Open Access

Identification of potential therapeutic targets for gliomas by bioinformatics analysis

  • Authors:
    • Ke Ma
    • Zhihua Cheng
    • Liqun Sun
    • Haibo Li
  • View Affiliations

  • Published online on: August 28, 2017     https://doi.org/10.3892/ol.2017.6850
  • Pages: 5203-5210
  • Copyright: © Ma et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Gliomas are primary tumors that originate in the brain or spinal cord and develop from supportive glial cells. The present study aimed to identify potential candidate molecular markers for the treatment of gliomas, and to explore the underlying mechanisms of this disease. The gene expression profile data GSE50021, which consisted of 10 specimens of normal brain tissues and 35 specimens of glioma tissues, was downloaded from Gene Expression Omnibus (GEO). The methylation microarray data GSE50022, consisting of 28 glioma specimens, was also downloaded from GEO. Differentially expressed genes (DEGs) between patients with glioma and normal individuals were identified, and key methylation sites were screened. Transcriptional regulatory networks were constructed, and target genes were selected. Survival analysis of key methylation sites and risk analysis of sub‑pathways were performed, from which key genes and pathways were selected. A total of 79 DEGs and 179 key methylation sites were identified, of which 20 target genes and 36 transcription factors were included in the transcriptional regulatory network. Glutamate metabotropic receptor 2 (GRM2) was regulated by 8 transcription factors. Inositol‑trisphosphate 3‑kinase A (ITPKA) was a significantly enriched DEG, associated with the inositol phosphate metabolism pathway, Survival analysis revealed that the survival time of patients with lower methylation levels in cg00157228 was longer than patients with higher methylation levels. ITPKA was the closest located gene to cg00157228. In conclusion, GRM2 and enriched ITPKA, associated with the inositol phosphate metabolism pathway, may be key mechanisms in the development and progression of gliomas. Furthermore, the present study provided evidence for an additional mechanism of methylation‑induced gliomas, in which methylation results in the dysregulation of specific transcripts. The results of the present study may provide a research direction for studying the mechanisms underlying the development and progression of gliomas.

Introduction

Gliomas are primary tumors that originate in the brain or spinal cord, and account for ~80% of all malignant brain tumors (1,2). Gliomas occur mostly in childhood, with symptoms including visual loss, pain, nausea, vomiting, weakness in the extremities, headaches and seizures (3,4). Glioma patients have a low survival rate, and of 10,000 Americans diagnosed with malignant gliomas each year, ~50% survive one year following diagnosis, and 25% two years later (5). Therefore, it is essential to explore the molecular mechanisms of glioma and develop effective methods for its treatment.

The methods used to treat gliomas at present are typically a combination of surgery, radiotherapy and chemotherapy, however, the median survival duration of patients with gliomas is only 9–12 months (6). Understanding the molecular mechanisms which underlie this disease is crucial for the development of more effective methods for its treatment (7). Previous studies revealed that methylation of CpG islands within or near promoters were associated with increased gene expression, and may contribute to tumor formation and progression (810). Costello et al (11) demonstrated that methylation of the pl6/CDKN2 tumor suppressor gene was detected in gliomas. Other studies reported that methylation of the promoter in the DNA repair gene O-6-methylguanine-DNA methyltransferase, contributed to the progression of gliomas (12,13). Chen et al (14) demonstrated that the methylation of the excision repair cross-complementation group 1 promoter promoted the development of gliomas. Although previous studies have made advances in the field, the exact mechanisms of methylation-driven gliomas have not been fully elucidated.

The present study aimed to identify methylation-associated genes from differentially expressed genes between patients with glioma and normal controls, in relation to associated pathways of gliomas, to elucidate the underlying molecular mechanisms. Methylation associated genes were identified from differentially expressed genes (DEGs) by methylation analysis. Significant genes and pathways were selected from the transcriptional regulatory network and sub-pathway enrichment analysis. Through the identification of key genes and pathways, the potential underlying molecular mechanisms and the potential biomarkers of gliomas were explored.

Materials and methods

Affymetrix microarray data

The gene expression profile data GSE50021 was downloaded from the Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo/) (15). Gene expression profiling was based on the GPL13938 platform using the Illumina HumanHT-12 WG-DASL V4.0 expression BeadChip (Illumina Inc., San Diego, CA, USA). The array consists of 29,377 probe-sets, which it is possible to use to detect the transcription level of 20,817 human genes. A total of 45 samples, including 10 specimens of normal brain tissues and 35 specimens of glioma tissues from children with a mean age of 1.008±1.910 years were available for the expression array.

