Open Access

Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data

  • Authors:
    • Lijuan Bo
    • Bo Wei
    • Zhanfeng Wang
    • Daliang Kong
    • Zheng Gao
    • Zhuang Miao
  • View Affiliations

  • Published online on: October 19, 2017     https://doi.org/10.3892/mmr.2017.7834
  • Pages: 9503-9511
  • Copyright: © Bo et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Gene expression data were analysed using bioinformatic tools to demonstrate molecular mechanisms underlying the glioma CpG island methylator phenotype (CIMP). A gene expression data set (accession no. GSE30336) was downloaded from Gene Expression Omnibus, including 36 CIMP+ and 16 CIMP- glioma samples. Differential analysis was performed for CIMP+ vs. CIMP‑ samples using the limma package in R. Functional enrichment analysis was subsequently conducted for differentially expressed genes (DEGs) using Database for Annotation, Visualization and Integration Discovery. Protein‑protein interaction (PPI) networks were constructed for upregulated and downregulated genes with information from STRING. MicroRNAs (miRNAs) targeting DEGs were also predicted using WebGestalt. A total of 439 DEGs were identified, including 214 upregulated and 198 downregulated genes. The upregulated genes were involved in extracellular matrix organisation, defence and immune response, collagen fibril organisation and regulation of cell motion and the downregulated genes in cell adhesion, sensory organ development, regulation of system process, neuron differentiation and membrane organisation. A PPI network containing 134 nodes and 314 edges was constructed from the upregulated genes, whereas a PPI network consisting of 85 nodes and 80 edges was obtained from the downregulated genes. miRNAs regulating upregulated and downregulated genes were predicted, including miRNA‑124a and miRNA‑34a. Numerous key genes associated with glioma CIMP were identified in the present study. These findings may advance the understanding of glioma and facilitate the development of appropriate therapies.

Introduction

Malignant glioma is the most common central nervous system tumour in adults and is associated with significant morbidity and mortality (1). Gliomas are highly invasive and poorly respond to conventional treatments; therefore, further studies to support the development of therapy for them are warranted (2).

Alterations in methylation serve a critical role in the pathogenesis of numerous human malignancies, including gliomas (3). CpG island methylator phenotype (CIMP) has emerged as a distinct molecular subclass of tumours (4). It features extensive, coordinated hypermethylation at specific loci (5,6). Several key genes regulated by methylation have been previously identified. O6-methylguanine-DNA methyltransferase (MGMT), which is responsible for DNA repair, is associated with chemotherapy resistance (7). Previous studies indicated that epigenetic silencing of MGMT via promoter methylation serves an important role in the regulation of MGMT expression in gliomas (8). Bruna et al (9) demonstrated that the methylation of platelet-derived growth factor (PDGF)-B can dictate transforming growth factor-β as an oncogenic factor to promote cell proliferation in human glioma. In addition, Wiencke et al (10) reported that methylation of the phosphatase and tensin homolog promoter defines low-grade gliomas and secondary glioblastoma. Mueller et al (11) also suggested that epigenetic dysregulation of runt-related transcription factor 3 and testin is involved in glioblastoma tumorigenesis. Abnormal DNA methylation of CD133 (12) and tumor protein 53 (13) is also observed in glioma. Additionally, Turcan et al (14) indicated that isocitrate dehydrogenase 1 mutation is sufficient to establish the glioma hypermethylator phenotype. However, the identification of glioma-CIMP (G-CIMP) tumours based on gene expression data has rarely been reported (15). In the present study, gene expression profiles of CIMP-positive (CIMP+) samples were compared with those of CIMP-negative (CIMP) samples to identify differentially expressed genes (DEGs), which were further subjected to functional enrichment analysis and network analyses. The findings of the present study may extend the understanding of the molecular mechanisms of CIMP+ glioma.

Materials and methods

Gene expression data

A gene expression data set (accession no. GSE30336) was downloaded from Gene Expression Omnibus (14), including 36 CIMP+ glioma and 16 CIMP samples. Gene expression levels were measured using the GPL571 (HG-U133A_2) Affymetrix Human Genome U133A 2.0 Array (Affymetrix; Thermo Fisher Scientific Inc., Waltham, MA, USA). Probe annotations were also acquired.

Pretreatment and differential analysis

Raw data were pre-treated with the Robust Multichip Average method using the Affy package of R (www.bioconductor.org/packages/release/bioc/html/affy.html). Differential analysis was performed for CIMP+ vs. CIMP using the limma package (16) of R. |Log (fold change)| >1.0 and P<0.05 were set as cut-offs for significant differential expression.

