, the differential expression of the genes was analyzed. Only genes with P<0.05 were considered significant. Finally, the relative fold-change (FC) in expression of each gene between the NPC tissues samples and normal control samples was calculated. Collectively, genes with P<0.05 were identified as DEGs.

 Gene Ontology (GO) functional and pathway enrichment analyses

The gene functional analysis tool Database for Annotation, Visualization and Integrated Discovery (http://david.abcc.ncifcrf.gov/) (21) was used to perform GO [including biological process (BP), cell component (CC) and molecular function (MF)] and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs. Only the GO and pathway terms with gene count ≥2 and P<0.05 were considered significant.

 Selection of DEGs with DMRs

Based on the gene symbols corresponding to the DMRs and identified DEGs, the overlapped genes between genes with hypermethylated DMRs and downregulated DEGs, and the genes with hypomethylated DMRs and upregulated DEGs were selected.

 Functional association analysis of the DEGs with DMRs

The GeneMANIA prediction server (22,23) (http://apps.cytoscape.org/download/stats/genemania/), a plugin in Cytoscape software (version 3.2.1; National Institute of General Medical Sciences, Seattle, WA, USA), was utilized to analyze the correlations among the identified DEGs that had DMRs, based on a large set of functional association data, including protein and genetic interactions, co-expression, co-localization pathways, and protein domain similarity.

 Results <sec> <title>Statistics of DMRs, DMIs and DEGs

In total, 2,262 probes of DMRs were obtained, including 2,234 hypermethylated CpG site probes corresponding to 1,676 gene symbols and 28 hypomethylated CpG site probes corresponding to 28 gene symbols. Furthermore, 2,983 DEGs were identified, including 1,655 upregulated and 1,328 downregulated genes. Additionally, 17 DMIs were identified, and all of them were hypermethylated in the NPC samples compared with their methylation status in the controls (Fig. 1). Among them, dual specificity phosphatase 6 (DUSP6) and tenascin XB (TNXB) were also identified as DEGs.

 Significant pathways enriched by DEGs

In order to investigate the potential pathways associated with the identified DEGs, KEGG pathway enrichment analysis was conducted. The upregulated DEGs were primarily associated with pathways in cancer, oxidative phosphorylation and the cell cycle (Fig. 2A). The downregulated DEGs were primarily associated with the regulation of actin cytoskeleton, chemokine signaling pathway and focal adhesion (Fig. 2B).

 Overlapped genes between genes with DMRs and DEGs

In order to reveal whether the genes with DMRs were DEGs, genes with DMRs were compared with DEGs. It was identified that 135 genes with hypermethylated DMRs were downregulated in the NPC samples compared with their expression levels in the normal controls, including prostaglandin D2 synthase, elongation factor for RNA polymerase II 3, ATPase sarcoplasmic/endoplasmic reticulum Ca2+ transporting 3 and claudin 3 (Table I).

However, the 28 genes with hypomethylated DMRs were not differentially expressed between the two group samples.

 Functional associations of the DEGs with DMRs

In order to reveal the potential functional associations among the above 135 hypermethylated DEGs, a functional network was constructed (Fig. 3). In the network, 154 genes and 1,651 association pairs were included. The association pairs included 1,155 co-expression associations, 24 physical interactions, 351 genetic interactions, 83 co-localization associations and 38 associations between shared protein domains. For example, DUSP6 was predicted to exhibit genetic interactions with other hypermethylated DEGs such as malic enzyme 3 (ME3) and ST3 β-galactoside α-2,3-sialytansferase 5 (ST3GAL5); TNXB was predicted to be co-expressed with genes such as EPH receptor B6 (EPHB6), aldehyde dehydrogenase 1 family, member L1 (ALDH1L1) and glutathione peroxidase 3 (GPX3).

According to the enrichment analysis, the 135 hypermethylated DEGs were significantly associated with the GO functions of protein amino acid phosphorylation and phosphate metabolic process, and the tight junction pathway (Table II).

