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Atlas of metastatic gastric cancer links ferroptosis to disease progression and immunotherapy response

GSE239676 Homo sapiens (human) Single-cell RNA-seq 68 samples Submitted 2024/05/23 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 68 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)
Topic
Gastric cancerThis series is part of our curated briefing on it.
Samples
68 samples: 68 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
Atlas of Metastatic Gastric Cancer Links Ferroptosis to Disease Progression and Immunotherapy ResponseGastroenterology · 2024
Summary (written by the submitters)
Gastric adenocarcinoma (GAC) remains a significant cause of cancer-related deaths worldwide, with the majority of mortality resulting from metastatic spread. To elucidate the evolution of cancer cells and their interactions with the tumour microenvironment (TME), we conducted a comprehensive single-cell transcriptome and immune repertoire profiling of primary tumours (PRIs), matched liver metastases (LMs) or peritoneal carcinomatosis (PC), adjacent normal tissues, and blood samples from 20 treatment-naïve patients.
Read the full summary ↓
We found that the TME within metastases varied substantially from PRIs and differed between LMs and PC samples. Indicative of immune evasion, we observed diminished T cell clonal expansion, a marked reduction in the fraction of tumour-reactive CD8 T cells, and an enrichment of naïve T cells, M2-like, and proliferative macrophages in PC samples compared to PRIs. LMs displayed significantly reduced B cells, with increased fractions of exhausted CD8 T cells and NK/NKT cells. Both LMs and PC showed nearly depleted plasma cells. Malignant cells largely exhibited diminished gastric lineage identity, predominantly adopting intestinal-like and mixed cell states, with the acquired expression of gastrointestinal stem-cell markers. LM cancer cells showed increased epithelial-to-mesenchymal signalling, while PC cancer cells had increased levels of aneuploidy. We observed differentially expressed cancer meta-programs and gene modules (GMs) between LMs and PC, with high GM2 expression in the PRI predicting an increased risk of distant metastases. Further analysis of GM signature genes revealed ferroptosis evasion through GPX4 upregulation during malignant progression. Functional validation via patient-derived xenografts and a novel autochthonous mouse model of GAC confirmed that genetic or pharmacological inhibition of ferroptosis resistance attenuates tumor growth and metastatic progression. Together, our mapping of malignant progression provides a rationale for targeting ferroptosis defense in combination with next-generation CAR T-cell immunotherapy as a potential avenue to overcome T cell dysfunction in advanced metastatic GAC therapy.
Published in
Atlas of Metastatic Gastric Cancer Links Ferroptosis to Disease Progression and Immunotherapy Response
Cheng X, Dai E, Wu J et al. · Gastroenterology 2024 · PMID 39097198 · doi:10.1053/j.gastro.2024.07.038
This dataset
Open GSE239676 in finder → Find more human RNA-seq datasets → Gastric 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 GSE239676_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 PRJNA1000548. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39097198) ↗
Study design
68 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 68 sample titles: 68 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

+ 28 more — browse all 68 samples with per-sample file links →

More Gastric cancer datasets

Search all human RNA-seq datasets in GEO →

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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.