DNA methylation profiling identifies potentially significant epigenetically‑regulated genes in glioblastoma multiforme

Kan,Shifeng; Chai,Song; Chen,Wenhua; Yu,Bo

doi:10.3892/ol.2019.10512

DNA methylation profiling identifies potentially significant epigenetically‑regulated genes in glioblastoma multiforme

Authors:
- Shifeng Kan
- Song Chai
- Wenhua Chen
- Bo Yu
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Affiliations: Department of Rehabilitation, Shanghai General Hospital, Shanghai Jiaotong University, Shanghai 200080, P.R. China
Published online on: June 21, 2019 https://doi.org/10.3892/ol.2019.10512
Pages: 1679-1688
Copyright: © Kan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Glioblastoma multiforme (GBM) is one of the most lethal and damaging types of human cancer. The current study was conducted to identify differentially methylated genes (DMGs) between GBM and normal controls, and to improve our understanding of GBM at the epigenetic level. The DNA methylation profile of GBM was downloaded from the Gene Expression Omnibus (GEO) database using the accession number GSE50923. The MethyAnalysis package was applied to identify DMGs between GBM and controls, which were then analyzed by functional enrichment analysis. Protein‑protein interaction (PPI) networks were constructed using the hypermethylated and hypomethylated genes. Finally, transcription factors (TFs) that can regulate the hypermethylated and hypomethylated genes were predicted, followed by construction of transcriptional regulatory networks. Furthermore, another relevant dataset, GSE22867, was downloaded from the GEO database for data validation. A total of 476 hypermethylated and 850 hypomethylated genes were identified, which were mainly associated with the functions of ‘G‑protein‑coupled receptors ligand binding’, ‘cytokine activity’, ‘cytokine‑cytokine receptor interaction’, and ‘D‑glutamine and D‑glutamate metabolism’. The hypermethylated gene neuropeptide Y (NPY) and the hypomethylated gene tumor necrosis factor (TNF) demonstrated high degrees in the PPI network. Forkhead box protein A1 (FOXA1), potassium voltage‑gated channel subfamily C member 3 (KCNC3) and caspase‑8 (CASP8) exhibited high degrees in the transcriptional regulatory networks. In addition, the methylation profiles of NPY, TNF, FOXA1, KCNC3 and CASP8 were confirmed by another dataset. In summary, the present study systematically analyzed the DNA methylation profile of GBM using bioinformatics approaches and identified several abnormally methylated genes, providing insight into the molecular mechanism underlying GBM.

