GEO series

Gene expression at single cell level of tumor-infiltrating cells from the tumor foci, draining lymph node and peripheral blood of triple-negative breast cancer

GSE263995 Homo sapiens (human) Single-cell RNA-seq 14 samples Submitted 2024/06/12 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 14 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Topic: Breast cancerThis series is part of our curated briefing on it.
Samples: 14 samples: 14 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: YTHDF2 favors protumoral macrophage polarization and implies poor survival outcomes in triple negative breast canceriScience · 2024

Summary (written by the submitters)

Tumor-infiltrating cells are considered as a homogeneous populations in the tumor microenvironment. We used single cell RNA sequencing (scRNA-seq) to analyze the diversity of tumor-infiltrating cells of the tumor foci, draining lymph node and peripheral blood from patients with triple-negative breast cancer who received chemotherapy.

Published in

YTHDF2 favors protumoral macrophage polarization and implies poor survival outcomes in triple negative breast cancer

Jin H, Chen Y, Zhang D et al. · iScience 2024 · PMID 38812540 · doi:10.1016/j.isci.2024.109902

This dataset

Open GSE263995 in finder → Find more human RNA-seq datasets → Breast 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 GSE263995_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 PRJNA1100523 and SRA study SRP501858. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38812540) ↗

Study design

14 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 14 sample titles: 14 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

GSM8208510 P0102, Pre, scRNAseq
GSM8208511 P0102, Post, scRNAseq
GSM8391107 P02_tumor,Pre, scRNAseq
GSM8391108 P02_TdLN,Pre, scRNAseq
GSM8391109 P02_PBMC,Pre, scRNAseq
GSM8391110 P02_tumor,Post, scRNAseq
GSM8391111 P02_TdLN,Post, scRNAseq
GSM8391112 P02_PBMC,Post, scRNAseq
GSM8391113 P03_tumor,Pre, scRNAseq
GSM8391114 P03_TdLN,Pre, scRNAseq
GSM8391115 P03_PBMC,Pre, scRNAseq
GSM8391116 P03_tumor,Post, scRNAseq
GSM8391117 P03_TdLN,Post, scRNAseq
GSM8391118 P03_PBMC,Post, scRNAseq

More Breast cancer datasets

GSE104193 Hypoxia-mediated translational activation of ITGB3 in breast cancer cells enhances TGF-β signalling and malignant features in vitro and in vivo 32 samples same disease briefing · shares breast
GSE104675 Role for the Transcriptional Activator ZRF1 in Breast Cancer Progression and Endocrine Resistance 6 samples same disease briefing · shares breast
GSE10886 A Supervised Risk Predictor of Breast Cancer Based on Biological Subtypes 226 samples same disease briefing · shares breast
GSE11121 The humoral immune system has a key prognostic impact in node-negative breast cancer 200 samples same disease briefing · shares breast
GSE10885 An integromic analysis reveals that metaplastic carcinomas are distinct from basal-like tumors. 244 samples same disease briefing
GSE328275 Single-cell RNA sequencing of CD45+ immune cells across primary tumor, sentinel tumor-draining lymph node, and axillary lymph node in treatment-naive triple-negative breast cancer 28 samples shares triple-negative, draining, breast, lymph
GSE322527 Single-cell transcriptomes and T cell receptor sequences of CD4 or CD8 single positive T lymphocytes from peripheral blood or urine of lupus nephritis patients at flare. 26 samples shares peripheral, lymph, blood
GSE232381 RNA-seq of Peripheral Blood Cells Identifies Molecular Network Linked to Lymphocyte Subset Aberrations and Active Lupus Nephritis in SLE 25 samples shares peripheral, lymph, blood

Search all human RNA-seq datasets in GEO →

Share this dataset

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.