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A spatially resolved atlas of gastric cancer characterises a lymphocyte aggregated region [bulk RNA-seq]

GSE270679 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2025/12/03 Platform GPL16791
At a glance

Single-cell RNA-seq · human · 16 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
16 samples: 16 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
A spatially resolved atlas of gastric cancer characterises a lymphocyte-aggregated regionNature communications · 2026
Summary (written by the submitters)
The tumour microenvironment (TME) is a focal point in cancer immunotherapy: its cellular composition and spatial organisation, especially the distribution of lymphocytes, can affect the clinical outcomes of cancer patients. In addition, the function of a cell differs depending on its spatial location and interaction with neighbouring cells.
Read the full summary ↓
Here, by integrating single-cell transcriptomics with spatial transcriptomics, we survey how the spatial distribution of different cell types varies across diverse histological regions of gastric cancer. Notably, tertiary lymphoid structures (TLSs), a tissue architecture harbouring lymphocytes more than any other region within the TME, possess great potential in modulating anti-tumorigenic immunity by elevating an array of genes (FDCSP and CCL19) principally involved in lymphocyte recruitment and activation. Our findings advance the understanding of TLSs in contributing to anti-tumorigenic immunity in a spatially resolved context, which could be further leveraged as a predictive marker for immunotherapy response.
Published in
A spatially resolved atlas of gastric cancer characterises a lymphocyte-aggregated region
Gao S, Qin S, Wang D et al. · Nature communications 2026 · PMID 41593079 · doi:10.1038/s41467-026-68612-z
This dataset
Open GSE270679 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 GSE270679_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 PRJNA1127983. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41593079) ↗
Study design
16 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 16 sample titles: 16 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.