Identification of aberrantly methylated differentially expressed genes in glioblastoma multiforme and their association with patient survival

Zhang,Miao; Lv,Xintong; Jiang,Yuanjun; Li,Guang; Qiao,Qiao

doi:10.3892/etm.2019.7807

Identification of aberrantly methylated differentially expressed genes in glioblastoma multiforme and their association with patient survival

Authors:
- Miao Zhang
- Xintong Lv
- Yuanjun Jiang
- Guang Li
- Qiao Qiao
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Affiliations: Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China, Department of Urology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China
Published online on: July 24, 2019 https://doi.org/10.3892/etm.2019.7807
Pages: 2140-2152
Copyright: © Zhang 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 the most malignant primary tumour type of the central nervous system with limited therapeutic options and poor prognosis, and its pathogenic mechanisms have remained to be fully elucidated. Aberrant DNA methylation is involved in multiple biological processes and may contribute to the occurrence and development of GBM by affecting the expression of certain genes. However, the specific molecular mechanisms remain to be fully elucidated. The present study focused on uncovering differentially expressed genes with altered methylation status in GBM and aimed to discover novel biomarkers for the diagnosis and treatment of GBM. These genes were identified by combined analysis of multiple gene expression and methylation datasets from gene expression omnibus (GSE16011, GSE50161 and GSE 50923) to increase the reliability. In addition, The Cancer Genome Atlas (TCGA) dataset for GBM was used to test the stability of the results. Overall, 251 hypomethylated upregulated genes (Hypo‑UGs) and 199 hypermethylated downregulated genes (Hyper‑DGs) were identified in the present study. Functional enrichment analysis revealed that the Hypo‑UGs are involved in the regulation of immune‑ and infection‑associated signalling, while the Hyper‑DGs are involved in the regulation of synaptic transmission. The three hub genes for Hyper‑DGs (somatostatin, neuropeptide Y and adenylate cyclase 2) and five hub genes for Hypo‑UGs [interleukin‑8, matrix metalloproteinase (MMP)9, cyclin‑dependent kinase 1, 2'‑5'‑oligoadenylate synthetase 1, C‑X‑C motif chemokine ligand 10 and MMP2] were identified by protein‑protein interaction network analysis. Among the Hypo‑UGs and Hyper‑DGs, overexpression of C‑type lectin domain containing 5A, epithelial membrane protein 3, solute carrier family 43 member 3, STEAP3 metalloreductase, tumour necrosis factor α‑induced protein 6 and apolipoprotein B mRNA editing enzyme catalytic subunit 3G was significantly associated with poor prognosis in the TCGA and GSE16011 datasets (P<0.001). In conclusion, the present study uncovered numerous novel aberrantly methylated genes and pathways associated with GBM. Methylation‑based markers, including the hub genes and prognostic genes identified, may potentially serve as markers for the diagnosis of GBM and targets for its treatment.