Functional enrichment analysis

The Gene Ontology (GO; www.geneontology.org/) database is a bioinformatics resource that can provide functional categorization and annotations for gene products via the use of structured, controlled vocabularies (17). The Kyoto Encyclopaedia of Genes and Genome (KEGG; www.genome.jp/kegg) is a database for systematic analysis of the functions of genes or proteins in several specific metabolic and regulatory pathways (18). Functional enrichment analyses of the GO and KEGG databases were conducted using the Database for Annotation, Visualization and Integration Discovery (david.abcc.ncifcrf.gov/) (19). The statistical method for this was based on hypergeometric distribution. P<0.05 was considered to indicate significant functions and pathways.

Construction of protein-protein interaction (PPI) network

Proteins work together to complete certain biological functions. Therefore, revealing PPI is useful in elucidating underlying molecular mechanisms. In the present study, PPI networks were constructed for upregulated and downregulated genes using information from STRING (20). Interactions with the required level of confidence (i.e., score >0.4) were retained in the network. The two networks were visualised using Cytoscape (21).

Proteins in the network were presented as ‘nodes’, and each pairwise protein interaction was represented by an undirected link and the ‘degree’ of a node corresponded to the number of interactions by the protein. ‘Degree’ was calculated for each node.

Prediction of miRNAs and construction of the whole regulatory network

Web-based Gene Set Enrichment Analysis Toolkit (WebGestalt; www.webgestalt.org/option.php) is a comprehensive and powerful analysis toolkit, which can be used for enrichment analysis and microRNA (miRNA)-target prediction by identifying miRNA-binding site motifs. In the present study, miRNAs regulating DEGs were predicted using WebGestalt (22). Count ≥2 was set as the cut-off for predicted miRNAs and the top 10 miRNAs were selected. Following miRNA-target gene network pairs using WebGestalt, PPI networks and miRNA-target gene interactions were integrated. Subsequently, the whole regulatory network was visualised using Cytoscape (21).

Results

DEGs

A total of 41,335 genes were detected and 439 DEGs between CIMP+ and CIMP samples were identified, including 241 upregulated and 198 downregulated genes in CIMP+ samples.

Functional enrichment analysis

The GO biological pathway terms enriched for the 241 upregulated genes in CIMP+ samples could be divided into 13 clusters. They were associated with extracellular matrix organisation, defence response, immune response, collagen fibril organisation, and regulation of cell motion. The top 10 terms are listed in Table I.

Table I.

GO biological process terms enriched in the differentially expressed genes.

Table I.

GO biological process terms enriched in the differentially expressed genes.

A, Upregulated genes

GO termCount(%)P-value
GO:0030198 extracellular matrix organization135.676855895 2.62×10−8
GO:0030199 collagen fibril organization  83.493449782 1.25×10−7
GO:0002504 antigen processing and presentation of peptide or polysaccharide antigen via major histocompatibility complex class II  83.493449782 3.25×10−7
GO:0009611 response to wounding2510.91703057 4.95×10−7
GO:0006955 immune response2812.22707424 1.58×10−6
GO:0043062 extracellular structure organization135.676855895 3.55×10−6
GO:0006952 defense response2510.91703057 6.64×10−6
GO:0016064 immunoglobulin mediated immune response83.493449782 1.05×10−5
GO:0019724 B cell mediated immunity83.493449782 1.34×10−5
GO:0006954 inflammatory response177.423580786 1.59×10−5

B, Downregulated genes

GO termCount(%)P-value

GO:0007423 sensory organ development115.882352941 2.00×10−4
GO:0007155 cell adhesion2010.69518717 2.07×10−4
GO:0022610 biological adhesion2010.69518717 2.10×10−4
GO:0030182 neuron differentiation158.021390374 2.97×10−4
GO:0048666 neuron development136.951871658 3.27×10−4
GO:0044057 regulation of system process126.417112299 5.55×10−4
GO:0048592 eye morphogenesis  63.20855615 9.10×10−4
GO:0051966 regulation of synaptic transmission, glutamatergic  42.139037433 1.07×10−3
GO:0031175 neuron projection development105.347593583 1.93×10−3
GO:0015672 monovalent inorganic cation transport115.882352941 2.48×10−3

[i] GO, Gene Ontology.

The GO biological pathway terms enriched for the 198 downregulated genes were divided into 12 clusters. They were associated with cell adhesion, sensory organ development, system process regulation, neuron differentiation and membrane organisation. The top 10 terms are listed in Table I.

