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Inhibition of GSK3 and TSC2 Mediates the Oncogenic Activity of AKT in Hepatocellular Carcinoma

GSE301906 Mus musculus (mouse) RNA-seq (gene expression) 10 samples Submitted 2025/08/30 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 10 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
Mus musculus (mouse)
Samples
10 samples: 4 × Normal live vs 3 × Akt/Nras vs 3 × Akt/Nras/FoxoAAAGroups 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)
Aberrant activation of AKT is a key oncogenic driver in hepatocellular carcinoma (HCC). As AKT activates multiple downstream signaling pathways, the key mechanisms mediating AKT-driven tumorigenesis must be elucidated to develop optimal treatment strategies. Using an Akt/NRas-induced HCC mouse model, we found that AKT promotes tumorigenesis by targeting TSC2 and GSK3α&β rather than FOXO family members.
Read the full summary ↓
Loss of either TSC2, leading to mTORC1 activation, or both GSK3 isoforms cooperated with activated NRAS to promote HCC formation in vivo, albeit with different latencies. Simultaneous TSC2 and GSK3α&β deletion cooperated with NRAS to rapidly induce HCC formation, mirroring observations from the Akt/NRas HCC model. RNA-seq studies indicated distinct pathways regulated by TSC2/mTORC1 and GSK3α&β during hepatocarcinogenesis, with FOXM1 functioning as a major effector of GSK3. In summary, these findings uncover AKT’s role in suppressing the TSC complex and GSK3 to drive HCC, offering mechanistic insights into oncogenic signaling and potential therapeutic targets.
This dataset
Open GSE301906 in finder → Find more mouse 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 GSE301906_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 PRJNA1287718 and SRA study SRP599294. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
4 × Normal live vs 3 × Akt/Nras vs 3 × Akt/Nras/FoxoAAA

Supports a between-group comparison across 10 samples.

How we read this: 3 replicated groups read from 10 sample titles; they account for 10 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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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.