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1	Anjum K, Shagufta BI, Abbas SQ, Patel S, Khan I, Shah SAA, Akhter N and Hassan SSU: Current status and future therapeutic perspectives of glioblastoma multiforme (GBM) therapy: A review. Biomed Pharmacother. 92:681–689. 2017. View Article : Google Scholar : PubMed/NCBI
2	Thakkar JP, Dolecek TA, Horbinski C, Ostrom QT, Lightner DD, Barnholtz-Sloan JS and Villano JL: Epidemiologic and molecular prognostic review of glioblastoma. Cancer Epidemiol Biomarkers Prev. 23:1985–1996. 2014. View Article : Google Scholar : PubMed/NCBI
3	Alexander BM and Cloughesy TF: Adult glioblastoma. J Clin Oncol. 35:2402–2409. 2017. View Article : Google Scholar : PubMed/NCBI
4	Batash R, Asna N, Schaffer P, Francis N and Schaffer M: Glioblastoma multiforme, diagnosis and treatment; Recent literature review. Curr Med Chem. 24:3002–3009. 2017. View Article : Google Scholar : PubMed/NCBI
5	Aldape K, Zadeh G, Mansouri S, Reifenberger G and von Deimling A: Glioblastoma: Pathology, molecular mechanisms and markers. Acta Neuropathol. 129:829–848. 2015. View Article : Google Scholar : PubMed/NCBI
6	Kanwal R, Gupta K and Gupta S: Cancer epigenetics: An introduction. Methods Mol Biol. 1238:3–25. 2015. View Article : Google Scholar : PubMed/NCBI
7	Romani M, Pistillo MP and Banelli B: Epigenetic targeting of glioblastoma. Front Oncol. 8:4482018. View Article : Google Scholar : PubMed/NCBI
8	Werner RJ, Kelly AD and Issa JJ: Epigenetics and precision oncology. Cancer J. 23:262–269. 2017. View Article : Google Scholar : PubMed/NCBI
9	Bender J: DNA methylation and epigenetics. Annu Rev Plant Biol. 55:41–68. 2004. View Article : Google Scholar : PubMed/NCBI
10	Berghoff AS, Hainfellner JA, Marosi C and Preusser M: Assessing MGMT methylation status and its current impact on treatment in glioblastoma. CNS Oncol. 4:47–52. 2015. View Article : Google Scholar : PubMed/NCBI
11	Skiriute D, Vaitkiene P, Saferis V, Asmoniene V, Skauminas K, Deltuva VP and Tamasauskas A: MGMT, GATA6, CD81, DR4, and CASP8 gene promoter methylation in glioblastoma. BMC Cancer. 12:2182012. View Article : Google Scholar : PubMed/NCBI
12	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
13	Sang L, Wang XM, Xu DY and Zhao WJ: Bioinformatics analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma. World J Gastroenterol. 24:2605–2616. 2018. View Article : Google Scholar : PubMed/NCBI
14	Nazarov PV, Muller A, Kaoma T, Nicot N, Maximo C, Birembaut P, Tran NL, Dittmar G and Vallar L: RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples. BMC Genomics. 18:4432017. View Article : Google Scholar : PubMed/NCBI
15	Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al: NCBI GEO: Archive for functional genomics data sets-update. Nucleic Acids Res. 41:D991–D995. 2013. View Article : Google Scholar : PubMed/NCBI
16	Edgar R, Domrachev M and Lash AE: Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30:207–210. 2002. View Article : Google Scholar : PubMed/NCBI
17	Gravendeel LA, Kouwenhoven MC, Gevaert O, de Rooi JJ, Stubbs AP, Duijm JE, Daemen A, Bleeker FE, Bralten LB, Kloosterhof NK, et al: Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology. Cancer Res. 69:9065–9072. 2009. View Article : Google Scholar : PubMed/NCBI
18	Griesinger AM, Birks DK, Donson AM, Amani V, Hoffman LM, Waziri A, Wang M, Handler MH and Foreman NK: Characterization of distinct immunophenotypes across pediatric brain tumor types. J Immunol. 191:4880–4888. 2013. View Article : Google Scholar : PubMed/NCBI
19	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
20	Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI
21	Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, et al: The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 45:D362–D368. 2017. View Article : Google Scholar : PubMed/NCBI
22	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
23	Goldman M, Craft B, Swatloski T, Cline M, Morozova O, Diekhans M, Haussler D and Zhu J: The UCSC cancer genomics browser: Update 2015. Nucleic Acids Res. 43:D812–D817. 2015. View Article : Google Scholar : PubMed/NCBI
24	Capper D, Jones DTW, Sill M, Hovestadt V, Schrimpf D, Sturm D, Koelsche C, Sahm F, Chavez L, Reuss DE, et al: DNA methylation-based classification of central nervous system tumours. Nature. 555:469–474. 2018. View Article : Google Scholar : PubMed/NCBI