The dual channel methylation microarray data GSE50022, was downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo/) (15). Gene expression profiling was based on the platform of GPL16304 using the Illumina HumanMethylation450 BeadChip (UBC enhanced annotation v1.0; Illumina, Inc.). The array consisted of 485,512 probe-sets, which detect >485,000 methylation sites per sample at single-nucleotide resolution. Methylation data of 28 samples from patients with glioma (mean age, 0.943±0.782 years) were analyzed in the present study. The methylation index matrix was processed with GenomeStudio v2011 software (Illumina, Inc.) which indicated the methylation ratios of the probes.

Identification of differentially expressed genes

The raw expression profile data were initially preprocessed using the impute package in R (16). The processed data were normalized using the preprocess Core in R (17). DEGs, between normal brain tissues and glioma tissues were analyzed by limma package in R (18). The fold change (FC) of the expression of individual genes was also calculated for differential expression test. All genes with a P-value <0.05 and log2 FC >1 were considered significant and selected as DEGs.

Screening of key methylation sites

The raw methylation index matrix were initially preprocessed using the impute package in R (16). The methylation sites located around the DEGs were screened according to the methylation chip annotation information. Methylation sites which had a methylation index >0.8 in >80% of samples were selected. Key methylation sites which were located 50 kb upstream and/or downstream of the transcription start site were screened.

Transcriptional regulatory network construction

The selected key methylation sites were mapped to the transcription factor binding site data predicted by the University of California Santa Cruz (UCSC) genome browser (19), and the methylation information in the transcription factor binding site was obtained. The transcriptional regulatory network was constructed using Cytoscape software (version 3.2.0; Institute for Systems Biology, Seattle, WA, USA) (20).

Survival analysis of key methylation sites

Survival analysis of methylation sites was performed based on the methylation index. The samples were divided into two parts according to the mean of the methylation index: One part had high methylation index (>0.87); another part had lower methylation index (≤0.87). A Kaplan-Meier curve based on the survival time of the two parts was constructed, and the log-rank test was used to test for a significant difference between the groups with P<0.05 considered to indicate a statistically significant difference.

Analysis of risk sub-pathway

Gene Ontology (GO) analysis is a commonly used method for functional studies of large-scale genomic or transcriptomic data (21). Kyoto Encyclopedia of Genes and Genomes (KEGG) (22) is a primary information-based database which stores information concerning how molecules and genes are networked. The Database for Annotation Visualization and Integrated Discovery (DAVID) (23) was used to systematically extract biological meaning from large gene or protein lists. GO function and KEGG pathways of downregulated DEGs, regulated by transcription factors were analyzed using DAVID 6.7, with a false discovery rate <0.05.

With in metabolic pathways, the closer the proximity of components in the network, the greater the potential for similarity of the biological functions. Therefore, identification of the sub-pathway of diseases is critical. The K-clique was used to divide the metabolic pathway into sub-pathways through the iSubpathwayMiner package in R (24). Sub-pathways with P<0.05 were considered significant.

Results

Identification of differentially expressed genes

Following analysis of the expression profile data, the expression information of 20,727 genes in 45 samples was obtained. The normalized results revealed that the expression median following normalization was a straight line (Fig. 1). From all the genes recorded, 79 significantly downregulated DEGs were selected. However, no upregulated DEGs were identified.

Screening of key methylation sites

Following preprocessing of the methylation index matrix, 382,049 methylation sites in 28 samples were detected. A total of 79 significantly downregulated DEGs overlapped with the methylation data, and 1,474 methylation sites associated with DEGs were identified. The methylation signals of 1,187 methylation sites were detected in the methylation chip. A total of 204 methylation sites, which had a methylation index >0.8 in >80% of samples were selected. A total of 179 key methylation sites in 65 genes, which were located 50 kb upstream or downstream of the transcription start site, were selected.

