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RNPC3 knockdown in A549 lung adenocarcinoma cells

GSE190943 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2025/06/03 Platform GPL24676Platform GPL26167
At a glance

RNA-seq (gene expression) · human · 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
Homo sapiens (human)
Topic
Lung cancerThis series is part of our curated briefing on it.
Samples
16 samples: 16 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
Inhibition of the minor spliceosome restricts the growth of a broad spectrum of cancersEMBO reports · 2025
Summary (written by the submitters)
Purpose of the study was to investigate the impact of reducing the efficiency of minor (U12-type) splicing on the growth and gene expression of a human cancer cell line. We chose the A549 cell line, derived from a human lung adenocarcinoma, for this study. There are only approximately 700 genes (3.5%) in the human genome that contain a minor intron.
Read the full summary ↓
Most transcripts (96.5%) require only major (U2-dependent) splicing for correct gene expression. This is carried out by the major spliceosome. A different spliceosome, known as the minor or U11/12-type spliceosome, is required to excise minor introns from pre-RNA transcripts that contain them. One of the unique protein components of the minor spliceosome is a 65kDa protein encoded by the RNPC3 gene. To reduce the efficiency of minor splicing, we used siRNA knockdown methodology to deplete RNPC3 transcripts in A549 cells by approximately 70%. Specifically, A549 cells in growth phase were transfected with siRNA targeted to RNPC3 transcripts for 72h. Control A549 cells were treated in the same experiment with non-targeted (NT) siRNA molecules instead of siRNA targeted to RNPC3.
Published in
Inhibition of the minor spliceosome restricts the growth of a broad spectrum of cancers
Doggett K, Morgan KJ, Olthof AM et al. · EMBO reports 2025 · PMID 40624356 · doi:10.1038/s44319-025-00511-8
This dataset
Open GSE190943 in finder → Find more human RNA-seq datasets → Lung cancer landscape →
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 GSE190943_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 PRJNA789044 and SRA study SRP351313. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40624356) ↗
Study design
16 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 16 sample titles: 16 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
More Lung cancer datasets

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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.