|Year : 2015 | Volume
| Issue : 6 | Page : 212-215
Genome-wide methylation profiling reveals new biomarkers for prognosis prediction of glioblastoma
Jiangong Ma1, Xin Hou2, Mingxuan Li1, Hongyu Ren1, Shumin Fang1, Xiaobin Wang1, Cheng He1
1 Department of Neurosurgery, The First Affiliated Hospital of Henan University, Kaifeng 475000, Henan Province, PR, China
2 Department of Oncology, The First Affiliated Hospital of Henan University, Kaifeng 475000, Henan Province, PR, China
|Date of Web Publication||26-Oct-2015|
Department of Neurosurgery, The First Affiliated Hospital of Henan University, Kaifeng 475000, Henan Province
Source of Support: None, Conflict of Interest: None
Objective: To identify a specific hypermethylated molecular biomarker for human malignant glioblastoma prognosis.
Materials and Methods: Genome-wide methylation profiling was performed on 33 tumors and 3 normal glioblastoma samples using the Infinium HumanMethylation450 microarray. Cluster analysis was carried out in these samples according to the differentiated methylated genes. DNA methylation of selected significant candidates was subsequently validated to analyze the association of methylation status of these genes with overall survival as well as gene expression.
Results: We found 217 hypermethylated CpG sites located in 210 respective genes with significant differences in short- and long-term survival (STS and LTS) samples (P < 0.01). Cluster analysis revealed fine clustering of genes with LTS and STS. Of these, we selected 10 most hypermethylated genes, including IL11, RRAD, MS4A6A, SNAPC2, ALDH1A3, ADCY1, MMS19L, NDUFB8, POMC, and THSD4, to perform cluster analysis. It came out with the same fine classification and with survival time of these patients. The top ranking genes were further examined to compare their methylation status with the overall survival rate of patients, as well as with gene expression levels.
Conclusion: We obtained a featured global profiling of DNA methylation in glioblastoma. Our findings strongly indicate that epigenetic silencing of IL11, RRAD, MS4A6A, SNAPC2, and ALDH1A3 are common events in glioblastoma which could be used as novel biomarkers for the prognosis of glioblastoma.
Keywords: Epigenetic regulation, glioblastoma, global methylation, prognosis
|How to cite this article:|
Ma J, Hou X, Li M, Ren H, Fang S, Wang X, He C. Genome-wide methylation profiling reveals new biomarkers for prognosis prediction of glioblastoma. J Can Res Ther 2015;11, Suppl S2:212-5
|How to cite this URL:|
Ma J, Hou X, Li M, Ren H, Fang S, Wang X, He C. Genome-wide methylation profiling reveals new biomarkers for prognosis prediction of glioblastoma. J Can Res Ther [serial online] 2015 [cited 2022 Aug 19];11, Suppl S2:212-5. Available from: https://www.cancerjournal.net/text.asp?2015/11/6/212/168188
| > Introduction|| |
Glioma constitutes approximately 70% of all primary tumors that developed in the CNS. In particular, the most frequent (65%) and most common lethal subtype of glioma is the glioblastoma, also named glioblastoma multiforme (GBM). It is classified as WHO grade IV, and characterized as infiltrative and aggressive, with a median survival of 14 months. In the past two decades, despite the molecular and genetic mechanisms, pathways of GBM have been extensively studied, the precise mechanism of GBM development and progression is still not clear, and the prognosis remains, unfortunately, poor., Hence, the future reliable identification of biomarkers is important for providing new insights into GBM carcinogenesis as well as improving the prognosis of clinical GBM patients.
Methylation of cytosine residues in CpG dinucleotides catalyzed by DNA methyltransferases is the predominant epigenetic gene expression modification manner in mammals. Aberrant DNA hypermethylation has been reported to be a major mechanism for the inactivation of specific tumor suppressors. Next-generation sequencing as a powerful tool that facilitates a genome-wide characterizing of DNA methylation, has been widely applied to investigate the roles of tumor-specific differential methylation patterns in various cancers, which further providing informative biomarkers for both cancer diagnosis and prognosis.,,,,
In the present study, we described a genome-wide survival-associated methylation map of GBM by interrogating 33 GBM tissues. Our findings newly identified 217 hypermethylated CpG sites located in 210 genes with significant differences between short- and long-term survival samples. Of these, we validated IL11, RRAD, MS4A6A, SNAPC2, and ALDH1A3 as being genes that are hypermethylated and silenced in GBM patients and associated with short-term survival (STS). Thus, they could be further exploited as novel biomarkers for the prognosis and prediction of GBM.
| > Materials and Methods|| |
Clinical samples and patients
Tissue samples of GBM were harvested during surgery from 60 patients at The First Affiliated Hospital of Henan University from 2012 to 2015. All tissues, consisted of 39 long-term survival (LTS) and 21 STS, were quickly frozen in liquid nitrogen after the surgery and stored at −80°C. Written informed consent was received in advance for all the patients selected in this paper.