 Discussion

In the present study, 2,234 hypermethylated CpG site probes corresponding to 1,676 gene symbols, 28 hypomethylated CpG site probes corresponding to 28 gene symbols and 17 DMIs were identified based on analysis of the methylation profiling dataset. Furthermore, 2,983 DEGs (1,655 upregulated and 1,328 downregulated) were identified based on the three gene expression profiling datasets. Among these DEGs, 135 downregulated genes were hypermethylated, including DUSP6 and TNXB, which were also among the 17 DMIs identified.

DUSP6 encodes dual specificity phosphatase 6, also termed mitogen-activated protein kinase phosphatase 3, which belongs to the dual specificity protein phosphatase subfamily (24). Phosphatases in this family inactivate their target kinases, such as members of the mitogen-activated protein kinase superfamily, which are involved in cellular proliferation and differentiation (25). In the present study, DUSP6 was identified to be hypermethylated and downregulated in NPC samples compared with its methylation status in the normal controls, which was consistent with other studies (14,26). DUSP6 has been identified as a tumor suppressor, and it is able to inhibit epithelial-mesenchymal transition (EMT) and cell invasion by negatively modulating the activity of extracellular-signal-regulated kinase in NPC (26). In the present study, DUSP6 was predicted to exhibit genetic interactions with other hypermethylated DEGs such as ME3 and ST3GAL5. ME3 is a mitochondrial nicotinamide adenine dinucleotide phosphate(+) -dependent enzyme (27), and it serves a unique role in tumor mitochondria (28). The protein encoded by ST3GAL5 is a sialyltransferase, a type II membrane protein that catalyzes the formation of α-2,3-sialyltransferase (GM3), a protein participating in cell differentiation and cell adhesion (29). There is no other evidence to indicate the associations of ME3 and ST3GAL5 with NPC at present. Therefore, ME3 and ST3GAL5 may be potential novel biomarker molecules in the progression of NPC.

TNXB was also identified to be hypermethylated and downregulated in NPC samples compared with its methylation status in the normal controls, which was consistent with previous studies (6,12). In Epstein-Barr virus-positive gastric cancer and pancreatic cancer, TNXB was also hypermethylated (30,31). TNXB encodes a tenascin, which exhibits an anti-adhesive effect (32). It is able to promote EMT by activating latent transforming growth factor-β (33). In malignancy, TNXB is usually suppressed, and it has been identified as a marker for malignant mesothelioma (34). Furthermore, in the present study, TNXB was predicted to be co-expressed with a set of other hypermethylated DEGs, including EPHB6, ALDH1L1 and GPX3. EPH receptor B6 (EPHB6) encodes a transmembrane protein, which may affect cell adhesion and migration. In tumor progression, EPHB6 is usually downregulated due to promoter DNA hypermethylation (35,36). It has been suggested that EPHB6 alters invasiveness, and is associated with the prognosis and/or diagnosis of breast carcinoma (35,37). ALDH1L1 and GPX3 have been previously identified to be silenced by methylation, which is associated with tumorigenesis (38,39). Although there is no experimental evidence to confirm the involvement of EPHB6, ALDH1L1 and GPX3 in NPC, the present study hypothesizes that these molecules may also serve a significant role in the progression of NPC, along with TNXB.

Additionally, there are several limitations in the present study. The expression levels and methylation status of the aforementioned genes are required to be validated using experiments. Functional associations of these genes also need to be confirmed. These validations will be performed and presented separately. Despite the absence of experiments, several genes such as ME3, ST3GAL5, EPHB6, ALDH1L1 and GPX3 were primarily identified to be potentially associated with NPC, and they may become novel therapeutic targets for NPC, once validated.

In conclusion, based on the comprehensive analysis of methylation profiling and gene expression profiling, 135 downregulated genes were identified to be hypermethylated in NPC compared with its methylation status in the controls in the present study. Among them, DUSP6 and TNXB contained DMIs. Hypermethylated DEGs that exhibited genetic interactions with DUSP6, including ME3 and ST3GAL5, and genes that co-expressed with TNXB, including EPHB6, ALDH1L1 and GPX3, may be potential novel molecules involved in the progression of NPC, and they may become novel therapeutic targets for NPC.