Analysis of the transcriptional regulatory network

According to the UCSC genome browser (19), 26 methylation sites were revealed to be located in 42 transcription factor binding sites (Table I). A total of 20 target genes and 36 transcription factors were included in the transcriptional regulatory network (Fig. 2). Based on this, the glutamate metabotropic receptor 2 (GRM2) gene was regulated by 8 transcription factors; the rhomboid-like 3 (RHBDL3) gene was regulated by 4 transcription factors and rhomboid 5 homolog 2 (RHBDF2) had 2 methylation sites in the transcription factor binding sites.

Table I.

Key methylation site information.

Table I.

Key methylation site information.

IDChromosomeMAPINFOtfbs_starttfbs_endtf Distance_closest_TSS Closest_TSS_gene_name
cg06191091chr17305838553058384830583862USF−9339RHBDL3
cg02629157chr9138670609138670546138670568TCF1125013KCNT1
cg11709150chr1244043824404312440444TCF1110256PLCH2
cg04585209chr11629231162922576292272TAXCREB306CCKBR
cg07125541chr12113534668113534663113534687STAT5A38736RASAL1
cg10707626chr3517470985174702751747051STAT5A6018GRM2
cg06191091chr17305838553058384930583860SREBP1−9339RHBDL3
cg12603173chr11645084216450840964508423RREB1−66RASGRP2
cg11025960chr3517491885174917751749195RFX18108GRM2
cg10692302chr3517472275174722451747245PPARG6147GRM2
cg02629157chr9138670609138670558138670569POU6F125013KCNT1
cg11014582chr6763337277633367576333696PAX6−852LMO7
cg04341461chr1241000624099782410006PAX5−1616PLCH2
cg05289873chr17403216364032157640321597PAX411660KCNH4
cg10692302chr3517472275174722251747252PAX46147GRM2
cg04625615chr15417883684178831041788330P532313ITPKA
cg07200386chr8220791692207911322079135OLF110682PHYHIP
cg11014582chr6763337277633367676333683NKX25−852LMO7
cg09864712chr16726786726720726749MYOGNF1712RHBDL1
cg06191091chr17305838553058384830583862MYCMAX−9339RHBDL3
cg00810908chr3136123191361230613612320 MEIS1AHOXA92080FBLN2
cg11025960chr3517491885174918151749190LMO2COM8108GRM2
cg03358506chr8220587022205868822058703ISRE31149PHYHIP
cg07776629chr16579891225798911657989129IRF215898CNGB1
cg07776629chr16579891225798911657989129IRF115898CNGB1
cg10692302chr3517472275174722551747244HNF46147GRM2
cg06632557chr11613135486131349561313505HMX1−3678SYT7
cg00155846chr9138011566138011506138011522 HAND1E4714081OLFM1
cg11025960chr3517491885174918151749190GATA38108GRM2
cg11025960chr3517491885174917951749193GATA18108GRM2
cg04625615chr15417883684178831041788321GATA32313ITPKA
cg05392169chr9138011814138011802138011816FOXO314329OLFM1
cg05289873chr17403216364032158540321597CREB11660KCNH4
cg12309456chr17744754027447534674475357CP22225RHBDF2
cg12163800chr17744753557447534674475357CP22272RHBDF2
cg07012189chr14934080439340803893408062COMP118599CHGA
cg04585209chr11629231162922516292269CMYB306CCKBR
cg05392169chr9138011814138011806138011824CMYB14329OLFM1
cg05934090chr22388231883882313738823161BRACH949KCNJ4
cg10368536chr16675181796751816867518184ARP1−463AGRP
cg06191091chr17305838553058384730583863ARNT−9339RHBDL3
cg03358506chr8220587022205869422058703AREB631149PHYHIP

[i] ID, probe number in methylation chip; MAPINFO, methylation position; tfbs_start, the starting point in transcription factor binding sites; tfbs_end, the end point in transcription factor binding sites; TF, transcription factor; Distance_closest_TSS, the nearest transcription start point position; Closest_TSS_gene_name, the nearest gene.

Survival analysis of key methylation sites

Survival analysis of the 204 methylation sites demonstrated that cg00157228 significantly affected the survival time of patients. The survival time of patients with lower methylation levels in cg00157228 was increased compared with patients with higher methylation levels in cg00157228 (Fig. 3). Inositol-triphosphate 3 kinase A (ITPKA) was the gene located closest to cg00157228.

