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Ki-67 promotes circulating tumor cell intravasation and metastasis in breast cancer

GSE303593 Homo sapiens (human) Single-cell RNA-seq 20 samples Submitted 2026/04/13 Platform GPL34290Platform GPL34284
At a glance

Single-cell RNA-seq · human · 20 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
Breast cancerThis series is part of our curated briefing on it.
Samples
20 samples: 8 conditions, mostly in duplicateGroups 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)
Hematogenous dissemination by circulating tumor cells (CTCs) drives metastatic spread. However, the molecular features that empower these cells to colonize distant organs remain unclear. We profiled single CTCs and CTC clusters from three breast cancer xenograft models by single-cell RNA sequencing and uncovered a shared transcriptional program marked by broad upregulation of cell-cycle genes.
Read the full summary ↓
A targeted CRISPR loss-of-function screen identified MKI67 as an essential regulator of CTC abundance, particularly of cluster abundance, and metastatic progression. MKI67 disruption markedly reduced CTC numbers and decreased the fraction ofCTC clusters, leading to diminished lung metastases. CRISPRa-mediated re-expression of downstream adhesion genes CD47 and KLF4 restored both cluster prevalence and metastatic burden. Our findings link cell-cycle upregulation to enhanced metastatic capacity and position MKI67-dependent adhesion signals as tractable targpendent adhesion signals as tractable targets against metastatic spread.
This dataset
Open GSE303593 in finder → Find more 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 GSE303593_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 PRJNA1295891 and SRA study SRP603319. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in duplicate
LM2_primary_tumor_m ×2 LM2_lung_metastasis_m ×2 LM2_ctc_single_m ×2 LM2_ctc_cluster_m ×2 MVT1_primary_tumor_m ×2 MVT1_lung_metastasis_m ×2 MVT1_ctc_single_m ×2 MVT1_ctc_cluster_m ×2 +4 more

Supports a between-group comparison across 16 samples.

How we read this: 8 replicated groups read from 20 sample titles; they account for 16 of them. 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
More Breast cancer datasets

Search all 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.