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Genome-wide plasma cell-free DNA methylation profiling in pancreatic cancer patients using MeDIP-seq

ABSTRACT: Genome-wide plasma cell-free DNA methylation profiling in pancreatic cancer patients using MeDIP-seq

PROVIDER: PRJEB40875 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	ERR4695152_1.fastq.gz	Fastqsanger.gz
	ERR4695152_2.fastq.gz	Fastqsanger.gz
	ERR4695153_1.fastq.gz	Fastqsanger.gz
	ERR4695153_2.fastq.gz	Fastqsanger.gz
	ERR4695154_1.fastq.gz	Fastqsanger.gz

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Genome-wide plasma cell-free DNA methylation profiling in pancreatic cancer patients using MeDIP-seq

Project description:We carried out a genome-wide cfDNA methylation profiling study of pancreatic ductal adenocarcinoma (PDAC) patients by Methylated DNA Immunoprecipitation coupled with high-throughput sequencing (MeDIP-seq). Compared with healthy individuals, 775 differentially methylated regions (DMRs) located in promoter regions were identified in PDAC patients with 761 hypermethylated and 14 hypomethylated regions; meanwhile, 761 DMRs in CpG islands (CGIs) were identified in PDAC patients with 734 hypermethylated and 27 hypomethylated regions (p-value < 35 0.0001). 143 hypermethylated DMRs were further selected which were located in promoter regions and completely overlapped with CGIs. A total of 8 probes from 8 genes were found to fairly distinguish PDAC patients from the healthy individuals, including TRIM73, FAM150A, EPB41L3, SIX3, MIR663, MAPT, LOC100128977 and LOC100130148.

2020-10-29 | E-MTAB-9678 | biostudies-arrayexpress

Genome-wide plasma cell-free DNA methylation profiling in lung cancer patients using MeDIP-seq

Project description:As a non-invasive blood testing, the detection of cell-free DNA (cfDNA) methylation in plasma is raising increasing interest due to its diagnostic and biology applications. Although extensively used in cfDNA methylation analysis, bisulfite sequencing is less cost-effective. Through enriching methylated cfDNA fragments with MeDIP followed by deep sequencing, we aimed to characterize cfDNA methylome in cancer patients. In this study, we investigated the cfDNA methylation patterns in lung cancer patients by MeDIP-seq. MEDIPS package was used for the identification of differentially methylated regions (DMRs) between patients and normal ones. Overall, we identified 330 differentially methylated regions (DMRs) in gene promoter regions, 33 hypermethylation and 297 hypomethylation respectively, by comparing lung cancer patients and healthy individuals as controls. The 33 hypermethylation regions represent 32 genes. Some of the genes had been previously reported to be associated with lung cancers, such as GAS7, AQP10, HLF, CHRNA9 and HOPX. Taken together, our study provided an alternative method of cfDNA methylation analysis in lung cancer patients with potential clinical applications.

2019-03-31 | E-MTAB-7163 | biostudies-arrayexpress

Genome-wide plasma cell-free DNA methylation profiling in lung cancer patients using MeDIP-seq

Project description:Genome-wide plasma cell-free DNA methylation profiling in lung cancer patients using MeDIP-seq

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Cell-Free Plasma MicroRNAs that Identify Patients with Glioblastoma

Project description:Cell-Free Plasma MicroRNAs that Identify Patients with Glioblastoma

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Project description:Three separate experiments were carried out using MeDIP-seq and cfMeDIP-seq for methylome analysis. For the first experiment, different starting amounts of HCT116 cell line DNA, sheared to mimic cell-free DNA, were analyzed using MeDIP-seq and cfMeDIP-seq. In the second experiment the limit of detection of cfMeDIP-seq was tested using varying dilutions of colorectal cancer cell line DNA (HCT116) with multiple myeloma cell line DNA (MM1.S). For both cell line DNA samples, the DNA was sheared to mimic cell-free DNA. In the final experiment, we tested the enrichment of human ctDNA using cfMeDIP-seq performed on plasma collected from patient-derived xenografts (PDXs) generated in mice from two colorectal cancer patients.

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Project description:Liquid biopsies provide a means for the profiling of cell-free RNAs secreted by cells throughout the body. Although well-annotated coding and noncoding transcripts in blood are readily detectable and can serve as biomarkers of disease, the overall diagnostic utility of the cell-free transcriptome remains unclear. Here we show that transposable elements and other repeat RNA elements are enriched in the cell-free transcriptome of patients with cancer, and that they serve as signatures for the accurate classification of the disease. We used repeat-element-aware sequencing and analysis technology and single-molecule nanopore sequencing to profile the cell-free transcriptome in plasma from patients with cancer and to examine millions of genomic features comprised of all annotated genes and of repeat elements throughout the genome. By aggregating individual repeat elements to the subfamily level, we found that samples with pancreatic cancer are enriched with specific Alu subfamilies, whereas other cancers exhibit their own characteristic cell-free RNA profile. Our findings show that repetitive RNA sequences are abundant in blood and can be used as disease-specific diagnostic biomarkers.

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Subtyping of Small Cell Lung Cancer using plasma cell-free nucleosomes

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Project description:Using cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq), we analyzed methylation profiles in 93 plasma samples from 77 pediatric brain tumor patients and 16 non-neoplastic patients. Binomial GLMnet classifiers of tumor and tumor subtypes were built in training sets, and performance was evaluated in test sets. The methylation profiles from plasma cfMeDIP-seq discriminated tumor from non-tumor patients with 0.83 accuracy (precision = 0.93, sensitivity = 0.86, and specificity = 0.67). Among major tumor subtypes vs. nontumors, circumscribed astrocytic glioma showed an accuracy of 0.86 (precision = 0.88, sensitivity = 0.88, and specificity = 0.83), and glioneuronal tumors had an accuracy of 0.83 (precision, sensitivity, and specificity = 0.83). Circumscribed astrocytic glioma and glioneuronal tumors could be discriminated from other tumor subtypes with 0.79 and 0.82 accuracy, respectively.

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