KEGG pathway enrichment analysis revealed 16 significant pathways associated with upregulated genes (Table II), including focal adhesion (hsa04510), asthma (hsa05310), ECM-receptor interaction (hsa04512), intestinal immune network for immunoglobulin A production (hsa04672) and allograft rejection (hsa05330). No significant pathway was identified for the downregulated genes.

Table II.

Kyoto Encyclopaedia of Genes and Genome pathways enriched in the upregulated genes.

Table II.

Kyoto Encyclopaedia of Genes and Genome pathways enriched in the upregulated genes.

TermCountP-value
hsa04510:Focal adhesion15 2.96×10−6
hsa05310:Asthma  7 5.16×10−6
hsa04512:Extracellular matrix-receptor interaction10 6.52×10−6
hsa04672:Intestinal immune network for immunoglobulin A production  8 1.09×10−5
hsa05330:Allograft rejection  7 1.94×10−5
hsa05322:Systemic lupus erythematosus10 2.52×10−5
hsa05332:Graft-vs.-host disease  7 3.12×10−5
hsa04514:Cell adhesion molecules11 4.31×10−5
hsa04940:Type I diabetes mellitus  7 4.83×10−5
hsa05416:Viral myocarditis  8 1.27×10−4
hsa05320:Autoimmune thyroid disease  7 1.48×10−4
PPI networks of the DEGs

A PPI network containing 134 nodes and 314 edges was constructed for the upregulated genes (Fig. 1), whereas a PPI network consisting of 85 nodes and 80 edges was obtained for the downregulated genes (Fig. 2).

The top ten nodes with a high degree in the up and downregulated PPI networks are listed in Table III. The top five nodes in the network of upregulated genes were collagen type III α1 (COL3A1), collagen type V α2 (COL5A2), TIMP metallopeptidase inhibitor 1 (TIMP1), collagen type V α1 (COL5A1) and vimentin (VIM). In the network of downregulated genes, the top six nodes were glutamate receptor ionotropic AMPA2 (GRIA2), bone morphogenetic protein 2 (BMP2), protein kinase X-linked (PRKX), v-myc avian myelocytomatosis viral oncogene homolog (MYC), tight junction protein 2 (TJP2) and platelet-derived growth factor receptor α polypeptide (PDGFRA).

Table III.

Top 10 nodes with a high degree in the up and downregulated protein-protein interaction network.

Table III.

Top 10 nodes with a high degree in the up and downregulated protein-protein interaction network.

GeneDegree
Upregulated
  COL3A122
  COL5A218
  TIMP116
  COL5A116
  VIM15
  ANXA213
  S100A612
  ANXA112
  COL4A111
  CXCL1011
Downregulated
  GRIA26
  BMP25
  PRKX5
  MYC5
  TJP25
  PDGFRA5
  DCX4
  SH3GL24
  RTN14
  ID14

[i] PPI, protein-protein interaction.

miRNA prediction and regulatory network analysis

The distribution of upregulated and downregulated genes in biological processes was analysed using WebGestalt (Figs. 3 and 4, respectively). The regulatory miRNAs of DEGs were also predicted (Table IV). Among these predicted miRNAs, miRNA-506 and miR-34b targeted the most DEGs in the up- and downregulated regulatory networks. In the upregulated regulatory network, miRNA-506 (miR-506) regulated five upregulated genes: VIM, aryl hydrocarbon receptor, proteolipid protein 2, IQ motif-containing GTPase activating protein 1 (IQGAP1) and syndecan 4. In the downregulated regulatory network, miR-34b regulated four downregulated genes: Sex determining region Y-box 4, PDGFRA, activated leukocyte cell adhesion molecule (ALCAM) and MYC. All predicted miRNAs with their target DEG pairs are presented in the regulatory network (Fig. 5).

Table IV.

Predicted miRs targeting the differentially expressed genes.

Table IV.

Predicted miRs targeting the differentially expressed genes.

