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Targeting the vulnerability of arachidonic acid metabolism to enhance immunotherapy efficacy in ARID1A-deficient colorectal cancer [RNA-Seq]

GSE278083 Mus musculus (mouse) RNA-seq (gene expression) 16 samples Submitted 2024/11/26 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 16 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
16 samples: 5 × CT26, ARID1AknockoutIgG vs 5 × CT26, ARID1AknockoutPD1 vs 3 × CT26, ARID1Awildtype vs 3 × CT26, ARID1AknockoutGroups are read from the sample names, not from the paper. Check the list below.
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)
ARID1A, a core constituent of SWI/SNF complex, is mutated in about 10% of colorectal cancer (CRC). This study demonstrates a synergistic effect of arachidonic acid metabolism inhibitors with immune checkpoint inhibitors (ICIs) by regulating functionality of CD8+ T cells and vasculogenic mimicry (VM) formation in ARID1A-deficient CRC. Mechanistically, ATAC-seq and ChIP-qPCR demonstrated that the lack of ARID1A results in reduced levels of the key enzymes PTGS1 and PTGS2, which control the arachidonic acid pathway.
Read the full summary ↓
This reduction generated a reliance on the remaining functionality of arachidonic acid pathway in ARID1A-deficient cells.
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 GSE278083_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 PRJNA1165659 and SRA study SRP534943. Searching any of these in the dataset finder brings you back here.

Study design
5 × CT26, ARID1AknockoutIgG vs 5 × CT26, ARID1AknockoutPD1 vs 3 × CT26, ARID1Awildtype vs 3 × CT26, ARID1Aknockout

Supports a case/control comparison: 13 samples read as cases, 3 as controls.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
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