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A multi-cancer CRISPR/Cas9 reveals distinct gene dysregulation by noncoding MSTRG transcripts

GSE229846 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2024/06/17 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 24 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
24 samples: 3 × A549-MSTRG-2513-6_KO_S vs 3 × A549-MSTRG-4401-1_KO_S vs 3 × HCT116-MSTRG-2513-6_KO_S vs 3 × HCT116-MSTRG-4401-1_KO_SGroups 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
Substantial unannotated noncoding transcripts in tumors may transcriptionally regulate cancer-related genesBMC biology · 2026
Summary (written by the submitters)
Long noncoding RNAs (lncRNAs) regulate gene expression with their functional domains without being translated into proteins. Whether wrong noncoding transcripts have functions is of special interest and lacks systematic investigation. Our study identified several MSTRG transcripts that may have regulatory functions through specific DNA-binding domains (DBDs).
Read the full summary ↓
We selected three MSTRG noncoding transcripts and validated their roles in cancer through CRISPR/Cas9-mediated knockout experiments, using A549 and HCT116 cell lines as experimental models. Then, we conducted a comparative gene expression analysis using RNA-seq data to compare the knockout of three aberrant human non-coding transcripts to their respective wild-type controls.
Published in
Substantial unannotated noncoding transcripts in tumors may transcriptionally regulate cancer-related genes
He S, Xiong W, Huo J et al. · BMC biology 2026 · PMID 41606598 · doi:10.1186/s12915-026-02524-8
This dataset
Open GSE229846 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 GSE229846_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 PRJNA956439 and SRA study SRP432947. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41606598) ↗
Study design
3 × A549-MSTRG-2513-6_KO_S vs 3 × A549-MSTRG-4401-1_KO_S vs 3 × HCT116-MSTRG-2513-6_KO_S vs 3 × HCT116-MSTRG-4401-1_KO_S

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 24 sample titles; they account for 12 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
Datasets on a similar topic

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