Analysis of risk sub-pathways

GO analysis of 20 target genes confirmed that specific DEGs were significantly enriched in different GO categories, which were associated with biological processes including potassium ion transport, monovalent inorganic cation transport and ion transport (Table II). However, the 20 target genes were not significantly enriched in any pathways. A total of 8 glioma related sub-pathways were mined from the inositol phosphate metabolism pathway. ITPKA was the DEG enriched in these 8 sub-pathways (Fig. 4).

Table II.

GO analysis of the differentially expressed genes.

Table II.

GO analysis of the differentially expressed genes.

CategoryTermCountP-valueFDR
GOTERM_BP_FAT GO:0006813-potassium ion transport  30.01314.588
GOTERM_BP_FAT GO:0007242-intracellular signaling cascade  50.04441.371
GOTERM_BP_FAT GO:0015672-monovalent inorganic cation transport  30.04743.728
GOTERM_BP_FATGO:0006811-ion transport  40.05045.428
GOTERM_CC_FATGO:0034703-cation channel complex  30.01210.880
GOTERM_CC_FATGO:0044459-plasma membrane part  80.01211.321
GOTERM_CC_FATGO:0005886-plasma membrane100.02320.748
GOTERM_CC_FATGO:0034702-ion channel complex  30.02623.371
GOTERM_MF_FATGO:0005509-calcium ion binding  7<0.0010.864
GOTERM_MF_FATGO:0005261-cation channel activity  40.0055.578
GOTERM_MF_FATGO:0022836-gated channel activity  40.0077.698
GOTERM_MF_FATGO:0046873-metal ion transmembrane transporter activity  40.0088.936
GOTERM_MF_FAT GO:0030955-potassium ion binding  30.01212.513
GOTERM_MF_FAT GO:0005267-potassium channel activity  30.01313.401
GOTERM_MF_FATGO:0005216-ion channel activity  40.01313.593
GOTERM_MF_FAT GO:0022838-substrate specific channel activity  40.01414.678
GOTERM_MF_FATGO:0015267-channel activity  40.01515.992
GOTERM_MF_FATGO:0022803-passive transmembrane transporter activity  40.01616.088
GOTERM_MF_FATGO:0046872-metal ion binding110.02020.008
GOTERM_MF_FATGO:0043169-cation binding110.02121.264
GOTERM_MF_FATGO:0043167-ion binding110.02423.361
GOTERM_MF_FAT GO:0004435-phosphoinositide phospholipase C activity  20.03230.645
GOTERM_MF_FATGO:0031420-alkali metal ion binding  30.03532.899
GOTERM_MF_FAT GO:0004629-phospholipase C activity  20.04036.483

[i] GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; counts, numbers of DEGs; FDR, false discovery rate.

Discussion

Gliomas are the most common malignant tumors of the brain, but the molecular mechanisms underlying the progression of gliomas remain unclear (25). In the present study, a bioinformatics approach was used to predict potential therapeutic targets and explore the possible molecular mechanisms involved. A total of 79 DEGs associated with caspase inhibition were identified. By constructing a transcriptional regulatory network and performing analysis of risk sub-pathways and survival analysis of key methylation sites, we identified key genes and pathways were identified, including GRM2, ITPKA and inositol phosphate metabolism.

GRM2 is a protein-coupled receptor, and is associated with diseases that include schizophrenia (26). GRM2 is expressed in the foetal and the adult brain, and is associated with inhibition of the cyclic adenosine monophosphate pathway (27). Meldrum et al (28) demonstrated that L-glutamate activates metabotropic glutamate receptors and functions as the main excitatory neurotransmitter in the central nervous system. Ullian et al (29) revealed that glutamate receptors may be involved in synaptogenesis or synaptic stabilization. Glutamatergic neurotransmission has been reported to participate in the majority of normal brain functions (30). Furthermore, previous studies have demonstrated that glioma is a primary central nervous system associated cancer (31,32). According to a previous study, the downregulation of GRM2 may be caused by methylation in the promoter, and GRM2 downregulation may promote the progression of gliomas (33). In the present study, GRM2 was downregulated in glioma cells, and 8 methylation sites were identified in the promoter region of GRM2. Transcriptional regulatory networks revealed that methylation in the promoter of GRM2 may influence the binding of 8 transcription factors. Furthermore, GRM2 may be a potential therapeutic target in the treatment of gliomas. Arcella et al (34) revealed that pharmacological blockade of group II metabotropic glutamate receptors reduced the growth of glioma cells in vivo.