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1	Wen PY and Kesari S: Malignant gliomas in adults. N Engl J Med. 359:492–507. 2008. View Article : Google Scholar : PubMed/NCBI
2	Rock K, McArdle O, Forde P, Dunne M, Fitzpatrick D, O'Neill B and Faul C: A clinical review of treatment outcomes in glioblastoma multiforme - the validation in a non-trial population of the results of a randomised phase III: Has a more radical approach improved survival? Br J Radiol. 85:e729–e733. 2012. View Article : Google Scholar : PubMed/NCBI
3	Holland EC: Gliomagenesis: Genetic alterations and mouse models. Nat Rev Genet. 2:120–129. 2001. View Article : Google Scholar : PubMed/NCBI
4	Nagarajan RP and Costello JF: Epigenetic mechanisms in glioblastoma multiforme. Semin Cancer Biol. 19:188–197. 2009. View Article : Google Scholar : PubMed/NCBI
5	Herman JG and Baylin SB: Gene silencing in cancer in association with promoter hypermethylation. N Engl J Med. 349:2042–2054. 2003. View Article : Google Scholar : PubMed/NCBI
6	Karpf AR and Matsui SI: Genetic disruption of cytosine DNA methyltransferase enzymes induces chromosomal instability in human cancer cells. Cancer Res. 65:8635–8639. 2005. View Article : Google Scholar : PubMed/NCBI
7	Nakamura M, Yonekawa Y, Kleihues P and Ohgaki H: Promoter hypermethylation of the RB1 gene in glioblastomas. Lab Invest. 81:77–82. 2001. View Article : Google Scholar : PubMed/NCBI
8	Amatya VJ, Naumann U, Weller M and Ohgaki H: TP53 promoter methylation in human gliomas. Acta Neuropathol. 110:178–184. 2005. View Article : Google Scholar : PubMed/NCBI
9	Baeza N, Weller M, Yonekawa Y, Kleihues P and Ohgaki H: PTEN methylation and expression in glioblastomas. Acta Neuropathol. 106:479–485. 2003. View Article : Google Scholar : PubMed/NCBI
10	Kreth S, Thon N, Eigenbrod S, Lutz J, Ledderose C, Egensperger R, Tonn JC, Kretzschmar HA, Hinske LC and Kreth FW: O-methylguanine-DNA methyltransferase (MGMT) mRNA expression predicts outcome in malignant glioma independent of MGMT promoter methylation. PLoS One. 6:e171562011. View Article : Google Scholar : PubMed/NCBI
11	Lalezari S, Chou AP, Tran A, Solis OE, Khanlou N, Chen W, Li S, Carrillo JA, Chowdhury R, Selfridge J, et al: Combined analysis of O6-methylguanine-DNA methyltransferase protein expression and promoter methylation provides optimized prognostication of glioblastoma outcome. Neuro Oncol. 15:370–381. 2013. View Article : Google Scholar : PubMed/NCBI
12	Garcia-Manero G: Demethylating agents in myeloid malignancies. Curr Opin Oncol. 20:705–710. 2008. View Article : Google Scholar : PubMed/NCBI
13	Li D, Xie Z, Pape ML and Dye T: An evaluation of statistical methods for DNA methylation microarray data analysis. BMC Bioinformatics. 16:2172015. View Article : Google Scholar : PubMed/NCBI
14	Xu C, Zhang JG, Lin D, Zhang L, Shen H and Deng HW: A systemic analysis of transcriptomic and epigenomic data to reveal regulation patterns for complex disease. G3 (Bethesda). 7:2271–2279. 2017. View Article : Google Scholar : PubMed/NCBI
15	Smith AA, Huang YT, Eliot M, Houseman EA, Marsit CJ, Wiencke JK and Kelsey KT: A novel approach to the discovery of survival biomarkers in glioblastoma using a joint analysis of DNA methylation and gene expression. Epigenetics. 9:873–883. 2014. View Article : Google Scholar : PubMed/NCBI
16	Gadaleta F, Bessonov K and Van Steen K: Integration of gene expression and methylation to unravel biological networks in glioblastoma patients. Genet Epidemiol. 41:136–144. 2017. View Article : Google Scholar : PubMed/NCBI
17	Lai RK, Chen Y, Guan X, Nousome D, Sharma C, Canoll P, Bruce J, Sloan AE, Cortes E, Vonsattel JP, et al: Genome-wide methylation analyses in glioblastoma multiforme. PLoS One. 9:e893762014. View Article : Google Scholar : PubMed/NCBI
18	Du P, Kibbe WA and Lin SM: Lumi: A pipeline for processing Illumina microarray. Bioinformatics. 24:1547–1548. 2008. View Article : Google Scholar : PubMed/NCBI
19	Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, Hou L and Lin SM: Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics. 11:5872010. View Article : Google Scholar : PubMed/NCBI
20	Du P and Bourgon R: methyAnalysis: An R package for DNA methylation data analysis and visualization. 2013.
21	Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eilbeck K, Lewis S, Marshall B, Mungall C, et al: The gene ontology (GO) database and informatics resource. Nucleic Acids Res. 32:D258–D261. 2004. View Article : Google Scholar : PubMed/NCBI
22	Kanehisa M and Goto S: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28:27–30. 2000. View Article : Google Scholar : PubMed/NCBI