DNA methylation analysis
The DNA methylation status of more than 450,000 CpG sites was interrogated using Illumina Infinium HumanMethylation450 microarray. The microarray was scanned by Illumina BeadArray Reader and the data were analyzed by BeadStudio.
Hierarchical clustering was performed using the methylation level of the 210 gene regions, which contains 217 most variable CpG sites (P < 0.01). The distance matrix was calculated using Pearson correlation and average linkage was applied.
Real-time polymerase chain reaction analysis
RNAs were extracted by Trizol from all the 60 tissues of GBM patients. Real-time polymerase chain reactions (PCRs) were performed using an ABI7900 real-time PCR system (Applied Biosystems, Carlsbad, CA) and the SYBR Premix Ex Taq reagent kit (Takara Bio Inc., Shiga, Japan) using Ct quantization method.
All the statistical analyses were performed using the SAS version 9.2 (http://www.sas.com/en_us/home.html). The κ2 test was performed by analyzing the different gene methylation level between STS and LTS tissues. The t-test was performed in analyzing the box plot of differential gene expression between STS and LTS tissues. P < 0.05 was considered as statistically significant.
| > Results|| |
Classification of glioblastoma multiforme using global DNA methylation status
At the onset of our study, the methylation profiles of 20 STS and 13 LTS GBM samples were analyzed using Illumina Infinium HumanMethylation450 microarray. Microarray analysis identified a total of 1421 CpG sites with a significant difference at methylation status, between STS and LTS groups (P < 0.05). Of these CpG sites, 217 hypermethylated CpG sites with a P value minimum than 0.01 were located to 210 different genes. Further hierarchical cluster analysis revealed fine classification of these genes with LTS and STS [Figure 1].
|Figure 1: Hierarchical clustering of the methylation level of the 210 relevant genes which contains 217 most variable CpG methylation regions in primary glioblastoma multiforme. Tissue types (short-term survival, n = 20; long-term survival, n = 13). Green represented nonmethylation, value = 0; red represented methylation, value = 1|
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Validation of hypermethylated genes in glioblastoma multiforme samples reveals survival-related features
We next chose the top 10 most hypermethylated genes, including IL11, RRAD, MS4A6A, SNAPC2, ALDH1A3, ADCY1, MMS19L, NDUFB8, POMC, and THSD4 genes, to perform the same cluster analysis as described above. The results presented in [Figure 2] also revealed good clustering according to short or long survival time. Besides, the correlation of methylated status of these genes with different survival time (STS and LTS) was further analyzed in an expanding scale of GBM samples, containing 39 STS and 21 LTS tissues, by using κ2 test. As shown in [Table 1], excluding NDUFB8 and POMC, the methylated status of other eight selected genes showed positive and notable correlation with short survival (P < 0.05, P < 0.01, and P < 0.001). These findings suggest the forecasting ability of these hypermethylated genes in predicting GBM patient survival. In detail, hypermethylation of these genes could indicate short or poor survival of GBM patients.
|Figure 2: Hierarchical clustering of the most variable methylated 10 genes in primary glioblastoma multiforme. Tissue types (blue bar: Short-term survival, n = 20; brown bar: Long-term survival, n = 13). Blue color represents low methylated level; red color represents high methylated level|
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|Table 1: Relationship between methylation status of 10 genes and different survival time (STS and LTS) in a scale of 60 cases of GBM samples|
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Comparison between DNA methylation and gene expression
Finally, we examined the expression of the top five hypermethylated genes in both STS and LTS samples (a total of 60 cases) by real-time PCR. As shown in [Figure 3], these genes presented a consistent downregulation in STS samples, demonstrating that the methylation modulation might be the dominant mechanism which is mediating their decrease. Therefore, our global methylation profiling of GBM tissues might identify a series of novel tumor suppressor, whose hypermethylation or downregulation could be utilized for GBM prognosis.
