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Ovarian cancer metastasis to the human omentum disrupts organ homeostasis and induces fundamental tissue reprogramming

GSE286057 Homo sapiens (human) Single-cell RNA-seq 36 samples Submitted 2025/01/08 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 36 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
Homo sapiens (human)
Samples
36 samples: 36 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.
Paper
Ovarian cancer metastasis to the human omentum disrupts organ homeostasis and induces fundamental tissue reprogrammingNature communications · 2025
Summary (written by the submitters)
The omentum, a visceral adipose tissue critical for metabolic, immunological, and stem cell functions, is a key site for ovarian cancer metastasis. However, its role in homeostasis and response to metastasis is not fully understood. Using single-cell transcriptomics, we profiled different omental regions in patients with benign conditions and ovarian cancer metastasis.
Read the full summary ↓
We found that the benign omentum maintains stable cell composition and a stem cell niche. Metastasis led to immune landscape diversification and loss of mesothelial and progenitor cells. The lesser omentum, often spared in surgery, emerged as a premetastatic niche with neutrophil infiltration, NETosis, and micrometastases. Cancer cells orchestrated reprogramming of resident cells, inducing regulatory, anti-adipogenic, and immunosuppressive phenotypes across the omentum. This cell atlas illuminates the cellular and molecular determinants of organ homeostasis and reveals a high degree of plasticity and cellular reprogramming promoted by cancer colonization.
Published in
Ovarian cancer metastasis to the human omentum disrupts organ homeostasis and induces fundamental tissue reprogramming
Lischetti U, Liang CY, Carrara M et al. · Nature communications 2025 · PMID 41397972 · doi:10.1038/s41467-025-67557-z
This dataset
Open GSE286057 in finder → Find more human 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 GSE286057_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 PRJNA1207557. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41397972) ↗
Study design
36 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 36 sample titles: 36 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
Datasets on a similar topic

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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.