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DNAJB1-PRKACA fusion drives fibrolamellar liver cancer through impaired SIK signaling and CRTC2/p300-mediated transcriptional reprogramming

GSE266253 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2024/09/08 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 12 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
12 samples: 12 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
DNAJB1-PRKACA Fusion Drives Fibrolamellar Liver Cancer through Impaired SIK Signaling and CRTC2/p300-Mediated Transcriptional ReprogrammingCancer discovery · 2025
Summary (written by the submitters)
Fibrolamellar carcinoma (FLC) is a liver cancer of adolescents and young adults defined by fusion of the DNAJB1 heat shock protein and protein kinase A (PKA) catalytic subunit (DNAJB1-PRKACA). The resulting chimeric protein has increased kinase activity and is essential for FLC xenograft growth. However, the critical oncogenic pathways controlled by DNAJB1-PRKACA have not been defined.
Read the full summary ↓
Here, we explored this question by studying patient-derived FLC models and engineered systems and analyzing patient samples. We show that the core function of DNAJB1-PRKACA is the direct phosphorylation and inactivation of the Salt-inducible kinases. This leads to deregulation of the CRTC2 co-activator and p300 acetyltransferase, resulting in transcriptional reprogramming and global increases in histone acetylation necessary for malignant growth. Our studies establish a central oncogenic mechanism of DNAJB1-PRKACA and suggest opportunities for therapeutic targeting of CRTC2/p300 in FLC. Notably, these findings link this signature fusion oncoprotein of a rare cancer type to more common cancer gene alterations involving the STK11 tumor suppressor and GNAS oncogene, which also function via SIK suppression.
Published in
DNAJB1-PRKACA Fusion Drives Fibrolamellar Liver Cancer through Impaired SIK Signaling and CRTC2/p300-Mediated Transcriptional Reprogramming
Gritti I, Wan J, Weeresekara V et al. · Cancer discovery 2025 · PMID 39326063 · doi:10.1158/2159-8290.CD-24-0634
This dataset
Open GSE266253 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 GSE266253_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 PRJNA1106454 and SRA study SRP504953. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39326063) ↗
Study design
12 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 12 sample titles: 12 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
Datasets on a similar topic

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