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Multi-omics unveils GTSE1 promotes esophageal squamous cell carcinoma stemness via Hippo signaling pathway

GSE305720 Homo sapiens (human) Single-cell RNA-seq 20 samples Submitted 2026/08/01 Platform GPL24676
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
Esophageal cancerThis series is part of our curated briefing on it.
Samples
20 samples
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)
Cancer stem cells (CSCs) are pivotal in driving tumorigenesis, metastasis, and resistance to multiple drugs in cancer. In esophageal squamous cell carcinoma (ESCC), CSCs are crucial for disease progression. This investigation sought to pinpoint genes associated with stemness and elucidate the impact and underlying mechanism of GTSE1 on ESCC stemness.
Read the full summary ↓
The analysis of mRNAsi in bulk RNA-seq and single RNA-seq data reveals elevated expression of GTSE1 in the high mRNAsi subgroup and clusters of highly malignant single cells. Functional studies demonstrate that depletion of GTSE1 hinders self-renewal and tumor progression of ESCC both in vitro and in vivo. Our multi-omics analysis reveals GTSE1 as a novel gene associated with stemness.
This dataset
Open GSE305720 in finder → Find more human RNA-seq datasets → Esophageal 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 GSE305720_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 PRJNA1307510 and SRA study SRP609725. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
A cohort of 20 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run P1 … P20 and carry no group information.

Samples in this study
More Esophageal cancer datasets

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.