|Figure 3: Box plot analysis of 5 candidate genes expression level – IL11, RRAD, MS4A6A, SNAPC2, and ALDH1A3 in short-term survival (n = 39) and long-term survival (n = 21) glioblastoma multiforme samples. Short-term survival tissue is indicated by a black box and long-term survival tissue is indicated by a red box. Values are displayed as the absolute value of each gene. The t-test was performed in analyzing the expression differences between short-term survival and long-term survival tissues. (*** P < 0.001)|
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| > Discussion|| |
The precise mechanisms guiding GBM progression are still lacking. DNA methylation is a recently, being intensively studied DNA modulation pattern that governing gene expression in mammals. To clarify the potential contribution of altered DNA methylation in the development and/or progression of GBM, we performed genome-wide methylation profiling in 33 cases of GBM samples. A total of 217 hypermethylated CpG sites corresponding to 210 genes were newly identified with significant differences between STS and LTS samples. DNA methylation status of the top ranking genes was then compared to clinicopathological survival time and gene expression. Our results suggested that epigenetic silencing of IL11, RRAD, MS4A6A, SNAPC2, and ALDH1A3 is common in GBM that could be used as novel tumor suppressor and biomarker for prognosis of GBM.
Tumor suppressors are inactivated more frequently by promoter methylation than by mutation in human cancers. Recent studies have indicated a prognostic role for genome-wide methylation in glioma., Our data further support the opinions of this respect. Identification of genes inactivated by DNA methylation is a powerful approach to discover novel tumor suppressors. Nevertheless, the biological functions of these newly identified genes in our study have not been clearly demonstrated. In future, more attention should be paid on the pathways of these candidates having a part in, for better understanding their roles in GBM tumorigenesis, and aiding prognosis prediction or potentially guiding the treatment for GBM patients.
Financial support and sponsorship
This study is supported by Henan Provincial Education Department Fund Project (NO.14A320072).
Conflicts of interest
There are no conflicts of interest.
| > References|| |
Ohgaki H, Kleihues P. Epidemiology and etiology of gliomas. Acta Neuropathol 2005;109:93-108.
Adamson C, Kanu OO, Mehta AI, Di C, Lin N, Mattox AK, et al.
Glioblastoma multiforme: A review of where we have been and where we are going. Expert Opin Investig Drugs 2009;18:1061-83.
Saran F. Recent advances in paediatric neuro-oncology. Curr Opin Neurol 2002;15:671-7.
Kumthekar PU, Macrie BD, Singh SK, Kaur G, Chandler JP, Sejpal SV. A review of management strategies of malignant gliomas in the elderly population. Am J Cancer Res 2014;4:436-44.
Subramaniam D, Thombre R, Dhar A, Anant S. DNA methyltransferases: A novel target for prevention and therapy. Front Oncol 2014;4:80.
Chen H, Zhang T, Sheng Y, Zhang C, Peng Y, Wang X, et al.
Methylation profiling of multiple tumor suppressor genes in hepatocellular carcinoma and the epigenetic mechanism of 3OST2 regulation. J Cancer 2015;6:740-9.
Choudhury JH, Ghosh SK. Promoter hypermethylation profiling identifies subtypes of head and neck cancer with distinct viral, environmental, genetic and survival characteristics. PLoS One 2015;10:e0129808.
Kok-Sin T, Mokhtar NM, Ali Hassan NZ, Sagap I, Mohamed Rose I, Harun R, et al.
Identification of diagnostic markers in colorectal cancer via integrative epigenomics and genomics data. Oncol Rep 2015;34:22-32.
Kuasne H, Cólus IM, Busso AF, Hernandez-Vargas H, Barros-Filho MC, Marchi FA, et al.
Genome-wide methylation and transcriptome analysis in penile carcinoma: Uncovering new molecular markers. Clin Epigenetics 2015;7:46.
Aine M, Sjödahl G, Eriksson P, Veerla S, Lindgren D, Ringnér M, et al.
Integrative epigenomic analysis of differential DNA methylation in urothelial carcinoma. Genome Med 2015;7:23.
Lai RK, Chen Y, Guan X, Nousome D, Sharma C, Canoll P, et al.
Genome-wide methylation analyses in glioblastoma multiforme. PLoS One 2014;9:e89376.
Collins VP, Ichimura K, Di Y, Pearson D, Chan R, Thompson LC, et al.
Prognostic and predictive markers in recurrent high grade glioma; results from the BR12 randomised trial. Acta Neuropathol Commun 2014;2:68.
[Figure 1], [Figure 2], [Figure 3]
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