 Acknowledgements

The present study was supported by a grant from the Natural Science Foundation of Liaoning Province of China (grant no. 201202287).

 Abbreviations NPC

nasopharyngeal carcinoma

 DMRs

differentially methylated regions

 DMIs

differentially methylated CpG islands

 DEGs

differentially expressed genes

 PCDH8

protocadherin 8

 FEZF2

FEZ family zinc finger 2

 ASS1

argininosuccinate synthetase

 KLHDC4

Kelch domain containing 4

 GEO

Gene Expression Omnibus

 BMIQ

β-mixture quantile normalization method

 FC

fold-change

 BP

biological process

 CC

cell component

 MF

molecular function

 KEGG

Kyoto Encyclopedia of Genes and Genomes

 MAP

mitogen-activated protein

 EMT

epithelial-mesenchymal transition

 ME3

malic enzyme 3

 GM3

α-2,3-sialyltransferase

 EPHB6

EPH Receptor B6

 ALDH1L1

aldehyde dehydrogenase 1 family, member L1

 GPX3

glutathione peroxidase 3

 References HildesheimAWangCPGenetic predisposition factors and nasopharyngeal carcinoma risk: A review of epidemiological association studies, 2000–2011: Rosetta Stone for NPC: Genetics, viral infection, and other environmental factorsSemin Cancer Biol22107116201210.1016/j.semcancer.2012.01.00722300735 WeiWIShamJSNasopharyngeal carcinomaLancet36520412054200510.1016/S0140-6736(05)66698-615950718 WangTJRiazNChengSKLuJJLeeNYIntensity-modulated radiation therapy for nasopharyngeal carcinoma: A reviewJ Radiat Oncol1129146201210.1007/s13566-012-0020-4 MeissnerAMikkelsenTSGuHWernigMHannaJSivachenkoAZhangXBernsteinBENusbaumCJaffeDBGenome-scale DNA methylation maps of pluripotent and differentiated cellsNature454766770200818600261 IrizarryRALadd-AcostaCWenBWuZMontanoCOnyangoPCuiHGaboKRongioneMWebsterMThe human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shoresNat Genet41178186200910.1038/ng.29819151715 DaiWCheungAKKoJMChengYZhengHNganRKNgWTLeeAWYauCCLeeVHLungMLComparative methylome analysis in solid tumors reveals aberrant methylation at chromosome 6p in nasopharyngeal carcinomaCancer Med410791090201510.1002/cam4.45125924914 HeDZengQRenGXiangTQianYHuQZhuJHongSHuGProtocadherin8 is a functional tumor suppressor frequently inactivated by promoter methylation in nasopharyngeal carcinomaEur J Cancer Prev21569575201210.1097/CEJ.0b013e328350b09722273848 ShuXSLiLJiMChengYYingJFanYZhongLLiuXTsaoSWChanATTaoQFEZF2, a novel 3p14 tumor suppressor gene, represses oncogene EZH2 and MDM2 expression and is frequently methylated in nasopharyngeal carcinomaCarcinogenesis3419841993201310.1093/carcin/bgt16523677067 LanJTaiHCLeeSWChenTJHuangHYLiCFDeficiency in expression and epigenetic DNA Methylation of ASS1 gene in nasopharyngeal carcinoma: Negative prognostic impact and therapeutic relevanceTumour Biol35161169201410.1007/s13277-013-1020-823897555 ZhouWFengXRenCJiangXLiuWHuangWLiuZLiZZengLWangLOver-expression of BCAT1, a c-Myc target gene, induces cell proliferation, migration and invasion in nasopharyngeal carcinomaMol