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Differentiation of Mammary Tumors and Reduction in Metastasis Upon Malat1 LncRNA Loss

GSE67647 Mus musculus (mouse) RNA-seq (gene expression) 26 samples Submitted 2016/01/12 Platform GPL13112
At a glance

RNA-seq (gene expression) · mouse · 26 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
26 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)
Genome-wide analyses have identified thousands of long non-coding RNAs (lncRNAs). Malat1 (Metastasis Associated Lung Adenocarcinoma Transcript 1) is among the most abundant lncRNAs whose expression is altered in numerous cancers. Here we report that genomic loss, as well as systemic knockdown of Malat1 using antisense oligonucleotides, in the MMTV-PyMT mouse mammary carcinoma model results in slower tumor growth accompanied by differentiation into highly cystic tumors and a significant reduction in lung metastasis.
Read the full summary ↓
Further, Malat1 loss results in a reduction of branching morphogenesis in MMTV-PyMT and Her2/neu amplified tumor organoids consistent with the in vivo reduction in lung metastasis. At the molecular level, Malat1 knockdown results in alterations in gene expression and changes in splicing patterns of genes involved in differentiation and pro-tumorigenic signaling pathways. Together, these data indicate that the lncRNA Malat1 regulates critical processes in mammary cancer pathogenesis and represents a promising therapeutic target for inhibiting breast cancer metastasis.
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 GSE67647_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA280534 and SRA study SRP056933. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 26 more — browse all 26 samples with per-sample file links →

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