Open Access

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
  • View Affiliations

  • 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.

References

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

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September 2019
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APA
Zhang, M., Lv, X., Jiang, Y., Li, G., & Qiao, Q. (2019). Identification of aberrantly methylated differentially expressed genes in glioblastoma multiforme and their association with patient survival. Experimental and Therapeutic Medicine, 18, 2140-2152. https://doi.org/10.3892/etm.2019.7807
MLA
Zhang, M., Lv, X., Jiang, Y., Li, G., Qiao, Q."Identification of aberrantly methylated differentially expressed genes in glioblastoma multiforme and their association with patient survival". Experimental and Therapeutic Medicine 18.3 (2019): 2140-2152.
Chicago
Zhang, M., Lv, X., Jiang, Y., Li, G., Qiao, Q."Identification of aberrantly methylated differentially expressed genes in glioblastoma multiforme and their association with patient survival". Experimental and Therapeutic Medicine 18, no. 3 (2019): 2140-2152. https://doi.org/10.3892/etm.2019.7807