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Tumor hypoxia causes DNA hypermethylation by reducing TET activity (RNA-Seq)

GSE71401 Homo sapiens (human) DNA methylation 6 samples Submitted 2016/09/01 Platform GPL11154
At a glance

DNA methylation · human · 6 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Homo sapiens (human)
Samples
6 samples
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
Hypermethylation of tumor suppressor gene (TSG) promoters confers growth advantages to cancer cells, but how these changes arise is poorly understood. Here, we report that tumor hypoxia reduces the activity of oxygen-dependent TET enzymes, which catalyze DNA de-methylation through 5-methylcytosine oxidation. This occurs independently of hypoxia-associated alterations in TET gene expression, basal metabolism, HIF activity or nuclear reactive oxygen species, but directly depends on oxygen shortage.
Read the full summary ↓
Hypoxia-induced loss of TET activity increases hypermethylation at gene promoters in vitro, while also in patients, gene promoters are markedly more methylated in hypoxic than normoxic tumors. Affected genes are frequently involved in DNA repair, cell cycle regulation, angiogenesis and metastasis, indicating cellular selection of hypermethylation events. Overall, up to 50% of the tumor-associated hypermethylation is ascribable to hypoxia across various cancer types. Accordingly, spontaneous murine breast tumors become hypermethylated when rendered hypoxic through vessel pruning, whereas vessel normalisation rescues this effect. Tumor hypoxia thus acts as a novel regulator underlying DNA methylation.
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE71401_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

What the files usually look like: Look for beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA291093 and SRA study SRP061651. Searching any of these in the dataset finder brings you back here.

Samples in this study

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