Cancer1253201310.1186/1476-4598-12-5323758864 LianYFYuanJCuiQFengQSXuMBeiJXZengYXFengLUpregulation of KLHDC4 predicts a poor prognosis in human nasopharyngeal carcinomaPLoS One11e0152820201610.1371/journal.pone.015282027030985 SenguptaSden BoonJAChenIHNewtonMADahlDBChenMChengYJWestraWHChenCJHildesheimAGenome-wide expression profiling reveals EBV-associated inhibition of MHC class I expression in nasopharyngeal carcinomaCancer Res6679998006200610.1158/0008-5472.CAN-05-439916912175 BaoYNCaoXLuoDHSunRPengLXWangLYanYPZhengLSXiePCaoYUrokinase-type plasminogen activator receptor signaling is critical in nasopharyngeal carcinoma cell growth and metastasisCell Cycle1319581969201410.4161/cc.2892124763226 BoseSYapLFFungMStarzcynskiJSalehAMorganSDawsonCChukwumaMBMainaEBuettnerMThe ATM tumour suppressor gene is down-regulated in EBV-associated nasopharyngeal carcinomaJ Pathol217345352200910.1002/path.248719142888 NorfoRZiniRPennucciVBianchiESalatiSGuglielmelliPBoganiCFanelliTMannarelliCRostiVmiRNA-mRNA integrative analysis in primary myelofibrosis CD34+ cells: Role of miR-155/JARID2 axis in abnormal megakaryopoiesisBlood124e21e32201410.1182/blood-2013-12-54419725097177 TeschendorffAEMarabitaFLechnerMBartlettTTegnerJGomez-CabreroDBeckSA beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation dataBioinformatics29189196201310.1093/bioinformatics/bts68023175756 GautierLCopeLBolstadBMIrizarryRAAffy-analysis of Affymetrix GeneChip data at the probe levelBioinformatics20307315200410.1093/bioinformatics/btg40514960456 SmythGKLimma: Linear models for microarray dataBioinformatics and computational biology solutions using R and BioconductorSpringerNew York, NY397420200510.1007/0-387-29362-0_23 WardenCDLeeHTompkinsJDLiXWangCRiggsADYuHJoveRYuanYCCOHCAP: An integrative genomic pipeline for single-nucleotide resolution DNA methylation analysisNucleic Acids Res41e117201310.1093/nar/gkt24223598999 QiCHongLChengZYinQIdentification of metastasis-associated genes in colorectal cancer using metaDE and survival analysisOncol Lett11568574201626870249 Huang daWShermanBTLempickiRASystematic and integrative analysis of large gene lists using DAVID bioinformatics resourcesNat Protoc44457200910.1038/nprot.2008.21119131956 Warde-FarleyDDonaldsonSLComesOZuberiKBadrawiRChaoPFranzMGrouiosCKaziFLopesCTThe GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene functionNucleic Acids Res38W214W220201010.1093/nar/gkq53720576703 ZuberiKFranzMRodriguezHMontojoJLopesCTBaderGDMorrisQGeneMANIA prediction server 2013 updateNucleic Acids Res41W115W122201310.1093/nar/gkt53323794635 SmithAPriceCCullenMMudaMKingAOzanneBArkinstallSAshworthAChromosomal localization of three human dual specificity phosphatase genes (DUSP4, DUSP6 and DUSP7)Genomics42524527199710.1006/geno.1997.47569205128 JurekAAmagasakiKGembarskaAHeldinCHLennartssonJNegative and positive regulation of MAPK phosphatase 3 controls platelet-derived growth factor-induced Erk activationJ Biol Chem28446264634200910.1074/jbc.M80849020019106095 WongVCChenHKoJMChanKWChanYPLawSChuaDKwongDLLungHLSrivastavaGTumor suppressor dual-specificity phosphatase 6 (DUSP6) impairs cell invasion and epithelial-mesenchymal transition (EMT)-associated phenotypeInt J Cancer1308395201210.1002/ijc.2597021387288 LoeberGMaurer-FogyISchwendenweinRPurification, cDNA cloning and heterologous expression of the human mitochondrial NADP(+)-dependent malic enzymeBiochem J304687692199410.1042/bj30406877818469 WiseDRThompsonCBGlutamine addiction: A new therapeutic target in cancerTrends Biochem Sci35427433201010.1016/j.tibs.2010.05.00320570523 IshiiAOhtaMWatanabeYMatsudaKIshiyamaKSakoeKNakamuraMInokuchiJSanaiYSaitoMExpression cloning and functional characterization of human cDNA for ganglioside GM3 synthaseJ Biol Chem2733165231655199810.1074/jbc.273.48.316529822625 ZhaoJLiangQCheungKFKangWLungRWTongJHToKFSungJJYuJGenome-wide identification of Epstein-Barr virus-driven promoter methylation profiles of human genes in gastric cancer cellsCancer119304312201310.1002/cncr.2772422833454 ShimizuHHoriiASunamuraMMotoiFEgawaSUnnoMFukushigeSIdentification of epigenetically silenced genes in human pancreatic cancer by a novel method ‘microarray coupled with methyl-CpG targeted transcriptional activation’ (MeTA-array)Biochem Biophys Res Commun411162167201110.1016/j.bbrc.2011.06.12121723258 ChiovaroFChiquet-EhrismannRChiquetMTranscriptional regulation of tenascin genesCell Adh Migr93447201510.1080/19336918.2015.100833325793574 AlcarazLBExpositoJYChuvinNPommierRMCluzelCMartelSSentisSBartholinLLethiasCValcourtUTenascin-X promotes epithelial-to-mesenchymal transition by activating latent TGF-βJ Cell Biol205409428201410.1083/jcb.20130803124821840 YuanYNymoenDAStavnesHTRosnesAKBjørangOWuCNeslandJMDavidsonBTenascin-X is a novel diagnostic marker of malignant mesotheliomaAm J Surg Pathol3316731682200910.1097/PAS.0b013e3181b6bde319738457 FoxBPKandpalRPDNA-based assay for EPHB6 expression in breast carcinoma cells as a potential diagnostic test for detecting tumor cells in circulationCancer Genomics Proteomics7916201020181626 YuJBulkEJiPHascherATangMMetzgerRMarraAServeHBerdelWEWiewrothRThe EPHB6 receptor tyrosine kinase is a metastasis suppressor that is frequently silenced by promoter DNA hypermethylation in non-small cell lung cancerClin Cancer Res1622752283201010.1158/1078-0432.CCR-09-200020371680 FoxBPKandpalRPEphB6 receptor significantly alters invasiveness and other phenotypic characteristics of human breast carcinoma cellsOncogene2817061713200910.1038/onc.2009.1819234485 HeYWangYLiPZhuSWangJZhangSIdentification of GPX3 epigenetically silenced by CpG methylation in human esophageal squamous cell carcinomaDig Dis Sci56681688201110.1007/s10620-010-1369-020725785 OleinikNVKrupenkoNIKrupenkoSAEpigenetic silencing of ALDH1L1, a metabolic regulator of cellular proliferation, in cancersGenes Cancer2130139201110.1177/194760191140584121779486

