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EPHB2 Drives EMT-Linked Vasculogenic Mimicry and Cervical Cancer Progression via an ERK–ETV4 Transcriptional Program

GSE326407 Homo sapiens (human) RNA-seq (gene expression) 9 samples Submitted 2026/04/08 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 9 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
Cervical cancerThis series is part of our curated briefing on it.
Samples
9 samples: 3 × HeLa_siCtrl vs 3 × HeLa_siEPHB2_si1 vs 3 × HeLa_siEPHB2_si2Groups 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
EPHB2 drives EMT-linked vasculogenic mimicry and cervical cancer progression via an ERK-ETV4 transcriptional programJournal of translational medicine · 2026
Summary (written by the submitters)
To investigate the biological function EPHB2 in the cervical cancer, we established hela cell lines in which the EPHB2 gene has been knocked down by the small interfering RNA (siRNA) technique. Two different siRNAs targeting EPHB2 (KD) were used to knockdown EPHB2 expression in hela cells. A scrambled siRNA (NC) was used as a control. For each sample, three independent biological replicates were performed. Then RNA-Seq experiment was performed by Shanghai Biotechnology Corporation (Shanghai, China).
Published in
EPHB2 drives EMT-linked vasculogenic mimicry and cervical cancer progression via an ERK-ETV4 transcriptional program
Jiao W, Liu S, Huai Q et al. · Journal of translational medicine 2026 · PMID 42143313 · doi:10.1186/s12967-026-08300-0
This dataset
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.

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 GSE326407_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1445209 and SRA study SRP687747. Searching any of these in the dataset finder brings you back here.

Study design
3 × HeLa_siCtrl vs 3 × HeLa_siEPHB2_si1 vs 3 × HeLa_siEPHB2_si2

Supports a between-group comparison across 9 samples.

How we read this: 3 replicated groups read from 9 sample titles; they account for 9 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 Cervical cancer datasets

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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.