GEO series

Cancer cell-derived arginine fuels polyamine biosynthesis in tumor-associated macrophages to promote immune evasion

GSE285794 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2025/06/30 Platform GPL34290

At a glance

Single-cell RNA-seq · mouse · 12 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: 12 samples: 3 × EO771-c57-SH vs 3 × EO771-c57-WT vs 3 × T1-balbc-Sh vs 3 × T1-balbc-WTGroups 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.
Paper: Cancer cell-derived arginine fuels polyamine biosynthesis in tumor-associated macrophages to promote immune evasionCancer cell · 2025

Summary (written by the submitters)

Cancer cell metabolic reprogramming reshapes the tumor microenvironment (TME) into a pro-tumor niche. Arginine metabolism plays complex roles in regulating tumor progression through metabolic cross-feeding among various cell types within the TME, but the primary intercellular communication dominating the collective impact of arginine on tumor progression remains unclear.

Read the full summary ↓

Here, we investigated the role of arginine metabolism in breast cancer progression using clinical analysis, single-cell RNA sequencing data analysis, and in vivo animal models. We found that the metabolic interplay between cancer cells and macrophages exerts a predominant influence on breast cancer progression mediated by arginine metabolism. Breast cancer cells act as the major source of arginine within the TME, where this tumor-derived arginine fosters a pro-tumor shift in tumor-associated macrophages (TAMs), leading them to suppress the anti-tumor activity of CD8+ T cells. Notably, this cancer cell-macrophage interaction overrides the stimulatory effect of arginine on the anti-tumor capability of CD8+ T cells. Mechanistically, polyamines derived from arginine metabolism mediate TAM polarization via TDG-mediated DNA demethylation, driven by p53 signaling. Importantly, inhibiting the arginine-polyamine-TDG axis across cancer cells to macrophages effectively suppresses breast cancer growth by modulating the immune microenvironment, suggesting therapeutic potential in targeting this axis for breast cancer therapy.

Published in

Cancer cell-derived arginine fuels polyamine biosynthesis in tumor-associated macrophages to promote immune evasion

Zhu Y, Zhou Z, Du X et al. · Cancer cell 2025 · PMID 40185095 · doi:10.1016/j.ccell.2025.03.015

This dataset

Open GSE285794 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 GSE285794_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 PRJNA1206102 and SRA study SRP555129. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40185095) ↗

Study design

3 × EO771-c57-SH vs 3 × EO771-c57-WT vs 3 × T1-balbc-Sh vs 3 × T1-balbc-WT

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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

GSM8709324 EO771-c57-SH9 EO771-c57-SH
GSM8709325 EO771-c57-SH13 EO771-c57-SH
GSM8709326 EO771-c57-SH24 EO771-c57-SH
GSM8709327 EO771-c57-WT17 EO771-c57-WT
GSM8709328 EO771-c57-WT20 EO771-c57-WT
GSM8709329 EO771-c57-WT21 EO771-c57-WT
GSM8709330 T1-balbc-Sh1 T1-balbc-Sh
GSM8709331 T1-balbc-Sh2 T1-balbc-Sh
GSM8709332 T1-balbc-Sh4 T1-balbc-Sh
GSM8709333 T1-balbc-WT1 T1-balbc-WT
GSM8709334 T1-balbc-WT3 T1-balbc-WT
GSM8709335 T1-balbc-WT4 T1-balbc-WT

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