A, Upregulated genes

miRGeneStatistics
hsa_TTGCACT, miR-130a, miR-301, miR-130b2C=52; O=2; E=6.41; R=0.31; raw P=1.0000; adj P=1.0000
hsa_TTTGCAC, miR-19a, miR-19b2C=71; O=2; E=8.76; R=0.23; raw P=1.0000; adj P=1.0000
hsa_TGGTGCT, miR-29a, miR-29b, miR-29c3C=59; O=3; E=7.28; R=0.41; raw P=1.0000; adj P=1.0000
hsa_TGCCTTA, miR-124a3C=84; O=3; E=10.36; R=0.29; raw P=1.0000; adj P=1.0000
hsa_GTGCCTT, miR-5065C=105; O=5; E=12.95; R=0.39; raw P=1.0000; adj P=1.0000
hsa_ACATTCC, miR-1, miR-2063C=61; O=3; E=7.52; R=0.40; raw P=1.0000; adj P=1.0000
hsa_ACACTCC, miR-122a2C=13; O=2; E=1.60; R=1.25; raw P=0.4895; adj P=1.0000
hsa_ATGTTTC, miR-4942C=28; O=2; E=3.45; R=0.58; raw P=1.0000; adj P=1.0000
hsa_GGGACCA, miR-133a, miR-133b2C=37; O=2; E=4.56; R=0.44; raw P=1.0000; adj P=1.0000
hsa_ATTCTTT, miR-1862C=45; O=2; E=5.55; R=0.36; raw P=1.0000; adj P=1.0000

B, Downregulated genes

miRGeneStatistics

hsa_ACTGCCT, miR-34b4C=41; O=4; E=4.11; R=0.97; raw P=1.0000; adj P=1.0000
hsa_CACTGCC, miR-34a, miR-34c, miR-4493C=47; O=3; E=4.71; R=0.64; raw P=1.0000; adj P=1.0000
hsa_AAACCAC, miR-1402C=25; O=2; E=2.50; R=0.80; raw P=1.0000; adj P=1.0000
hsa_TAGCTTT, miR-92C=31; O=2; E=3.11; R=0.64; raw P=1.0000; adj P=1.0000
hsa_TGCACTT, miR-519c, miR-519b, miR-519A3C=54; O=3; E=5.41; R=0.55; raw P=1.0000; adj P=1.0000
hsa_GTTAAAG, miR-302b2C=9; O=2; E=0.90; R=2.22; raw P=0.2255; adj P=1.0000
hsa_AACTGGA, miR-1452C=33; O=2; E=3.31; R=0.61; raw P=1.0000; adj P=1.0000
hsa_ACCAAAG, miR-92C=67; O=2; E=6.71; R=0.30; raw P=1.0000; adj P=1.0000
hsa_TGCTGCT, miR-15a, miR-16, miR-15b, miR-195, miR-424, miR-4972C=86; O=2; E=8.61; R=0.23; raw P=1.0000; adj P=1.0000
hsa_CTGAGCC, miR-243C=35; O=3; E=3.51; R=0.86; raw P=1.0000; adj P=1.0000

[i] miR, microRNA; C, number of reference genes in the category; O, number of genes in the gene set and also in the category; E, expected number in the category; R, ratio of enrichment; raw P, P-value from hypergeometric test; adj P, adjusted P-value.

Discussion

In the present study, a total of 439 DEGs were identified, including 241 upregulated and 198 downregulated genes. Functional enrichment analysis predicted that upregulated genes were associated with extracellular matrix organisation, defence response, immune response, collagen fibril organisation and regulation of cell motion, whereas downregulated genes were associated with cell adhesion, sensory organ development, regulation of system process, neuron differentiation and membrane organisation. These findings are consistent with previous reports (2326). Ulrich et al (27) pointed out that the mechanical rigidity of the extracellular matrix regulates the structure, motility and proliferation of glioma cells. Cell motion and cell adhesion were closely associated with the invasion of glioma cells.

In the present study, a PPI network containing 134 nodes and 314 edges was constructed for upregulated genes, whereas a PPI network consisting of 85 nodes and 80 edges was also obtained for downregulated genes. The top five nodes in the network of upregulated genes were COL3A1, COL5A2, TIMP1, COL5A1 and VIM. TIMP1, as an inhibitor of matrix metalloproteinases, can promote cell proliferation and may have anti-apoptotic function (28). Groft et al (29) reported the differential expression and localisation of TIMP-1 and TIMP-4 in human gliomas and suggested that they may contribute to the pathophysiology of human malignant gliomas. In addition, Aaberg-Jessen et al (30) demonstrated that low expression of tissue inhibitor of TIMP-1 in glioblastoma predicts longer patient survival. Serum TIMP-1 level is also regarded as an independent predictor of survival (31). VIM is a member of the intermediate filament family and functions as an organiser of numerous critical proteins involved in cell adhesion, migration and cell signalling (32). Overexpression of VIM has been reported in central nervous system tumours and it strongly correlates with accelerated tumour growth, invasion and poor prognosis (33). The top six nodes in the network of downregulated genes in the present study were GRIA2, BMP2, PRKX, MYC, TJP2 and PDGFRA. BMP2 is involved in cell differentiation. Deregulation of the BMP developmental pathway in glioblastoma-initiating cells contributes to their tumorigenicity both by desensitising cells to normal differentiation cues and converting otherwise cytostatic signals to pro-proliferative ones (34). Liu et al (35) indicated that BMP2 expression levels may be a potent tool for assessing the clinical prognosis of glioma patients. In addition, Wang et al (36) reported that c-MYC is required for the maintenance of glioma cancer stem cells. Furthermore, Jensen et al (37) demonstrated that astroglial c-MYC overexpression predisposes mice to primary malignant gliomas. Overexpression of PDGFRA has also been reported in gliomas (38,39). Taken these findings together, TIMP-1, VIM, BMP2, MYC and PDGFRA may be associated with the development of glioma.