Heatmap of the 17 DMIs. Each row represents a gene that contains DMI, and each column represents a tissue sample. Green indicates hypomethylated, while red indicates hypermethylated. DMIs, differentially methylated CpG islands.

Kyoto Encyclopedia of Genes and Genomes pathway terms that were significantly enriched by differentially expressed genes. Pathway terms enriched by (A) upregulated and (B) downregulated genes. hsa, Homo sapiens.

Functional network displaying functional associations of the 135 differentially hypermethylated genes. Green nodes with red outer ring represent the downregulated genes containing hypermethylated regions, while grey nodes represent the non-differentially expressed genes.

Information regarding the downregulated genes containing hypermethylated regions.

Site ID Chr Loc Gene Island Mean log2FCa Mean β-value in NPC samples Mean β-value in normal samples Δβ (NPC vs. normal) FDR (NPC vs. normal)
cg20450318 11 65415260 SIPA1 chr11:65413778-65415203 −0.72 6.57×10−1 4.65×10−1 1.92×10−1 1.30×10−4
cg17953300 11 65418265 chr11:65419853-65420527 5.91×10−1 4.57×10−1 1.34×10−1 2.33×10−3
cg16915828 11 73371940 PLEKHB1 chr11:73371800-73372632 −0.59 6.19×10−1 4.31×10−1 1.88×10−1 2.93×10−5
cg07223180 13 20989142 CRYL1 chr13:20989007-20989836 −0.62 5.98×10−1 4.63×10−1 1.35×10−1 5.69×10−5
cg21177426 15 37386586 MEIS2 chr15:37387386-37387614 −0.82 6.28×10−1 4.68×10−1 1.60×10−1 1.70×10−5
cg24361265 15 44068668 ELL3 chr15:44068586-44069792 −1.24 6.36×10−1 4.32×10−1 2.04×10−1 2.51×10−5
cg06786050 16 84401247 ATP2C2 chr16:84401957-84402497 −0.82 5.58×10−1 4.36×10−1 1.22×10−1 1.21×10−4
cg26824780 16 89004908 CBFA2T3 chr16:89006334-89008600 −0.87 5.95×10−1 4.73×10−1 1.23×10−1 6.23×10−6
cg09013975 17 3847872 ATP2A3 chr17:3847999-3848570 −1.06 6.68×10−1 4.90×10−1 1.78×10−1 4.51×10−5
cg05247914 19 35629701 FXYD1 chr19:35632356-35632572 −0.62 6.76×10−1 4.83×10−1 1.93×10−1 3.71×10−5
cg03078169 19 35629791 chr19:35632356-35632572 6.31×10−1 4.79×10−1 1.52×10−1 4.40×10−5
cg27461196 19 35630106 chr19:35632356-35632572 5.59×10−1 4.51×10−1 1.08×10−1 1.81×10−4
cg16334795 21 42538894 BACE2 chr21:42539367-42540872 −0.86 6.06×10−1 4.51×10−1 1.55×10−1 1.58×10−5
cg08481491   3 125900108 ALDH1L1 chr3:125898662-125899568 −0.80 5.85×10−1 4.46×10−1 1.38×10−1 1.04×10−4
cg04161526   6 31696519 DDAH2 chr6:31695894-31698245 −0.63 5.65×10−1 4.33×10−1 1.32×10−1 2.25×10−5
cg21286967   6 31696710 chr6:31695894-31698245 6.01×10−1 4.71×10−1 1.30×10−1 3.27×10−5
cg25526039   6 107813291 SOBP chr6:107810066-107812733 −0.81 5.53×10−1 4.36×10−1 1.17×10−1 4.45×10−4
cg24419391   7 73183516 CLDN3 chr7:73183379-73185115 −1.02 5.89×10−1 4.67×10−1 1.21×10−1 1.64×10−3
cg08580268   7 150038502 RARRES2 chr7:150037459-150039031 −0.94 5.66×10−1 3.95×10−1 1.71×10−1 9.63×10−5
cg27494647   7 150038898 chr7:150037459-150039031 6.14×10−1 4.15×10−1 1.99×10−1 7.82×10−5
cg11714502   9 130640212 AK1 chr9:130639738-130640143 −0.61 5.71×10−1 4.47×10−1 1.24×10−1 5.00×10−4
cg02156769   9 139872246 PTGDS chr9:139872237-139873143 −1.81 5.58×10−1 4.56×10−1 1.01×10−1 1.95×10−5
cg07390373   X 43741933 MAOB chrX:43741299-43741827 −0.78 5.96×10−1 4.88×10−1 1.08×10−1 3.94×10−5
cg05605944   X 43741945 chrX:43741299-43741827 5.84×10−1 4.62×10−1 1.22×10−1 1.72×10−5

This table lists the genes with |log2 FC|>0.58. Site ID, probe identity of CpG site; Chr, chromosome where probe is located; Loc, location of probe; FC, fold-change; NPC, nasopharyngeal carcinoma; FDR, false discovery rate.

Results of GO and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the 135 downregulated genes containing hypermethylated regions.

