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Effect of SHP2 Inhibition on mRNA Expression Profiles of GIST Cells

GSE282434 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2024/11/30 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 18 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
Homo sapiens (human)
Topic
Gastrointestinal stromal tumourThis series is part of our curated briefing on it.
Samples
18 samples: 18 samples, each with a distinct title — no replicated groupsGroups 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.
Paper
SHP2 inhibition and adjuvant therapy synergistically target KIT-mutant GISTs via ERK1/2-regulated GSK3β/cyclin D1 pathwayClinical and translational medicine · 2025
Summary (written by the submitters)
Secondary mutation of KIT/PDGFRA is the main reason that contributes to the multi-drug resistance phenotype of advanced GIST in the clinical setting. SHP2 is a specific signal transducer of KIT. Therefore, the combination of approved KIT/PDGFRA kinase inhibitors and SHP2 inhibitors is a promising treatment strategy for advanced GIST patients. Here, we treated GIST cells with the SHP2 inhibitor SHP099 and sequenced the total mRNA to analyze the effect of SHP2 inhibition on GIST cells.
Published in
SHP2 inhibition and adjuvant therapy synergistically target KIT-mutant GISTs via ERK1/2-regulated GSK3β/cyclin D1 pathway
He C, Yu J, Mao S et al. · Clinical and translational medicine 2025 · PMID 39981588 · doi:10.1002/ctm2.70231
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 GSE282434_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 PRJNA1188572 and SRA study SRP546510. Searching any of these in the dataset finder brings you back here.

Study design
18 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 9 samples read as cases, 9 as controls.

How we read this: Read from 18 sample titles: 18 distinct titles with little repetition. 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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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.