← BioTransfer GEO Dataset Finder
GEO series

Harnessing Small Molecules for Revealing Vulnerabilities in Circulating Tumor Cells.

GSE251771 Homo sapiens (human) RNA-seq (gene expression) 42 samples Submitted 2026/02/18 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 42 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
42 samples: 42 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
Novel Cell Cycle Inhibitors Decrease Primary and Metastatic Breast Cancer Growth In VivoCancers · 2026
Summary (written by the submitters)
Breast cancer is among the most commonly diagnosed cancers, accounting for 685,000 fatalities in 2020 worldwide. High mortality rate is associated with the development of metastases, a complex process involving the spread of cancer cells from the primary tumor site to secondary organs. At present, treating patients with metastatic disease is challenging, and this still represent an unmet medical need.
Read the full summary ↓
As clinically-relevant models, circulating tumor cell (CTC)-derived models can serve as a drug screen platforms that enable drug susceptibility testing and discovery of novel compounds with anti-cancer progression activity. Here, by employing a combination of CTC-based drug screening platform, in vivo models of metastatic breast cancer, RNA sequencing, and in vitro cell assays, we identified new agents for the reduction of tumor growth and lung colonization in breast cancer. Furthermore, our transcriptomic data delineate the metastasis-relevant signaling pathways affected by these treatments.
Published in
Novel Cell Cycle Inhibitors Decrease Primary and Metastatic Breast Cancer Growth In Vivo
Maqbool MS, Zhang Y, Strittmatter K et al. · Cancers 2026 · PMID 41681937 · doi:10.3390/cancers18030466
This dataset
Open GSE251771 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 GSE251771_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 PRJNA1055444 and SRA study SRP479404. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41681937) ↗
Study design
42 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 42 sample titles: 42 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

+ 2 more — browse all 42 samples with per-sample file links →

More Breast cancer datasets

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.