Category Term P-value Gene count Genes
BP GO:0006468~protein amino acid phosphorylation 0.0021 14 STK10, DRD4, PTPRA, MKNK2, PRKCH, ADRBK1, FES, TRIB2, CSNK2A2, EPHB6, TNK2, CD6, MAP3K12, AKT2
GO:0006796~phosphate metabolic process 0.0087 16 STK10, DRD4, EPM2A, PTPRA, MKNK2, PRKCH, ADRBK1, FES, TRIB2, CSNK2A2, EPHB6, TNK2, CD6, MAP3K12, AKT2, DUSP6
GO:0006793~phosphorus metabolic process 0.0087 16 STK10, DRD4, EPM2A, PTPRA, MKNK2, PRKCH, ADRBK1, FES, TRIB2, CSNK2A2, EPHB6, TNK2, CD6, MAP3K12, AKT2, DUSP6
GO:0016310~phosphorylation 0.0096 14 STK10, DRD4, PTPRA, MKNK2, PRKCH, ADRBK1, FES, TRIB2, CSNK2A2, EPHB6, TNK2, CD6, MAP3K12, AKT2
GO:0006357~regulation of transcription from RNA polymerase II promoter 0.0113 13 CDKN1C, GATA2, SORBS3, MEIS2, E2F4, CRTC1, TFAP2B, GFI1, TCEA2, LRRFIP1, ELL3, NtnxbR0B1, ZBTB38
CC GO:0044459~plasma membrane part 0.0118 27 FXYD1, OCLN, CLDN3, SLC6A20, DRD4, ADRBK1, PRR7, SORBS3, EPHB6, ANK1, ST3GAL5, ITGB7, GRIN2D, CD6, SLC43A1, KCNQ1, SLC1A1, HCN2, CR1, PTPRA, MAOB, TSPAN13, ACTN3, LSP1, AP2A2, MPL, SPTAN1
GO:0005887~integral to plasma membrane 0.0166 17 FXYD1, HCN2, CR1, CLDN3, SLC6A20, DRD4, PTPRA, TSPAN13, EPHB6, ST3GAL5, GRIN2D, ITGB7, MPL, CD6, KCNQ1, SLC1A1, SLC43A1
GO:0031226~intrinsic to plasma membrane 0.0201 17 FXYD1, HCN2, CR1, CLDN3, SLC6A20, DRD4, PTPRA, TSPAN13, EPHB6, ST3GAL5, GRIN2D, ITGB7, MPL, CD6, KCNQ1, SLC1A1, SLC43A1
GO:0005626~insoluble fraction 0.0247 13 DES, JPH2, BACE2, GRIN2D, EPHX1, TSPAN13, ADRBK1, LTC4S, NR0B1, SLC1A1, MAP3K12, SPTAN1, AKT2
GO:0000267~cell fraction 0.0336 15 JPH2, TSPAN13, EPHX1, ADRBK1, LTC4S, NR0B1, DES, BACE2, GRIN2D, GPX3, SLC1A1, MAP3K12, AKT2, SPTAN1, DUSP6
MF GO:0008134~transcription factor binding 0.0009 13 ZNF274, E2F4, CRTC1, NR0B1, GATA2, SORBS3, MEIS2, DIP2C, NCOA4, GPX3, TFAP2B, SOX17, DDAH2
GO:0048037~cofactor binding 0.0156   7 SDHA, CRYL1, ME3, ALDH1L1, ACADS, GPT, OAT
GO:0005200~structural constituent of cytoskeleton 0.0220   4 SORBS3, ANK1, DES, SPTAN1
GO:0004672~protein kinase activity 0.0247 11 CSNK2A2, EPHB6, STK10, MKNK2, PRKCH, ADRBK1, TNK2, FES, MAP3K12, TRIB2, AKT2
Pathway hsa04530: Tight junction 0.0036   7 CSNK2A2, OCLN, CLDN3, PRKCH, ACTN3, SPTAN1, AKT2

BP, biological process; CC, cell component; MF, molecular function; GO, Gene Ontology; hsa, Homo sapiens.