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KRAS and YAP1 converge to regulate EMT and tumor survival

GSE56175 Mus musculus (mouse) RNA-seq (gene expression) 9 samples Submitted 2014/07/27 Platform GPL13112
At a glance

RNA-seq (gene expression) · mouse · 9 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
9 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)
Cancer cells that express oncogenic alleles of RAS typically require sustained expression of the mutant allele for survival, but the molecular basis of this oncogene dependency remains incompletely understood. To identify genes that can functionally substitute for oncogenic RAS, we systematically expressed 15,294 open reading frames in a human KRAS-dependent colon cancer cell line engineered to express an inducible KRAS-specific shRNA.
Read the full summary ↓
We found 147 genes that promoted survival in the setting of KRAS suppression. In this model, the transcriptional co-activator YAP1 rescued cell viability in KRAS-dependent cells upon suppression of KRAS and was required for KRAS-induced cell transformation. Acquired resistance to Kras suppression in a Kras-driven murine lung cancer model also involved increased YAP1 signaling. KRAS and YAP1 converge on the transcription factor FOS and activate a transcriptional program involved in regulating the epithelial-mesenchymal transition (EMT). Together, these findings implicate transcriptional regulation of EMT by YAP1 as a significant component of oncogenic RAS signaling
This dataset
Open GSE56175 in finder → Find more mouse RNA-seq datasets →
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.

Series matrix ↓
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 GSE56175_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

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 PRJNA242635 and SRA study SRP040553. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 24954536) ↗
Samples in this study

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+ 9 more — browse all 9 samples with per-sample file links →

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