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Impaired CD103+ T cell accumulation facilitates oxidative damage induced lung adenocarcinoma

GSE245130 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/10/11 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 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
Mus musculus (mouse)
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.
Summary (written by the submitters)
Immune microenvironment plays essential roles in tumor progression. The molecular mechanism underlying the regulation of immune surveillance in tumor initiation remains to be elucidated. Here we show that mice with T cell-specific deletion of the mediator subunit Med23 develops the alveolar epithelial type II cells (AT2 cells) originated lung adenocarcinoma, and such lung tumors displays an increased mutation frequency of oncogenes.
Read the full summary ↓
Notably, the tumorigenesis of lung adenocarcinoma in Med23-deficient mice is promoted by the accumulation of AT2 cells under oxidative stress and oxidative DNA damage. Antioxidant treatment partially rescues spontaneous lung adenocarcinoma in Med23-deficient mice. Moreover, we find that loss of MED23 impairs the accumulation of lung CD103+ T cells, usually recognized as tissue resident T cells. CD103+ T cells have the ability to scavenge AT2 cells bearing oxidative DNA damage and prevent the tumorigenesis of lung adenocarcinoma. Mechanistically, CD103+ T cells predispose cells with high-level of oxidative stress to apoptosis. Collectively, our study finds MED23 is the key regulator of CD103+ T cell in lungs and reveals the immunosurveillance of CD103+ T cells in tumorigenesis.
This dataset
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.

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 GSE245130_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1026984 and SRA study SRP465792. Searching any of these in the dataset finder brings you back here.

Study design
12 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 3 samples read as cases, 3 as controls.

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
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