Inositol phosphate metabolism was the selected sub-pathway in the present study. Tilly et al (35) demonstrated that stimulation of human epidermoid carcinoma cells using bradykinin, results in very rapid release of inositol phosphates. Lee et al (36) revealed that changes in inositol phosphate metabolism are associated with neoplasia in mouse keratinocytes. Mishra et al (37) demonstrated that inositol phosphates trigger numerous cellular processes by regulating calcium release from internal stores. Another previous study revealed that calcium imbalance is associated with gastric cancer (38). The results of the present study provide evidence that inositol phosphate metabolism was the enriched pathway associated with methylation-induced gene silencing. Thus, inositol phosphate metabolism may be a potential candidate pathway for the treatment of gliomas.

ITPKA is responsible for regulating a large number of inositol polyphosphates that are important in cellular signaling (39). Kato et al (39) indicated that ITPKA was downregulated in oral squamous cell carcinoma, and may be a potential novel molecular target. Windhorst et al (40) demonstrated that ITPKA was a novel cell motility-promoting protein that increased the metastatic potential of tumor cells. In the present study, ITPKA was downregulated and was enriched in the inositol phosphate metabolism pathway. Survival analysis revealed the survival time of patients with lower methylation levels in cg00157228 was longer than patients with higher methylation levels in cg00157228. ITPKA was the nearest gene to cg00157228. Taken together, these results indicated that downregulation of ITPKA due to methylation in cg00157228 may be a potential molecular mechanism involved in the development of gliomas, and may be a potential therapeutic target for novel treatments.

In conclusion, GRM2, ITPKA and inositol phosphate metabolism may contribute to the progression of gliomas. Furthermore, the present study provides an additional mechanism underlying methylation-induced gliomas, which is that methylation results in the dysregulation of specific transcripts. However, further experiments are required to confirm these results.

References

1 

Mamelak AN and Jacoby DB: Targeted delivery of antitumoral therapy to glioma and other malignancies with synthetic chlorotoxin (TM-601). Expert Opin Drug Deliv. 4:175–186. 2007. View Article : Google Scholar : PubMed/NCBI

2 

Goodenberger ML and Jenkins RB: Genetics of adult glioma. Cancer Genet. 205:613–621. 2012. View Article : Google Scholar : PubMed/NCBI

3 

Osoba D, Brada M, Prados MD and Yung WK: Effect of disease burden on health-related quality of life in patients with malignant gliomas. Neuro Oncol. 2:221–228. 2000. View Article : Google Scholar : PubMed/NCBI

4 

Tym R: Piloid gliomas of the anterior optic pathways. Br J Surg. 49:322–331. 1961. View Article : Google Scholar : PubMed/NCBI

5 

Gutin PH, Phillips TL, Wara WM, Leibel SA, Hosobuchi Y, Levin VA, Weaver KA and Lamb S: Brachytherapy of recurrent malignant brain tumors with removable high-activity iodine-125 sources. J Neurosurg. 60:61–68. 1984. View Article : Google Scholar : PubMed/NCBI

6 

Yung WK, Kyritsis AP, Gleason MJ and Levin VA: Treatment of recurrent malignant gliomas with high-dose 13-cis-retinoic acid. Clin Cancer Res. 2:1931–1935. 1996.PubMed/NCBI

7 

Furnari FB, Fenton T, Bachoo RM, Mukasa A, Stommel JM, Stegh A, Hahn WC, Ligon KL, Louis DN, Brennan C, et al: Malignant astrocytic glioma: Genetics, biology, and paths to treatment. Genes Dev. 21:2683–2710. 2007. View Article : Google Scholar : PubMed/NCBI

8 

Herman JG, Latif F, Weng Y, Lerman MI, Zbar B, Liu S, Samid D, Duan DS, Gnarra JR, Linehan WM, et al: Silencing of the VHL tumor-suppressor gene by DNA methylation in renal carcinoma. Proc Natl Acad Sci USA. 91:9700–9704. 1994. View Article : Google Scholar : PubMed/NCBI

9 

Antequera F and Bird A: Number of CpG islands and genes in human and mouse. Proc Natl Acad Sci USA. 90:11995–11999. 1993. View Article : Google Scholar : PubMed/NCBI

