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Single nucleus RNA sequencing analysis reveals transcriptional heterogeneity of endometrioid endometrial cancer

GSE278879 Homo sapiens (human) Single-nucleus RNA-seq 11 samples Submitted 2026/06/01 Platform GPL24676
At a glance

Single-nucleus RNA-seq · human · 11 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). 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)
Topic
Endometrial cancerThis series is part of our curated briefing on it.
Samples
11 samples: 4 × normal endometrium of patient vs 3 × high grade endometrioid endometrial cancer of patient vs 2 × middle grade endometrioid endometrial cancer of patient vs 2 × low grade endometrioid endometrial cancer of patientGroups 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)
Endometrial cancer (EC) is the sixth most prevalent malignancy affecting women worldwide, with an increasing incidence that poses a substantial public health challenge. This study investigates the tumor microenvironment (TME) of EC, particularly high grade endometrioid endometrial cancer (HEEC), utilizing single nucleus RNA sequencing (snRNA-seq).
Read the full summary ↓
The analysis encompassed normal endometrium (NE), HEEC, middle grade endometrioid endometrial cancer (MEEC), and low grade endometrioid endometrial cancer (LEEC). Significant cellular heterogeneity was identified within the TME, including immune cells, fibroblasts, and epithelial cells. Notably, SULF1+ NREP+ fibroblasts were significantly enriched in HEEC and associated with aggressive tumor behavior. Additionally, the study highlighted the overexpression of metabolic pathways and immune checkpoint molecules in HEEC epithelial cells, including Human Leukocyte Antigen E (HLA-E) and Killer Cell Lectin Like Receptor C1 (KLRC1), which may contribute to immune evasion and enhanced tumor aggressiveness. Identifying the EYA transcriptional coactivator and phosphatase 1 (EYA1) gene as a prospective biomarker in HEEC due to its association with poor prognosis and the enhanced proliferative and migratory capabilities of EC cells. Furthermore, analyses of cell-cell interactions revealed critical signaling pathways, such as the Leucocyte Associated Immunoglobulin Like Receptor-1 (LAIR1)- Leukocyte Immunoglobulin-like Receptor B4 (LILRB4) axis in macrophages, which may play a role in immune suppression and EEC progression. A comprehensive single-cell level characterization of the TME in EEC offers critical insights into the molecular mechanisms driving EEC progression, while identifying potential therapeutic targets. These discoveries advance our understanding of EEC pathophysiology and hold promise for the development of targeted therapies, particularly in HEEC
This dataset
Open GSE278879 in finder → Find more human RNA-seq datasets → Endometrial cancer landscape →
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 GSE278879_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 PRJNA1169347 and SRA study SRP536797. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
4 × normal endometrium of patient vs 3 × high grade endometrioid endometrial cancer of patient vs 2 × middle grade endometrioid endometrial cancer of patient vs 2 × low grade endometrioid endometrial cancer of patient

Supports a between-group comparison across 11 samples.

How we read this: 4 replicated groups read from 11 sample titles; they account for 11 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
More Endometrial cancer datasets

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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.