Dataset Information

Genome-wide methylome analysis using MethylCap-seq uncovers 4 hypermethylated markers with high sensitivity for both adeno- and squamous-cell cervical carcinoma.

ABSTRACT:

Background

Cytology-based screening methods for cervical adenocarcinoma (ADC) and to a lesser extent squamous-cell carcinoma (SCC) suffer from low sensitivity. DNA hypermethylation analysis in cervical scrapings may improve detection of SCC, but few methylation markers have been described for ADC. We aimed to identify novel methylation markers for the early detection of both ADC and SCC.

Results

Genome-wide methylation profiling for 20 normal cervices, 6 ADC and 6 SCC using MethylCap-seq yielded 53 candidate regions hypermethylated in both ADC and SCC. Verification and independent validation of the 15 most significant regions revealed 5 markers with differential methylation between 17 normals and 13 cancers. Quantitative methylation-specific PCR on cervical cancer scrapings resulted in detection rates ranging between 80% and 92% while between 94% and 99% of control scrapings tested negative. Four markers (SLC6A5, SOX1, SOX14 and TBX20) detected ADC and SCC with similar sensitivity. In scrapings from women referred with an abnormal smear (n=229), CIN3+ sensitivity was between 36% and 71%, while between 71% and 93% of adenocarcinoma in situ (AdCIS) were detected; and CIN0/1 specificity was between 88% and 98%. Compared to hrHPV, the combination SOX1/SOX14 showed a similar CIN3+ sensitivity (80% vs. 75%, respectively, P>0.2), while specificity improved (42% vs. 84%, respectively, P < 10-5).

Conclusion

SOX1 and SOX14 are methylation biomarkers applicable for screening of all cervical cancer types.

SUBMITTER: Wang R

PROVIDER: S-EPMC5348351 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Publications

Genome-wide methylome analysis using MethylCap-seq uncovers 4 hypermethylated markers with high sensitivity for both adeno- and squamous-cell cervical carcinoma.

Wang Rong R van Leeuwen Robert W RW Boers Aniek A Klip Harry G HG de Meyer Tim T Steenbergen Renske D M RD van Criekinge Wim W van der Zee Ate G J AG Schuuring Ed E Wisman G Bea A GB

Oncotarget 20161201 49

<h4>Background</h4>Cytology-based screening methods for cervical adenocarcinoma (ADC) and to a lesser extent squamous-cell carcinoma (SCC) suffer from low sensitivity. DNA hypermethylation analysis in cervical scrapings may improve detection of SCC, but few methylation markers have been described for ADC. We aimed to identify novel methylation markers for the early detection of both ADC and SCC.<h4>Results</h4>Genome-wide methylation profiling for 20 normal cervices, 6 ADC and 6 SCC using Methyl ...[more]

PMID: 27738327

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Project description:BACKGROUND:The KEAP1/NRF2 (Kelch-like ECH-associated protein 1/nuclear factor erythroid 2-related factor 2) pathway modulates detoxification processes and participates in the resistance of solid tumors to therapy. Scientific evidence about the presence of genetic and epigenetic abnormalities of the KEAP1 gene was firstly reported in non-small-cell lung cancer (NSCLC) and then described in other tumors. At present, the prognostic role of aberrant methylation at cytosine-guanine dinucleotide (CpG) sites of the KEAP1 gene promoter is debated in NSCLC, and its correlation with transcriptional changes and protein levels remains to be defined in large sample cohorts. METHODS:We evaluated and compared multiple KEAP1 omics data (methylation, transcript, and protein expression levels) from The Cancer Genome Atlas (TCGA) to explore the role of CpGs located in different portions of KEAP1 and the correlation between methylation, transcription, and protein levels. Data from two subsets of lung adenocarcinoma (LUAD, n = 617) and lung squamous cell carcinoma (LUSC, n = 571) cohorts of NSCLC patients with different disease stages were evaluated. RESULTS:We found that the methylation levels of many KEAP1 CpGs at various promoter and intragenic locations showed a significant inverse correlation with the transcript levels. Interestingly, these results were limited to the KRAS wild-type LUSC and LUAD cohorts, whereas in LUAD the effect of the epigenetic silencing of KEAP1 on its transcription was also observed in the EGFR mutated subpopulation. CONCLUSIONS:These results support the idea that the prognostic role of KEAP1 CpG sites warrants more in-depth investigation and that the impact of their changes in methylation levels may differ among specific NSCLC histologies and molecular backgrounds. Moreover, the observed impact of epigenetic silencing on KEAP1 expression in specific KRAS and EGFR settings may suggest a potential role of KEAP1 methylation as a predictive marker for NSCLC patients for whom anti-EGFR treatments are considered.

Project description:Understanding the role of DNA methylation often requires accurate assessment and comparison of these modifications in a genome-wide fashion. Sequencing-based DNA methylation profiling provides an unprecedented opportunity to map and compare complete DNA CpG methylomes. These include whole genome bisulfite sequencing (WGBS), Reduced-Representation Bisulfite-Sequencing (RRBS), and enrichment-based methods such as MeDIP-seq, MBD-seq, and MRE-seq. An investigator needs a method that is flexible with the quantity of input DNA, provides the appropriate balance among genomic CpG coverage, resolution, quantitative accuracy, and cost, and comes with robust bioinformatics software for analyzing the data. In this chapter, we describe four protocols that combine state-of-the-art experimental strategies with state-of-the-art computational algorithms to achieve this goal. We first introduce two experimental methods that are complementary to each other. MeDIP-seq, or methylation-dependent immunoprecipitation followed by sequencing, uses an anti-methylcytidine antibody to enrich for methylated DNA fragments, and uses massively parallel sequencing to reveal identity of enriched DNA. MRE-seq, or methylation-sensitive restriction enzyme digestion followed by sequencing, relies on a collection of restriction enzymes that recognize CpG containing sequence motifs, but only cut when the CpG is unmethylated. Digested DNA fragments enrich for unmethylated CpGs at their ends, and these CpGs are revealed by massively parallel sequencing. The two computational methods both implement advanced statistical algorithms that integrate MeDIP-seq and MRE-seq data. M&M is a statistical framework to detect differentially methylated regions between two samples. methylCRF is a machine learning framework that predicts CpG methylation levels at single CpG resolution, thus raising the resolution and coverage of MeDIP-seq and MRE-seq to a comparable level of WGBS, but only incurring a cost of less than 5% of WGBS. Together these methods form an effective, robust, and affordable platform for the investigation of genome-wide DNA methylation.

Dataset Information

Genome-wide methylome analysis using MethylCap-seq uncovers 4 hypermethylated markers with high sensitivity for both adeno- and squamous-cell cervical carcinoma.

Background

Results

Conclusion

Publications

Genome-wide methylome analysis using MethylCap-seq uncovers 4 hypermethylated markers with high sensitivity for both adeno- and squamous-cell cervical carcinoma.

Similar Datasets

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