10 

Jones PA and Buckley JD: The role of DNA methylation in cancer. Adv Cancer Res. 54:1–23. 1990. View Article : Google Scholar : PubMed/NCBI

11 

Costello JF, Berger MS, Huang HS and Cavenee WK: Silencing of p16/CDKN2 expression in human gliomas by methylation and chromatin condensation. Cancer Res. 56:2405–2410. 1996.PubMed/NCBI

12 

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

13 

Spiegl-Kreinecker S, Pirker C, Filipits M, Lötsch D, Buchroithner J, Pichler J, Silye R, Weis S, Micksche M, Fischer J and Berger W: O6-Methylguanine DNA methyltransferase protein expression in tumor cells predicts outcome of temozolomide therapy in glioblastoma patients. Neuro Oncol. 12:28–36. 2010. View Article : Google Scholar : PubMed/NCBI

14 

Chen HY, Shao CJ, Chen FR, Kwan AL and Chen ZP: Role of ERCC1 promoter hypermethylation in drug resistance to cisplatin in human gliomas. Int J Cancer. 126:1944–1954. 2010.PubMed/NCBI

15 

Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, et al: NCBI GEO: Archive for functional genomics data sets-10 years on. Nucleic Acids Res. 39:(Database Issue). D1005–D1010. 2011. View Article : Google Scholar : PubMed/NCBI

16 

Crookston NL and Finley AO: Yaimpute: An r package for knn imputation. J Statistical Software. 23:1–16. 2008. View Article : Google Scholar

17 

Bolstad BM and Bolstad MBM: Package ‘preprocessCore’. 2013.

18 

Smyth GK: Limma: Linear models for microarray dataStatistics for Biology and Health: Bioinformatics and Computational Biology Solutions using R and Bioconductor. Gentleman R, Carey V, Huber W, Irizarry R and Dudoit S: Springer; New York: pp. 397–420. 2005, View Article : Google Scholar

19 

Karolchik D, Barber GP, Casper J, Clawson H, Cline MS, Diekhans M, Dreszer TR, Fujita PA, Guruvadoo L, Haeussler M, et al: The UCSC genome browser database: 2014 Update. Nucleic Acids Res. 42:(Database Issue). D764–D770. 2014. View Article : Google Scholar : PubMed/NCBI

20 

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

21 

Hulsegge I, Kommadath A and Smits MA: Globaltest and GOEAST: Two different approaches for Gene Ontology analysis. BMC Proc. 3 Suppl 4:S102009. View Article : Google Scholar : PubMed/NCBI

22 

Ogata H, Goto S, Sato K, Fujibuchi W, Bono H and Kanehisa M: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 27:29–34. 1999. View Article : Google Scholar : PubMed/NCBI

23 

Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC and Lempicki RA: DAVID: Database for annotation, visualization and integrated discovery. Genome Biol. 4:P32003. View Article : Google Scholar : PubMed/NCBI

24 

Li C and Li MC: Package ‘iSubpathway Miner’. 2013.

25 

Chakravarti A, Zhai GG, Zhang M, Malhotra R, Latham DE, Delaney MA, Robe P, Nestler U, Song Q and Loeffler J: Survivin enhances radiation resistance in primary human glioblastoma cells via caspase-independent mechanisms. Oncogene. 23:7494–7506. 2004. View Article : Google Scholar : PubMed/NCBI

26 

Joo A, Shibata H, Ninomiya H, Kawasaki H, Tashiro N and Fukumaki Y: Structure and polymorphisms of the human metabotropic glutamate receptor type 2 gene (GRM2): Analysis of association with schizophrenia. Mol Psychiatry. 6:186–192. 2001. View Article : Google Scholar : PubMed/NCBI

27 

Flor PJ, Lindauer K, Püttner I, Rüegg D, Lukic S, Knöpfel T and Kuhn R: Molecular cloning, functional expression and pharmacological characterization of the human metabotropic glutamate receptor type 2. Eur J Neurosci. 7:622–629. 1995. View Article : Google Scholar : PubMed/NCBI

28 

Meldrum BS: Glutamate as a neurotransmitter in the brain: Review of physiology and pathology. J Nutr. 130 4S Suppl:1007S–1015S. 2000.PubMed/NCBI

29 

Ullian EM, Christopherson KS and Barres BA: Role for glia in synaptogenesis. Glia. 47:209–216. 2004. View Article : Google Scholar : PubMed/NCBI

