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High-throughput assessment of CpG site methylation for distinguishing between HCV-cirrhosis and HCV-associated hepatocellular carcinoma.

ABSTRACT: Methylation of promoter CpG islands has been associated with gene silencing and demonstrated to lead to chromosomal instability. Therefore, some postulate that aberrantly methylated CpG regions may be important biomarkers indicative of cancer development. In this study we used the Illumina GoldenGate Methylation BeadArray Cancer Panel I for simultaneously profiling methylation of 1,505 CpG sites in order to identify methylation differences in 76 liver tissues ranging from normal to pre-neoplastic and neoplastic states. CpG sites for ESR1, GSTM2, and MME were significantly differentially methylated when comparing the pre-neoplastic tissues from patients with concomitant hepatocellular carcinoma (HCC) to the pre-neoplastic tissues from patients without HCC. When comparing paired HCC tissues to their corresponding pre-neoplastic non-tumorous tissues, eight CpG sites, including one CpG site that was hypermethylated (APC) and seven (NOTCH4, EMR3, HDAC9, DCL1, HLA-DOA, HLA-DPA1, and ERN1) that were hypomethylated in HCC, were identified. Our study demonstrates that high-throughput methylation technologies may be used to identify differentially methylated CpG sites that may prove to be important molecular events involved in carcinogenesis.

SUBMITTER: Archer KJ

PROVIDER: S-EPMC2898187 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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High-throughput assessment of CpG site methylation for distinguishing between HCV-cirrhosis and HCV-associated hepatocellular carcinoma.

Archer Kellie J KJ Mas Valeria R VR Maluf Daniel G DG Fisher Robert A RA

Molecular genetics and genomics : MGG 20100218 4

Methylation of promoter CpG islands has been associated with gene silencing and demonstrated to lead to chromosomal instability. Therefore, some postulate that aberrantly methylated CpG regions may be important biomarkers indicative of cancer development. In this study we used the Illumina GoldenGate Methylation BeadArray Cancer Panel I for simultaneously profiling methylation of 1,505 CpG sites in order to identify methylation differences in 76 liver tissues ranging from normal to pre-neoplasti ...[more]

PMID: 20165882

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Project description:Background & aimsHepatocellular carcinoma (HCC) prediction models can inform clinical decisions about HCC screening provided their predictions are robust. We conducted an external validation of 6 HCC prediction models for UK patients with cirrhosis and a HCV virological cure.MethodsPatients with cirrhosis and cured HCV were identified from the Scotland HCV clinical database (N = 2,139) and the STratified medicine to Optimise Treatment of Hepatitis C Virus (STOP-HCV) study (N = 606). We calculated patient values for 4 competing non-genetic HCC prediction models, plus 2 genetic models (for the STOP-HCV cohort only). Follow-up began at the date of sustained virological response (SVR) achievement. HCC diagnoses were identified through linkage to nation-wide cancer, hospitalisation, and mortality registries. We compared discrimination and calibration measures between prediction models.ResultsMean follow-up was 3.4-3.9 years, with 118 (Scotland) and 40 (STOP-HCV) incident HCCs observed. The age-male sex-ALBI-platelet count score (aMAP) model showed the best discrimination; for example, the Concordance index (C-index) in the Scottish cohort was 0.77 (95% CI 0.73-0.81). However, for all models, discrimination varied by cohort (being better for the Scottish cohort) and by age (being better for younger patients). In addition, genetic models performed better in patients with HCV genotype 3. The observed 3-year HCC risk was 3.3% (95% CI 2.6-4.2) and 5.1% (3.5-7.0%) in the Scottish and STOP-HCV cohorts, respectively. These were most closely matched by aMAP, in which the mean predicted 3-year risk was 3.6% and 5.0% in the Scottish and STOP-HCV cohorts, respectively.ConclusionsaMAP was the best-performing model in terms of both discrimination and calibration and, therefore, should be used as a benchmark for rival models to surpass. This study underlines the opportunity for 'real-world' risk stratification in patients with cirrhosis and cured HCV. However, auxiliary research is needed to help translate an HCC risk prediction into an HCC-screening decision.Lay summaryPatients with cirrhosis and cured HCV are at high risk of developing liver cancer, although the risk varies substantially from one patient to the next. Risk calculator tools can alert clinicians to patients at high risk and thereby influence decision-making. In this study, we tested the performance of 6 risk calculators in more than 2,500 patients with cirrhosis and cured HCV. We show that some risk calculators are considerably better than others. Overall, we found that the 'aMAP' calculator worked the best, but more work is needed to convert predictions into clinical decisions.

Dataset Information

High-throughput assessment of CpG site methylation for distinguishing between HCV-cirrhosis and HCV-associated hepatocellular carcinoma.

Publications

High-throughput assessment of CpG site methylation for distinguishing between HCV-cirrhosis and HCV-associated hepatocellular carcinoma.

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