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mTORC2-HIF-1β signaling creates a targetable sphingolipid vulnerability in lung cancer [Polysome-seq]

GSE336137 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2026/07/01 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)
Topic
Lung cancerThis series is part of our curated briefing on it.
Samples
24 samples: 8 conditions, mostly in triplicateGroups 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)
Despite extensive genetic heterogeneity, lung tumors frequently converge on shared signaling dependencies that remain therapeutically underexploited. Here, we identify mTORC2 signaling as a convergent dependency across genetically distinct lung cancer subtypes and demonstrate that RICTOR-dependent mTORC2 activity promotes tumor progression through HIF-1β-dependent sphingolipid reprogramming.
Read the full summary ↓
Elevated mTORC2 signaling in lung adenocarcinoma was associated with poor overall survival, metastatic dissemination and metabolic rewiring. Using complementary genetically engineered mouse models of Rictor deletion or overexpression in Kras-driven lung tumors, we show that mTORC2 activity is dispensable for normal lung homeostasis but required for tumor progression and metabolic adaptation in vivo. Mechanistically, mTORC2 stabilized HIF-1β by preventing its ubiquitin-independent proteasomal degradation through a non-canonical PKCα-CK2 signaling axis, independently of AKT. Integrated multi-omics analyses revealed that mTORC2-HIF-1β signaling drives extensive sphingolipid remodeling through the regulation of key metabolic enzymes, including SPHK1 and SPTLC2. Accordingly, pharmacological inhibition of sphingolipid metabolism markedly impaired the growth of mTORC2-driven lung tumors in vivo. Together, our findings uncover a previously unrecognized mTORC2-HIF-1β-sphingolipid signaling axis that creates targetable metabolic vulnerabilities in lung cancer.
This dataset
Open GSE336137 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 GSE336137_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 PRJNA1480756 and SRA study SRP711547. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in triplicate
A549-siNT-POLY ×3 A549-siRIC-POLY ×3 A549-siRIC-Tot-RNA ×3 H460-siNT-POLY ×3 H460-siNT-Tot-RNA ×3 H460-siRIC-POLY ×3 H460-siRIC-Tot-RNA ×3 A549-siNT-Tot-RNA ×2 +1 more

Supports a between-group comparison across 23 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 23 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
More Lung cancer datasets

Search all human RNA-seq datasets in GEO →

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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.