J Korean Med Sci.  2012 Jun;27(6):594-604. 10.3346/jkms.2012.27.6.594.

Pharmacological Unmasking Microarray Approach-Based Discovery of Novel DNA Methylation Markers for Hepatocellular Carcinoma

• Jung N
• Won JK
• Kim BH
• Suh KS
• Jang JJ
• Kang GH

• KMID: 1421610
• DOI: http://doi.org/10.3346/jkms.2012.27.6.594

Abstract

DNA methylation is one of the main epigenetic mechanisms and hypermethylation of CpG islands at tumor suppressor genes switches off these genes. To find novel DNA methylation markers in hepatocellular carcinoma (HCC), we performed pharmacological unmasking (treatment with 5-aza-2'-deoxycytidine or trichostatin A) followed by microarray analysis in HCC cell lines. Of the 239 promoter CpG island loci hypermethylated in HCC cell lines (as revealed by methylation-specific PCR), 221 loci were found to be hypermethylated in HCC or nonneoplastic liver tissues. Thirty-three loci showed a 20% higher methylation frequency in tumors than in adjacent nonneoplastic tissues. Correlation of individual cancer-related methylation markers with clinicopathological features of HCC patients (n = 95) revealed that the number of hypermethylated genes in HCC tumors was higher in older than in younger patients. Univariate and multivariate survival analysis revealed that the HIST1H2AE methylation status is closely correlated with the patient's overall survival (P = 0.022 and P = 0.010, respectively). In conclusion, we identified 221 novel DNA methylation markers for HCC. One promising prognostic marker, HIST1H2AE, should be further validated in the prognostication of HCC patients.

Keyword

CpG Islands; DNA Methylation; Carcinoma, Hepatocellular; Microarray; Prognosis

MeSH Terms

Azacitidine/analogs & derivatives/pharmacology
Carcinoma, Hepatocellular/*genetics/mortality
Cell Line, Tumor
CpG Islands
DNA Methylation/*drug effects
Down-Regulation
Female
Hep G2 Cells
Humans
Hydroxamic Acids/pharmacology
Liver/metabolism
Liver Neoplasms/*genetics/mortality
Male
Middle Aged
Oligonucleotide Array Sequence Analysis
Promoter Regions, Genetic
Survival Analysis
Tumor Markers, Biological/*genetics
Hydroxamic Acids
Tumor Markers, Biological
Azacitidine

Figure

• 

  Fig. 1 Flow chart for selection of candidate genes. Screening of candidate tumor suppressor genes (TSGs) was performed in 5 hepatocellular carcinoma (HCC) cell lines treated with 5 µM 5-aza-2'-deoxycytidine (AZA) or 300 nM trichostatin A (TSA) by using a 24,526-oligonucleotide mRNA microarray. We obtained 793 candidates whose gene expression did not increase with TSA treatment (< 1.4-fold) but increased more than 2-fold after AZA treatment. We excluded genes that do not harbor CpG islands in their promoters or whose methylation status in HCC tumors had already been reported in the literature. We further excluded genes for which adequate oligonucleotide primers could not be designed by using the MSPprimer or MethPrimer software programs. As a result, we selected 380 genes to be examined for their methylation status in HCC cell lines by using methylation-specific PCR (MSP).

• 

  Fig. 2 Hypermethylation-dependent expression changes. Gene expression changes for the indicated cells treated with trichostatin A (TSA) (x-axis) or 5-aza-2'-deoxycytidine (AZA) (y-axis) are plotted by log-fold change, and individual genes are shown in circles.

• 

  Fig. 3 Methylation-specific PCR (MSP) analysis of 380 selected genes in 8 hepatocellular carcinoma (HCC) cell lines. The methylated and unmethylated status is indicated by a gray and a white box, respectively.

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  Fig. 4 Frequency of H3K27me3 modification and occupancy rate of SUZ12 and EED in methylated (n = 239) and unmethylated genes (n = 141) in human embryonic stem cells. The chi-squared test was conducted to analyze the significance of the association.

• 

  Fig. 5 Comparison of ALU and LINE-1 repeats between methylated and unmethylated genes. For ALU counting, the promoter sequence of a specific gene was divided into 20 bins of 1-kb sequence each (10 bins upstream and 10 bins downstream of each gene transcription start site), and the presence of ALU was annotated for each bin. We counted bins containing ALU within a 1-kb sequence. For LINE-1 counting, the promoter sequence of a specific gene was divided into 7 bins of 1-kb sequence each (2 bins upstream and 5 bins downstream of each gene transcription site), and the presence of LINE-1 was annotated for each bin. Bins containing LINE-1 within a 1-kb sequence were counted. Student's t-test was performed to determine the statistical significance of the difference of means between 2 groups.

• 

  Fig. 6 Methylation-specific PCR (MSP) analysis of 239 genes in 10 pairs of tumor and surrounding nontumor tissue. The methylated and unmethylated status is indicated by a gray and a white box, respectively. Genes were divided into subgroups based on the methylation pattern in hepatocellular carcinoma (HCC) tumors and surrounding nonneoplastic liver tissues (see main text).

• 

  Fig. 7 Effect of 5-aza-2'-deoxycytidine (AZA) and trichostatin A (TSA) on gene expression. RNA was isolated from hepatocellular carcinoma (HCC) cell lines left untreated or treated with AZA, TSA, or a combination of AZA and TSA. mRNA was reverse-transcribed, and gene expression was quantitated by real-time PCR and normalized to GAPDH expression.

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  Fig. 8 Methylation frequencies of 33 DNA methylation markers in HCC cell lines (n = 8) and tissue samples (10 paired HCC and non-neoplastic liver tissue samples). Methylation frequencies of these DNA methylation markers in HCC tissue samples were higher than those of non-neoplastic liver tissue samples: the differences were 20% or more.

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  Fig. 9 Methylation frequencies of 33 DNA methylation markers in hepatocellular carcinoma (HCC) tissue samples (n = 95). DNA methylation markers were distributed along the x-axis according to the decreasing order of methylation frequency.

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  Fig. 10 Kaplan-Meier survival curves of 90 hepatocellular carcinoma (HCC) patients. Correlation of (A) HIST1H2AE, and (B) EFEMP2 methylation status with overall survival.

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