miRNAs regulating upregulated and downregulated genes identified in the present study were predicted using WebGestalt, including miR-124a and miR-34a. miR-124a can inhibit the proliferation of glioblastoma multiforme cells and induce the differentiation of brain tumour stem cells (40). It is frequently downregulated in glioblastoma and is involved in migration and invasion (41). IQGAP1 is one of its target genes, which has been implicated in the regulation of E-cadherin-mediated cell-cell adhesion (42,43). In addition, miR-34a can inhibit glioblastoma growth by targeting multiple oncogenes (44). In the present study, we predicted that miR-34a regulates PDGFRA and ALCAM, which were downregulated in CIMP+ samples. This is noteworthy because PDGFRA is involved in tumour progression (45) and its suppression by targeting miR-34a could contribute to tumorigenesis in pro-neural malignant gliomas (46), whereas ALCAM is associated with cell adhesion and cell migration (47). Therefore, it is speculated that these miRNAs may be useful for treating gliomas but further confirmation is needed.

Although we identified several DEGs that were important to define a distinct subgroup of glioma and understand the progression of glioma CIMP, there are certain limitations in the present study. The association between DEGs and methylation level in the different CIMPs was not investigated due to the lack of information on DEGs methylation levels in the dataset used. Additionally, experimental or data verification for the DEGs identified in glioma CIMP was not conducted, and in future, samples should be divided into different CIMPs based on methylation analysis to conduct the experimental validation.

In conclusion, several key genes were identified in glioma CIMP, some of which (TIMP1, VIM, BMP2, c-MYC and PDGFRA) may be viewed as potential markers or therapeutic targets for gliomas. In addition, relevant miRNAs, such as miR-124a and miR-34a that regulate genes involved in gliomas were also detected. These findings may provide helpful guidance to reveal molecular mechanisms underlying glioma CIMP.

Glossary

Abbreviations

Abbreviations:

CIMP

CpG island methylator phenotype

DEGs

differentially expressed genes

PPI

protein-protein interaction

MGMT

O6-methylguanine-DNA methyltransferase

PDGF

platelet-derived growth factor

GO

Gene Ontology

COL3A1

collagen type III α1

COL5A2

collagen type V α2

TIMP1

TIMP metallopeptidase inhibitor 1

COL5A1

collagen type V α1

VIM

vimentin

GRIA2

glutamate receptor ionotropic AMPA2

BMP2

bone morphogenetic protein 2

PRKX

protein kinase X-linked

MYC

v-myc avian myelocytomatosis viral oncogene homolog

TJP2

tight junction protein 2

PDGFRA

platelet-derived growth factor receptor α polypeptide

IQGAP1

IQ motif-containing GTPase activating protein 1

ALCAM

activated leukocyte cell adhesion molecule

References

1 

Kim TY, Zhong S, Fields CR, Kim JH and Robertson KD: Epigenomic profiling reveals novel and frequent targets of aberrant DNA methylation-mediated silencing in malignant glioma. Cancer Res. 66:7490–7501. 2006. View Article : Google Scholar : PubMed/NCBI

2 

Pan D, Wei X, Liu M, Feng S, Tian X, Feng X and Zhang X: Adenovirus mediated transfer of p53, GM-CSF and B7-1 suppresses growth and enhances immunogenicity of glioma cells. Neurol Res. 32:502–509. 2010. View Article : Google Scholar : PubMed/NCBI

3 

Christensen BC, Smith AA, Zheng S, Koestler DC, Houseman EA, Marsit CJ, Wiemels JL, Nelson HH, Karagas MR, Wrensch MR, et al: DNA methylation, isocitrate dehydrogenase mutation, and survival in glioma. J Natl Cancer Inst. 103:143–153. 2011. View Article : Google Scholar : PubMed/NCBI

4 

Noushmehr H, Weisenberger DJ, Diefes K, Phillips HS, Pujara K, Berman BP, Pan F, Pelloski CE, Sulman EP, Bhat KP, et al: Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell. 17:510–522. 2010. View Article : Google Scholar : PubMed/NCBI