25	Aoki K and Natsume A: Overview of DNA methylation in adult diffuse gliomas. Brain Tumor Pathol. 36:84–91. 2019. View Article : Google Scholar : PubMed/NCBI
26	Wen WS, Hu SL, Ai Z, Mou L, Lu JM and Li S: Methylated of genes behaving as potential biomarkers in evaluating malignant degree of glioblastoma. J Cell Physiol. 232:3622–3630. 2017. View Article : Google Scholar : PubMed/NCBI
27	Wang W, Zhao Z, Wu F, Wang H, Wang J, Lan Q and Zhao J: Bioinformatic analysis of gene expression and methylation regulation in glioblastoma. J Neurooncol. 136:495–503. 2018. View Article : Google Scholar : PubMed/NCBI
28	Chi J, Gu B, Zhang C, Peng G, Zhou F, Chen Y, Zhang G, Guo Y, Guo D, Qin J, et al: Human herpesvirus 6 latent infection in patients with glioma. J Infect Dis. 206:1394–1398. 2012. View Article : Google Scholar : PubMed/NCBI
29	Cobbs CS, Harkins L, Samanta M, Gillespie GY, Bharara S, King PH, Nabors LB, Cobbs CG and Britt WJ: Human cytomegalovirus infection and expression in human malignant glioma. Cancer Res. 62:3347–3350. 2002.PubMed/NCBI
30	Akhtar S, Vranic S, Cyprian FS and Al Moustafa AE: Epstein-barr virus in gliomas: Cause, association, or artifact? Front Oncol. 8:1232018. View Article : Google Scholar : PubMed/NCBI
31	Tani E, Takeuchi J and Ametani T: Virus-like particles in cultured human glioma. Acta Neuropathol. 16:266–270. 1970. View Article : Google Scholar : PubMed/NCBI
32	Lynch JR and Wang JY: G protein-coupled receptor signaling in stem cells and cancer. Int J Mol Sci. 17:E7072016. View Article : Google Scholar : PubMed/NCBI
33	O'Hayre M, Degese MS and Gutkind JS: Novel insights into G protein and G protein-coupled receptor signaling in cancer. Curr Opin Cell Biol. 27:126–135. 2014. View Article : Google Scholar : PubMed/NCBI
34	Miller G: Brain cancer. A viral link to glioblastoma? Science. 323:30–31. 2009.PubMed/NCBI
35	Nitta T, Allegretta M, Okumura K, Sato K and Steinman L: Neoplastic and reactive human astrocytes express interleukin-8 gene. Neurosurg Rev. 15:203–207. 1992. View Article : Google Scholar : PubMed/NCBI
36	Florio T and Schettini G: Somatostatin and its receptors. Role in the control of cell proliferation. Minerva Endocrinol. 26:91–102. 2001.(In Italian). PubMed/NCBI
37	Theodoropoulou M and Stalla GK: Somatostatin receptors: From signaling to clinical practice. Front Neuroendocrinol. 34:228–252. 2013. View Article : Google Scholar : PubMed/NCBI
38	Leiszter K, Sipos F, Galamb O, Krenács T, Veres G, Wichmann B, Fűri I, Kalmár A, Patai ÁV, Tóth K, et al: Promoter hypermethylation-related reduced somatostatin production promotes uncontrolled cell proliferation in colorectal cancer. PLoS One. 10:e01183322015. View Article : Google Scholar : PubMed/NCBI
39	Liu J, Li H, Sun L, Wang Z, Xing C and Yuan Y: Aberrantly methylated-differentially expressed genes and pathways in colorectal cancer. Cancer Cell Int. 17:752017. View Article : Google Scholar : PubMed/NCBI
40	Li H, Liu JW, Liu S, Yuan Y and Sun LP: Bioinformatics-based identification of methylated-differentially expressed genes and related pathways in gastric cancer. Dig Dis Sci. 62:3029–3039. 2017. View Article : Google Scholar : PubMed/NCBI
41	Han M, Xu R, Wang S, Yang N, Ni S, Zhang Q, Xu Y, Zhang X, Zhang C, Wei Y, et al: Six-transmembrane epithelial antigen of prostate 3 predicts poor prognosis and promotes glioblastoma growth and invasion. Neoplasia. 20:543–554. 2018. View Article : Google Scholar : PubMed/NCBI
42	Alaminos M, Davalos V, Ropero S, Setién F, Paz MF, Herranz M, Fraga MF, Mora J, Cheung NK, Gerald WL and Esteller M: EMP3, a myelin-related gene located in the critical 19q13.3 region, is epigenetically silenced and exhibits features of a candidate tumor suppressor in glioma and neuroblastoma. Cancer Res. 65:2565–2571. 2005. View Article : Google Scholar : PubMed/NCBI
43	Taylor V and Suter U: Epithelial membrane protein-2 and epithelial membrane protein-3: Two novel members of the peripheral myelin protein 22 gene family. Gene. 175:115–120. 1996. View Article : Google Scholar : PubMed/NCBI
44	Ben-Porath I and Benvenisty N: Characterization of a tumor-associated gene, a member of a novel family of genes encoding membrane glycoproteins. Gene. 183:69–75. 1996. View Article : Google Scholar : PubMed/NCBI