23	Joshi-Tope G, Gillespie M, Vastrik I, D'Eustachio P, Schmidt E, de Bono B, Jassal B, Gopinath GR, Wu GR, Matthews L, et al: Reactome: A knowledgebase of biological pathways. Nucleic Acids Res. 33:D428–D432. 2005. View Article : Google Scholar : PubMed/NCBI
24	Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
25	Zhao M, Sun J and Zhao Z: TSGene: A web resource for tumor suppressor genes. Nucleic Acids Res. 41:D970–D976. 2013. View Article : Google Scholar : PubMed/NCBI
26	Chen JS, Hung WS, Chan HH, Tsai SJ and Sun HS: In silico identification of oncogenic potential of fyn-related kinase in hepatocellular carcinoma. Bioinformatics. 29:420–427. 2013. View Article : Google Scholar : PubMed/NCBI
27	ENCODE Project Consortium: The ENCODE (ENCyclopedia of DNA elements) project. Science. 306:636–640. 2004. View Article : Google Scholar : PubMed/NCBI
28	Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, Batzoglou S, Bernstein BE, Bickel P, Brown JB, Cayting P, et al: ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22:1813–1831. 2012. View Article : Google Scholar : PubMed/NCBI
29	Etcheverry A, Aubry M, de Tayrac M, Vauleon E, Boniface R, Guenot F, Saikali S, Hamlat A, Riffaud L, Menei P, et al: DNA methylation in glioblastoma: Impact on gene expression and clinical outcome. BMC Genomics. 11:7012010. View Article : Google Scholar : PubMed/NCBI
30	Gentleman R, Carey V, Huber W and Hahne F: Genefilter: Methods for filtering genes from microarray experiments. R Package version 1.66.0. 2011.
31	Eiró N and Vizoso FJ: Inflammation and cancer. World J Gastrointest Surg. 4:62–72. 2012. View Article : Google Scholar : PubMed/NCBI
32	Coussens LM and Werb Z: Inflammation and cancer. Nature. 420:860–867. 2002. View Article : Google Scholar : PubMed/NCBI
33	Spiegelberg BD and Hamm HE: Roles of G-protein-coupled receptor signaling in cancer biology and gene transcription. Curr Opin Genet Dev. 17:40–44. 2007. View Article : Google Scholar : PubMed/NCBI
34	Anglim PP, Alonzo TA and Laird-Offringa IA: DNA methylation-based biomarkers for early detection of non-small cell lung cancer: An update. Mol Cancer. 7:812008. View Article : Google Scholar : PubMed/NCBI
35	Hill VK, Ricketts C, Bieche I, Vacher S, Gentle D, Lewis C, Maher ER and Latif F: Genome-wide DNA methylation profiling of CpG islands in breast cancer identifies novel genes associated with tumorigenicity. Cancer Res. 71:2988–2999. 2011. View Article : Google Scholar : PubMed/NCBI
36	Shin SH, Kim BH, Jang JJ, Suh KS and Kang GH: Identification of novel methylation markers in hepatocellular carcinoma using a methylation array. J Korean Med Sci. 25:1152–1159. 2010. View Article : Google Scholar : PubMed/NCBI
37	Knerr I, Schuster S, Nomikos P, Buchfelder M, Dötsch J, Schoof E, Fahlbusch R and Rascher W: Gene expression of adrenomedullin, leptin, their receptors and neuropeptide Y in hormone-secreting and non-functioning pituitary adenomas, meningiomas and malignant intracranial tumours in humans. Neuropathol Appl Neurobiol. 27:215–222. 2001. View Article : Google Scholar : PubMed/NCBI
38	Kore RA and Abraham EC: Inflammatory cytokines, interleukin-1 beta and tumor necrosis factor-alpha, upregulated in glioblastoma multiforme, raise the levels of CRYAB in exosomes secreted by U373 glioma cells. Biochem Biophys Res Commun. 453:326–331. 2014. View Article : Google Scholar : PubMed/NCBI
39	Wang L, Qin H, Li L, Feng F, Ji P, Zhang J, Li G, Zhao Z and Gao G: Forkhead-box A1 transcription factor is a novel adverse prognosis marker in human glioma. J Clin Neurosci. 20:654–658. 2013. View Article : Google Scholar : PubMed/NCBI
40	Hurlock EC, McMahon A and Joho RH: Purkinje-cell-restricted restoration of Kv3.3 function restores complex spikes and rescues motor coordination in Kcnc3 mutants. J Neurosci. 28:4640–4648. 2008. View Article : Google Scholar : PubMed/NCBI
41	Martinez R, Stuhmer W, Martin S, Schell J, Reichmann A, Rohde V and Pardo L: Analysis of the expression of Kv10.1 potassium channel in patients with brain metastases and glioblastoma multiforme: Impact on survival. BMC Cancer. 15:8392015. View Article : Google Scholar : PubMed/NCBI
42	Arvind S, Arivazhagan A, Santosh V and Chandramouli BA: Differential expression of a novel voltage gated potassium channel-Kv 1.5 in astrocytomas and its impact on prognosis in glioblastoma. Br J Neurosurg. 26:16–20. 2012. View Article : Google Scholar : PubMed/NCBI
43	Qi L, Bellail AC, Rossi MR, Zhang Z, Pang H, Hunter S, Cohen C, Moreno CS, Olson JJ, Li S and Hao C: Heterogeneity of primary glioblastoma cells in the expression of caspase-8 and the response to TRAIL-induced apoptosis. Apoptosis. 16:1150–1164. 2011. View Article : Google Scholar : PubMed/NCBI