5 

Fang F, Turcan S, Rimner A, Kaufman A, Giri D, Morris LG, Shen R, Seshan V, Mo Q, Heguy A, et al: Breast cancer methylomes establish an epigenomic foundation for metastasis. Sci Transl Med. 3:75ra252011. View Article : Google Scholar : PubMed/NCBI

6 

Cheng YW, Pincas H, Bacolod MD, Schemmann G, Giardina SF, Huang J, Barral S, Idrees K, Khan SA, Zeng Z, et al: CpG island methylator phenotype associates with low-degree chromosomal abnormalities in colorectal cancer. Clin Cancer Res. 14:6005–6013. 2008. View Article : Google Scholar : PubMed/NCBI

7 

Weller M, Stupp R, Reifenberger G, Brandes AA, van den Bent MJ, Wick W and Hegi ME: MGMT promoter methylation in malignant gliomas: Ready for personalized medicine? Nat Rev Neurol. 6:39–51. 2010. View Article : Google Scholar : PubMed/NCBI

8 

Hegi ME, Liu L, Herman JG, Stupp R, Wick W, Weller M, Mehta MP and Gilbert MR: Correlation of O6-methylguanine methyltransferase (MGMT) promoter methylation with clinical outcomes in glioblastoma and clinical strategies to modulate MGMT activity. J Clin Oncol. 26:4189–4199. 2008. View Article : Google Scholar : PubMed/NCBI

9 

Bruna A, Darken RS, Rojo F, Ocaña A, Peñuelas S, Arias A, Paris R, Tortosa A, Mora J, Baselga J and Seoane J: High TGFbeta-Smad activity confers poor prognosis in glioma patients and promotes cell proliferation depending on the methylation of the PDGF-B gene. Cancer Cell. 11:147–160. 2007. View Article : Google Scholar : PubMed/NCBI

10 

Wiencke JK, Zheng S, Jelluma N, Tihan T, Vandenberg S, Tamgüney T, Baumber R, Parsons R, Lamborn KR, Berger MS, et al: Methylation of the PTEN promoter defines low-grade gliomas and secondary glioblastoma. Neuro Oncol. 9:271–279. 2007. View Article : Google Scholar : PubMed/NCBI

11 

Mueller W, Nutt CL, Ehrich M, Riemenschneider MJ, Von Deimling A, Van Den Boom D and Louis DN: Downregulation of RUNX3 and TES by hypermethylation in glioblastoma. Oncogene. 26:583–593. 2007. View Article : Google Scholar : PubMed/NCBI

12 

Yi JM, Tsai HC, Glöckner SC, Lin S, Ohm JE, Easwaran H, James CD, Costello JF, Riggins G, Eberhart CG, et al: Abnormal DNA methylation of CD133 in colorectal and glioblastoma tumors. Cancer Res. 68:8094–8103. 2008. View Article : Google Scholar : PubMed/NCBI

13 

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

14 

Turcan S, Rohle D, Goenka A, Walsh LA, Fang F, Yilmaz E, Campos C, Fabius AW, Lu C, Ward PS, et al: IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature. 483:479–483. 2012. View Article : Google Scholar : PubMed/NCBI

15 

Baysan M, Bozdag S, Cam MC, Kotliarova S, Ahn S, Walling J, Killian JK, Stevenson H, Meltzer P and Fine HA: G-cimp status prediction of glioblastoma samples using mRNA expression data. PLoS One. 7:e478392012. View Article : Google Scholar : PubMed/NCBI

16 

Diboun I, Wernisch L, Orengo CA and Koltzenburg M: Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genomics. 7:2522006. View Article : Google Scholar : PubMed/NCBI

17 

Gene Ontology Consortium, ; Blake JA, Dolan M, Drabkin H, Hill DP, Li N, Sitnikov D, Bridges S, Burgess S, Buza T, et al: Gene Ontology annotations and resources. Nucleic Acids Res. 41(Database issue): D530–D535. 2013.PubMed/NCBI

18 

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

19 

Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, Stephens R, Baseler MW, Lane HC and Lempicki RA: The DAVID gene functional classification tool: A novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 8:R1832007. View Article : Google Scholar : PubMed/NCBI

20 

Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, et al: The STRING database in 2011: Functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39(Database issue): D561–D568. 2011. View Article : Google Scholar : PubMed/NCBI

21 

Smoot ME, Ono K, Ruscheinski J, Wang PL and Ideker T: Cytoscape 2.8: New features for data integration and network visualization. Bioinformatics. 27:431–432. 2011. View Article : Google Scholar : PubMed/NCBI

22 

Zhang B, Kirov S and Snoddy J: WebGestalt: An integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res. 33(Web server issue): W741–W748. 2005. View Article : Google Scholar : PubMed/NCBI

23 

Yao ZQ and Lu YC: Research on molecular mechanism of human glioma and its clinical application. Chin J Cancer Biother. 15:90–94. 2008.

