GEO series

Identification of KIF20A as a vulnerability for breast cancer stem-like cells in triple negative breast cancer

GSE298177 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2025/10/09 Platform GPL34284

At a glance

RNA-seq (gene expression) · human · 24 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: 24 samples: 8 conditions, mostly in triplicateGroups 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), being both aggressive and highly lethal, poses a significant challenge in terms of treatment. Its heterogeneity and lack of hormone receptors or HER2 expression further restrict the availability of targeted therapy. Breast cancer stem cells (BCSCs), known to fuel TNBC malignancy, are now being exploited as the vulnerability for TNBC treatment.

Read the full summary ↓

Here, we dissected the transcriptome of BCSCs and identified Kinesin Family Member 20A (KIF20A) as an essential gene for BCSC survival and TNBC tumorigenesis. Depletion or pharmacologic inhibition of KIF20A impairs BCSC viability, tumor initiation and development in vitro and in vivo. Mechanistically, KIF20A regulates BCSC stemness through modulating the oxidative phosphorylation (OXPHOS) pathway in mitochondria, which is repressed by a member of the SWItch/Sucrose Non-Fermentable (SWI/SNF) chromatin remodeling complex, SMARCA4. Therapeutically, treatment with a KIF20A inhibitor sensitizes TNBC xenograft tumors to standard-of-care chemotherapy. Our study highlights the significance of targeting KIF20A to exploit BCSC vulnerabilities in TNBC.

This dataset

Open GSE298177 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 GSE298177_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 PRJNA1268475 and SRA study SRP587899. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

8 conditions, mostly in triplicate

Hs578T, NEG ×3 Hs578T, POS ×3 HCC70, NEG ×3 HCC70, POS ×3 MDA-MB-231, sgCTRL ×3 MDA-MB-231, KIF20A SG ×3 MDA-MB-231, EV ×3 MDA-MB-231, KIF20A OE ×3

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 24 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

GSM9009524 Hs578T, NEG1 Hs578T, NEG
GSM9009525 Hs578T, NEG2 Hs578T, NEG
GSM9009526 Hs578T, NEG3 Hs578T, NEG
GSM9009527 Hs578T, POS1 Hs578T, POS
GSM9009528 Hs578T, POS2 Hs578T, POS
GSM9009529 Hs578T, POS3 Hs578T, POS
GSM9009530 HCC70, NEG1 HCC70, NEG
GSM9009531 HCC70, NEG2 HCC70, NEG
GSM9009532 HCC70, NEG3 HCC70, NEG
GSM9009533 HCC70, POS1 HCC70, POS
GSM9009534 HCC70, POS2 HCC70, POS
GSM9009535 HCC70, POS3 HCC70, POS
GSM9009536 MDA-MB-231, sgCTRL1 MDA-MB-231, sgCTRL
GSM9009537 MDA-MB-231, sgCTRL2 MDA-MB-231, sgCTRL
GSM9009538 MDA-MB-231, sgCTRL3 MDA-MB-231, sgCTRL
GSM9009539 MDA-MB-231, KIF20A SG1 MDA-MB-231, KIF20A SG
GSM9009540 MDA-MB-231, KIF20A SG2 MDA-MB-231, KIF20A SG
GSM9009541 MDA-MB-231, KIF20A SG3 MDA-MB-231, KIF20A SG
GSM9009542 MDA-MB-231, EV1 MDA-MB-231, EV
GSM9009543 MDA-MB-231, EV2 MDA-MB-231, EV
GSM9009544 MDA-MB-231, EV3 MDA-MB-231, EV
GSM9009545 MDA-MB-231, KIF20A OE1 MDA-MB-231, KIF20A OE
GSM9009546 MDA-MB-231, KIF20A OE2 MDA-MB-231, KIF20A OE
GSM9009547 MDA-MB-231, KIF20A OE3 MDA-MB-231, KIF20A OE

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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.