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Cold-inducible brown fat thermogenesis enhances MHC class I expression and antitumor immunity in ovarian cancer.

GSE264447 Mus musculus (mouse) RNA-seq (gene expression) 28 samples Submitted 2025/12/31 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 28 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
28 samples: 6 conditions, mostly with about 6 replicates eachGroups 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)
Brown adipose tissue (BAT) thermogenesis, induced by cold exposure or β3-adrenergic receptors (β3AR) agonist, decreases tumor glucose availability and arrests tumor progression. However, the effect of BAT activation on tumor metabolic reprogramming, the tumor microenvironment (TME), and the possibility of in combination with immunotherapy has not been defined.
Read the full summary ↓
Here, we found that cold-inducible BAT thermogenesis promoted an anti-Warburg metabolic shift that reshaped TME characterized by accumulation of tumor-infiltrating CD8+ T cells (CD8+ TILs) with effector-like and memory phenotype and proinflammatory macrophages. Consistently, combination treatment with anti-PD-1 and cold exposure or β3AR agonist, strongly inhibited tumor growth or complete regressions across multiple preclinical mouse models. Therefore, BAT thermogenesis by cold exposure or β3aR agonist, highlighting a promising therapeutic approach to increase tumor immunogenicity and improve immunotherapy efficacy.
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 GSE264447_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 PRJNA1102423 and SRA study SRP502931. Searching any of these in the dataset finder brings you back here.

Study design
6 conditions, mostly with about 6 replicates each
Tumor tissues, Control, ×6 Tumor tissues, Cold, ×6 Tumor cells, Control, ×5 CD8TILs, Cold, ×4 Tumor cells, Cold, ×4 CD8TILs, Control, ×3

Supports a case/control comparison: 10 samples read as cases, 3 as controls.

How we read this: 6 replicated groups read from 28 sample titles; they account for 28 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
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