24 

Huse JT and Holland EC: Targeting brain cancer: Advances in the molecular pathology of malignant glioma and medulloblastoma. Nat Rev Cancer. 10:319–331. 2010. View Article : Google Scholar : PubMed/NCBI

25 

Clubb BH and Shivers RR: Extracellular matrix regulates microfilament and vinculin organization in C6-glioma cells. Acta Neuropathol. 91:31–40. 1996. View Article : Google Scholar : PubMed/NCBI

26 

Bauke AC, Sasse S, Matzat T and Klämbt C: A transcriptional network controlling glial development in the Drosophila visual system. Development. 142:2184–2193. 2015. View Article : Google Scholar : PubMed/NCBI

27 

Ulrich TA, de Juan Pardo EM and Kumar S: The mechanical rigidity of the extracellular matrix regulates the structure, motility, and proliferation of glioma cells. Cancer Res. 69:4167–4174. 2009. View Article : Google Scholar : PubMed/NCBI

28 

Lee SJ, Yoo HJ, Bae YS, Kim HJ and Lee ST: TIMP-1 inhibits apoptosis in breast carcinoma cells via a pathway involving pertussis toxin-sensitive G protein and c-Src. Biochem Biophys Res Commun. 312:1196–1201. 2003. View Article : Google Scholar : PubMed/NCBI

29 

Groft LL, Muzik H, Rewcastle NB, Johnston RN, Knäuper V, Lafleur MA, Forsyth PA and Edwards DR: Differential expression and localization of TIMP-1 and TIMP-4 in human gliomas. Br J Cancer. 85:55–63. 2001. View Article : Google Scholar : PubMed/NCBI

30 

Aaberg-Jessen C, Christensen K, Offenberg H, Bartels A, Dreehsen T, Hansen S, Schrøder HD, Brünner N and Kristensen BW: Low expression of tissue inhibitor of metalloproteinases-1 (TIMP-1) in glioblastoma predicts longer patient survival. J Neurooncol. 95:117–128. 2009. View Article : Google Scholar : PubMed/NCBI

31 

Crocker M, Ashley S, Giddings I, Petrik V, Hardcastle A, Aherne W, Pearson A, Bell BA, Zacharoulis S and Papadopoulos MC: Serum angiogenic profile of patients with glioblastoma identifies distinct tumor subtypes and shows that TIMP-1 is a prognostic factor. Neuro Oncol. 13:99–108. 2011. View Article : Google Scholar : PubMed/NCBI

32 

Yamasaki T, Seki N, Yamada Y, Yoshino H, Hidaka H, Chiyomaru T, Nohata N, Kinoshita T, Nakagawa M and Enokida H: Tumor suppressive microRNA-138 contributes to cell migration and invasion through its targeting of vimentin in renal cell carcinoma. Int J Oncol. 41:805–817. 2012. View Article : Google Scholar : PubMed/NCBI

33 

Satelli A and Li S: Vimentin in cancer and its potential as a molecular target for cancer therapy. Cell Mol Life Sci. 68:3033–3046. 2011. View Article : Google Scholar : PubMed/NCBI

34 

Lee J, Son MJ, Woolard K, Donin NM, Li A, Cheng CH, Kotliarova S, Kotliarov Y, Walling J, Ahn S, et al: Epigenetic-mediated dysfunction of the bone morphogenetic protein pathway inhibits differentiation of glioblastoma-initiating cells. Cancer Cell. 13:69–80. 2008. View Article : Google Scholar : PubMed/NCBI

35 

Liu C, Tian G, Tu Y, Fu J, Lan C and Wu N: Expression pattern and clinical prognostic relevance of bone morphogenetic protein-2 in human gliomas. Jpn J Clin Oncol. 39:625–631. 2009. View Article : Google Scholar : PubMed/NCBI

36 

Wang J, Wang H, Li Z, Wu Q, Lathia JD, McLendon RE, Hjelmeland AB and Rich JN: c-Myc is required for maintenance of glioma cancer stem cells. PLoS One. 3:e37692008. View Article : Google Scholar : PubMed/NCBI

