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Aging represses oncogenic KRAS-driven lung tumorigenesis and alters tumor suppression

GSE297023 Mus musculus (mouse) Single-cell RNA-seq 16 samples Submitted 2025/08/03 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 16 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
16 samples: 16 × Dissociated tumor-bearing lung, mixed age and genotype (all samples represented), sublibraryGroups 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)
Most cancers are diagnosed in people over the age of sixty, but little is known about how age impacts tumorigenesis. While aging is accompanied by mutation accumulation — widely understood to contribute to cancer risk — it is also associated with numerous other cellular and molecular changes likely to impact tumorigenesis. Moreover, cancer incidence decreases in the oldest part of the population, suggesting that very old age may reduce carcinogenesis.
Read the full summary ↓
Here we show that aging represses oncogenic KRAS-driven tumor initiation and growth in genetically engineered mouse models of human lung cancer. Moreover, aging dampens the impact of inactivating many, but not all, tumor suppressor genes with the impact of inactivating PTEN, a negative regulator of the PI3K/AKT pathway, weakened to a disproportionate extent. Single-cell transcriptomic analysis revealed that neoplastic cells from tumors in old mice retain many age-related transcriptomic changes, showing that age has an enduring impact that persists through oncogenic transformation. Furthermore, the consequences of PTEN inactivation were strikingly age-dependent, with PTEN deficiency reducing signatures of aging in cancer cells and the tumor microenvironment. Our findings suggest that the relationship between age and lung cancer incidence may reflect an integration of the competing effects of driver mutation accumulation and tumor suppressive effects of aging.
This dataset
Open GSE297023 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 GSE297023_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1262399 and SRA study SRP584959. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 × Dissociated tumor-bearing lung, mixed age and genotype (all samples represented), sublibrary

Supports a between-group comparison across 16 samples.

How we read this: 1 replicated groups read from 16 sample titles; they account for 16 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

Search all mouse 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.