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Mutant KRAS-Driven Selective mRNA Translation Reveals Mechanisms and Therapeutic Vulnerabilities in Cancer

GSE285857 Homo sapiens (human) RNA-seq (gene expression) 48 samples Submitted 2026/06/17 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 48 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)
Samples
48 samples: 48 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.
Summary (written by the submitters)
mRNA translation is highly regulated and rapidly controls the functional proteome in response to physiological cues in health and disease. Mutant KRAS signals to ERKs intersect with the mTOR and EIF4E-EIF4F complex, thereby influencing cap-dependent mRNA translation to promote tumorigenesis. With recent clinical advances in KRAS-targeted therapies, it is important to understand the changes in the mRNA translational landscape and its role in cellular adaptations in response to mutant KRAS-targeted therapies.
Read the full summary ↓
We discover that mutant KRAS controls the translation of a subset of mRNAs and affects the protein production of the translation apparatus. Interestingly, these specific subsets of mRNAs have short, weakly folded 5’UTRs, or harbor low folding energy consensus RNA sequences. This mutant KRAS-dependent mechanism differs from mTOR and EIF4E-EIF4F regulation, which governs cap-dependent translation of mRNAs with highly structured, GC-rich 5' UTRs or TOP motifs. Our findings clarify the unique and indispensable role of mutant KRAS in regulating mRNA translation, distinctly setting it apart from the mTOR and EIF4E-EIF4A-dependent mechanism. Our findings uncover a novel mechanism by which mutant KRAS selectively uncouples the mRNA translation of the translation machinery from the broader mRNA pool, redefining our understanding of the oncogenic regulation of mRNA translation in cancer.
This dataset
Open GSE285857 in finder → Find more human 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 GSE285857_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 PRJNA1206226 and SRA study SRP555235. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
48 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 48 sample titles: 48 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

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.