37 

Jensen NA, Pedersen KM, Lihme F, Rask L, Nielsen JV, Rasmussen TE and Mitchelmore C: Astroglial c-Myc overexpression predisposes mice to primary malignant gliomas. J Biol Chem. 278:8300–8308. 2003. View Article : Google Scholar : PubMed/NCBI

38 

Puputti M, Tynninen O, Sihto H, Blom T, Mäenpää H, Isola J, Paetau A, Joensuu H and Nupponen NN: Amplification of KIT PDGFRA, VEGFR2, and EGFR in gliomas. Mol Cancer Res. 4:927–934. 2006. View Article : Google Scholar : PubMed/NCBI

39 

Giannini C, Sarkaria JN, Saito A, Uhm JH, Galanis E, Carlson BL, Schroeder MA and James CD: Patient tumor EGFR and PDGFRA gene amplifications retained in an invasive intracranial xenograft model of glioblastoma multiforme. Neuro Oncol. 7:164–176. 2005. View Article : Google Scholar : PubMed/NCBI

40 

Silber J, Lim DA, Petritsch C, Persson AI, Maunakea AK, Yu M, Vandenberg SR, Ginzinger DG, James CD, Costello JF, et al: miR-124 and miR-137 inhibit proliferation of glioblastoma multiforme cells and induce differentiation of brain tumor stem cells. BMC Med. 6:142008. View Article : Google Scholar : PubMed/NCBI

41 

Fowler A, Thomson D, Giles K, Maleki S, Mreich E, Wheeler H, Leedman P, Biggs M, Cook R, Little N, et al: miR-124a is frequently down-regulated in glioblastoma and is involved in migration and invasion. Eur J Cancer. 47:953–963. 2011. View Article : Google Scholar : PubMed/NCBI

42 

Kuroda S, Fukata M, Nakagawa M, Fujii K, Nakamura T, Ookubo T, Izawa I, Nagase T, Nomura N, Tani H, et al: Role of IQGAP1, a target of the small GTPases Cdc42 and Rac1, in regulation of E-cadherin-mediated cell-cell adhesion. Science. 281:832–835. 1998. View Article : Google Scholar : PubMed/NCBI

43 

Noritake J, Watanabe T, Sato K, Wang S and Kaibuchi K: IQGAP1: A key regulator of adhesion and migration. J Cell Sci. 118:2085–2092. 2005. View Article : Google Scholar : PubMed/NCBI

44 

Li Y, Guessous F, Zhang Y, Dipierro C, Kefas B, Johnson E, Marcinkiewicz L, Jiang J, Yang Y, Schmittgen TD, et al: MicroRNA-34a inhibits glioblastoma growth by targeting multiple oncogenes. Cancer Res. 69:7569–7576. 2009. View Article : Google Scholar : PubMed/NCBI

45 

Heinrich MC, Corless CL, Duensing A, Mcgreevey L, Chen CJ, Joseph N, Singer S, Griffith DJ, Haley A, Town A, et al: PDGFRA activating mutations in gastrointestinal stromal tumors. Science. 299:708–710. 2003. View Article : Google Scholar : PubMed/NCBI

46 

Silber J, Jacobsen A, Ozawa T, Harinath G, Holland EC, Sander C and Huse JT: Abstract B15: Repression of PDGFRA-targeting miR-34a promotes tumorigenesis in proneural malignant gliomas. Cancer Res. 72 Suppl 2:B152012. View Article : Google Scholar

47 

Swart GW: Activated leukocyte cell adhesion molecule (CD166/ALCAM): Developmental and mechanistic aspects of cell clustering and cell migration. Eur J Cell Biol. 81:313–321. 2002. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

December-2017
Volume 16 Issue 6

Print ISSN: 1791-2997
Online ISSN:1791-3004

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Bo L, Wei B, Wang Z, Kong D, Gao Z and Miao Z: Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data. Mol Med Rep 16: 9503-9511, 2017
APA
Bo, L., Wei, B., Wang, Z., Kong, D., Gao, Z., & Miao, Z. (2017). Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data. Molecular Medicine Reports, 16, 9503-9511. https://doi.org/10.3892/mmr.2017.7834
MLA
Bo, L., Wei, B., Wang, Z., Kong, D., Gao, Z., Miao, Z."Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data". Molecular Medicine Reports 16.6 (2017): 9503-9511.
Chicago
Bo, L., Wei, B., Wang, Z., Kong, D., Gao, Z., Miao, Z."Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data". Molecular Medicine Reports 16, no. 6 (2017): 9503-9511. https://doi.org/10.3892/mmr.2017.7834