Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Genome-wide DNA methylation profiles of non-small cell lung carcinomas and control lung tissues

ABSTRACT: Purpose: Generate genome-wide methylation profiles of non-small cell lung carcinomas (NSCLC) and their matching lung tissues for detection of hypermethylated and hypomethylated regions present in the tumors. Methods: MethylCapture followed by next-generation sequencing (Illumina GAIIx) of 7 nsclc tumor samples and paired lung tissues in replicated, plus one cell line, 2 fully artificially methylated and 2 fully artificially unmethylated controls. Normalization of methylation reads based on CpG coupling factor–method. Relative methylation scores (rms) in 500bp non-overlapping windows. 90th percentile of rpm (reads per million) values for all 500bp genome-wide windows, with rpm <1.33 were considered. Distributions of 10bp bins rms values within each 500bp genomic region were compared using both one-sided Student’s t-test and one-sided Wilcoxon rank-sum test. Testing was done separately for hypo- and hypermethylation and p-value threshold of 10-18. Results: MethylCap-seq data revealed strong positive correlation between replicate experiments and between paired tumor/lung samples. 14472 differentially methylated regions (DMR) with non-overlapping 500 bp windows were found. 57 DMRs were present in all NSCLC tumors. 287 were unique for squamous-cell carcinomas and 26 unique for adenocarcinomas. While hypomethylated DMRs did not correlate to any particular functional category of genes, the hypermethylated DMRs were strongly associated with genes encoding transcriptional regulators. Furthermore, subtelomeric regions and satellite repeats were hypomethylated in the NSCLC samples. Conclusion: We provide a resource containing genome-wide DNA methylation maps of NSCLC and their paired lung tissues, and comprehensive lists of known and novel DMRs and associated genes in NSCLC. The DMRs can be in further studies to develop sensitive biological markers for NSCLC, which may enable non-invasive diagnosis and early detection of the disease, and potentially allow histological classification. MethylCap-seq of 7 nsclc tumor samples and paired lung tissues, plus 2 fully methylated and 2 fully unmethylated controls.

ORGANISM(S): Homo sapiens

SUBMITTER: Sjaak Philipsen

PROVIDER: E-GEOD-41343 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Genome-wide DNA methylation profiling of non-small cell lung carcinomas.

Carvalho Rejane Hughes RH Haberle Vanja V Hou Jun J van Gent Teus T Thongjuea Supat S van Ijcken Wilfred W Kockx Christel C Brouwer Rutger R Rijkers Erikjan E Sieuwerts Anieta A Foekens John J van Vroonhoven Mirjam M Aerts Joachim J Grosveld Frank F Lenhard Boris B Philipsen Sjaak S

Epigenetics & chromatin 20120622 1

<h4>Background</h4>Non-small cell lung carcinoma (NSCLC) is a complex malignancy that owing to its heterogeneity and poor prognosis poses many challenges to diagnosis, prognosis and patient treatment. DNA methylation is an important mechanism of epigenetic regulation involved in normal development and cancer. It is a very stable and specific modification and therefore in principle a very suitable marker for epigenetic phenotyping of tumors. Here we present a genome-wide DNA methylation analysis ...[more]

PMID: 22726460

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Project description:Purpose: Generate genome-wide methylation profiles of non-small cell lung carcinomas (NSCLC) and their matching lung tissues for detection of hypermethylated and hypomethylated regions present in the tumors. Methods: MethylCapture followed by next-generation sequencing (Illumina GAIIx) of 7 nsclc tumor samples and paired lung tissues in replicated, plus one cell line, 2 fully artificially methylated and 2 fully artificially unmethylated controls. Normalization of methylation reads based on CpG coupling factor–method. Relative methylation scores (rms) in 500bp non-overlapping windows. 90th percentile of rpm (reads per million) values for all 500bp genome-wide windows, with rpm <1.33 were considered. Distributions of 10bp bins rms values within each 500bp genomic region were compared using both one-sided Student’s t-test and one-sided Wilcoxon rank-sum test. Testing was done separately for hypo- and hypermethylation and p-value threshold of 10-18. Results: MethylCap-seq data revealed strong positive correlation between replicate experiments and between paired tumor/lung samples. 14472 differentially methylated regions (DMR) with non-overlapping 500 bp windows were found. 57 DMRs were present in all NSCLC tumors. 287 were unique for squamous-cell carcinomas and 26 unique for adenocarcinomas. While hypomethylated DMRs did not correlate to any particular functional category of genes, the hypermethylated DMRs were strongly associated with genes encoding transcriptional regulators. Furthermore, subtelomeric regions and satellite repeats were hypomethylated in the NSCLC samples. Conclusion: We provide a resource containing genome-wide DNA methylation maps of NSCLC and their paired lung tissues, and comprehensive lists of known and novel DMRs and associated genes in NSCLC. The DMRs can be in further studies to develop sensitive biological markers for NSCLC, which may enable non-invasive diagnosis and early detection of the disease, and potentially allow histological classification.

Project description:Purpose: To investigate the role of DNA methylation in human uterine leiomyoma tumorigenesis, we profiled genome-wide DNA methylation patterns of leiomyoma cells (LM) and adjacent normal myometrial cells (MM) treated with or without DNA methylation inhibitor 5-Aza. Methods: Primary cells were isolated from fresh uterine myometrium or leiomyoma tissue. Cells were treated with vehicle (DMSO) or 5 µM 5’-aza for 96h with medium refreshed every 24h. Genomic DNA was extracted from LM and MM cells using DNeasy Blood and Tissue Kit (Qiagen, 69504) and fragmented to 300-500bp using Covaris M220. Methylated DNA fragments were captured using the MethylCap Kit (Diagenode, C02020010). Next-generation sequencing libraries were prepared using the KAPA Hyper Prep Kit (KAPA Biosystems, KK8502) and KAPA Single-Indexed Adapter Kit (KAPA Biosystems, KK8710). Libraries were sequenced under 75bp paired-end sequencing format. Sequences were aligned to the hg19 reference genome using Bowtie2. Histone style peak calling, differential binding analysis (getDifferentialPeaks) and motif analysis were performed using Homer under default settings. Differentily methylated regions (DMRs) were detected with a fold enrichment over background tag count ≥4 and poisson enrichment p-value over background tag count <0.0001. Differential DNA methylation between MM and LM (with or without treatment) at select regions were validated through qPCR. Results: More than 100,000 methyalted regions were detected in each sample. We discovered 21,086 DMRs between MM and LM cells under basal (vehicle treated) condition. In cells treated with 5-aza, we detected 8811 DMRs between MM and LM. We compared the genomic context of DMRs identified by MethylCap-Seq with the probe distribution of the HM450 array and detected large differences between these two methods. For example, 44.23% of DMR were located in intergenic regions by MethylCap-Seq, which was drastically higher than the percentage of HM450 array probes located in intergenic regions Downstream network enrichment analysis and motif analysis indicated that DMR-associated genes and regions are crucial for the tumor progression and hormone responsiveness of LM. Conclusions: Our study represents the detailed analysis of differential DNA methyaltion profiles between MM and LM using MethylCap-seq. Our results showed a more comprehensive evaluation of DNA methyaltion landscapes of MM and LM compared with previous array based analysis. We conclude that DNA methylation plays a crucial role in hormone-dependent LM tumorigenesis.

Dataset Information

Genome-wide DNA methylation profiles of non-small cell lung carcinomas and control lung tissues

Publications

Genome-wide DNA methylation profiling of non-small cell lung carcinomas.

Similar Datasets

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