GEO series

Transcriptomic Profiling of Immune Cell-Enriched Regions in Triple-Negative Breast Cancer Using Targeted RNA Sequencing

GSE305953 Homo sapiens (human) RNA-seq (gene expression) 15 samples Submitted 2025/08/21 Platform GPL16791

At a glance

RNA-seq (gene expression) · human · 15 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Homo sapiens (human)
Topic: Breast cancerThis series is part of our curated briefing on it.
Samples: 15 samples: 15 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.

Summary (written by the submitters)

Triple-negative breast cancer (TNBC), characterized by the lack of hormone receptors and HER2 expression, remains a challenging subtype due to the absence of effective targeted therapies. Tumor budding (TB), a histopathological feature reflecting invasive potential, has recently emerged as a valuable prognostic marker across various cancers, including TNBC.

Read the full summary ↓

Identifying transcriptomic alterations associated with high TB is critical for understanding mechanisms of tumor progression and immune evasion. In this study, we employed TempO-Seq, a high-throughput targeted RNA sequencing technology, to profile gene expression in immune cell-enriched regions of breast cancer tissues. Our objective was to uncover immune-related biomarkers and gene signatures specific to high TB cases, with the ultimate goal of informing prognostic stratification and potential therapeutic targets in aggressive breast cancer subtypes.

This dataset

Open GSE305953 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 GSE305953_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1306984 and SRA study SRP609486. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

15 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 15 sample titles: 15 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

GSM9188506 B165_BCA01_HER2_IM_LowTB
GSM9188507 IBC-11B_BCA02_luminal_IM_HighTB
GSM9188508 B168_BCA03_TNBC_IM_LowTB
GSM9188509 TNBC-G06_BCA04_TNBC_LowTB
GSM9188510 B031_BCA05_TNBC_HighTB
GSM9188511 B041_BCA06_TNBC_HighTB
GSM9188512 B166_BCA07_TNBC_HighTB
GSM9188513 PC-B-099_BCA08_TNBC_HighTB
GSM9188514 PC-B-130_BCA09_TNBC_HighTB
GSM9188515 TNBC-G01_BCA10_TNBC_HighTB
GSM9188516 TNBC-G03_BCA11_TNBC_HighTB
GSM9188517 TNBC-G19_BCA12_TNBC_HighTB
GSM9188518 TNBC-P04_BCA13_TNBC_HighTB
GSM9188519 TNBC-P06_BCA14_TNBC_HighTB
GSM9188520 TNBC-P09_BCA15_TNBC_HighTB

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GSE11121 The humoral immune system has a key prognostic impact in node-negative breast cancer 200 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
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GSE300385 Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies 12 samples shares triple-negative, targeted, breast
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, breast
GSE268851 Immunogenomic Diversity of Triple-Negative Breast Cancers in Obese and Non-obese Black and White Women 253 samples shares triple-negative, breast

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.