30 

Bezzi P and Volterra A: A neuron-glia signalling network in the active brain. Curr Opin Neurobiol. 11:387–394. 2001. View Article : Google Scholar : PubMed/NCBI

31 

Giese A and Westphal M: Glioma invasion in the central nervous system. Neurosurgery. 39:235–250. 1996. View Article : Google Scholar : PubMed/NCBI

32 

Longo AM and Penhoet EE: Nerve growth factor in rat glioma cells. Proceedings of the National Academy of Sciences. 71:2347–2349. 1974. View Article : Google Scholar

33 

Kordi-Tamandani DM, Dahmardeh N and Torkamanzehi A: Evaluation of hypermethylation and expression pattern of GMR2, GMR5, GMR8 and GRIA3 in patients with schizophrenia. Gene. 515:163–166. 2013. View Article : Google Scholar : PubMed/NCBI

34 

Arcella A, Carpinelli G, Battaglia G, D'Onofrio M, Santoro F, Ngomba RT, Bruno V, Casolini P, Giangaspero F and Nicoletti F: Pharmacological blockade of group II metabotropic glutamate receptorsreduces the growth of glioma cells in vivo. Neuro Oncol. 7:236–245. 2005. View Article : Google Scholar : PubMed/NCBI

35 

Tilly BC, van Paridon PA, Verlaan I, Wirtz KW, de Laat SW and Moolenaar WH: Inositol phosphate metabolism in bradykinin-stimulated human A431 carcinoma cells. Relationship to calcium signalling. Biochem J. 244:129–135. 1987. View Article : Google Scholar : PubMed/NCBI

36 

Lee E and Yuspa SH: Changes in inositol phosphate metabolism are associated with terminal differentiation and neoplasia in mouse keratinocytes. Carcinogenesis. 12:1651–1658. 1991. View Article : Google Scholar : PubMed/NCBI

37 

Mishra J and Bhalla US: Simulations of inositol phosphate metabolism and its interaction with InsP3-mediated calcium release. Biophys J. 83:1298–1316. 2002. View Article : Google Scholar : PubMed/NCBI

38 

El-Rifai W, Moskaluk CA, Abdrabbo MK, Harper J, Yoshida C, Riggins GJ, Frierson HF Jr and Powell SM: Gastric cancers overexpress S100A calcium-binding proteins. Cancer Res. 62:6823–6826. 2002.PubMed/NCBI

39 

Kato H, Uzawa K, Onda T, Kato Y, Saito K, Nakashima D, Ogawara K, Bukawa H, Yokoe H and Tanzawa H: Down-regulation of 1D-myo-inositol 1,4,5-trisphosphate 3-kinase A protein expression in oral squamous cell carcinoma. Int J Oncol. 28:873–881. 2006.PubMed/NCBI

40 

Windhorst S, Fliegert R, Blechner C, Möllmann K, Hosseini Z, Günther T, Eiben M, Chang L, Lin HY, Fanick W, et al: Inositol 1,4,5-trisphosphate 3-kinase-A is a new cell motility-promoting protein that increases the metastatic potential of tumor cells by two functional activities. J Biol Chem. 285:5541–5554. 2010. View Article : Google Scholar : PubMed/NCBI

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Volume 14 Issue 5

Print ISSN: 1792-1074
Online ISSN:1792-1082

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Spandidos Publications style
Ma K, Cheng Z, Sun L and Li H: Identification of potential therapeutic targets for gliomas by bioinformatics analysis. Oncol Lett 14: 5203-5210, 2017.
APA
Ma, K., Cheng, Z., Sun, L., & Li, H. (2017). Identification of potential therapeutic targets for gliomas by bioinformatics analysis. Oncology Letters, 14, 5203-5210. https://doi.org/10.3892/ol.2017.6850
MLA
Ma, K., Cheng, Z., Sun, L., Li, H."Identification of potential therapeutic targets for gliomas by bioinformatics analysis". Oncology Letters 14.5 (2017): 5203-5210.
Chicago
Ma, K., Cheng, Z., Sun, L., Li, H."Identification of potential therapeutic targets for gliomas by bioinformatics analysis". Oncology Letters 14, no. 5 (2017): 5203-5210. https://doi.org/10.3892/ol.2017.6850