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Proteogenomic characterization reveals that lipid droplet formation promotes esophageal squamous cell cancer progression

GSE180791 Homo sapiens (human) RNA-seq (gene expression) 102 samples Submitted 2025/04/02 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 102 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
Esophageal cancerThis series is part of our curated briefing on it.
Samples
102 samples: 102 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
Proteogenomic characterization reveals that lipid droplet formation promotes esophageal squamous cell cancer progressionScience translational medicine · 2025
Summary (written by the submitters)
This study aims to uncover the transcriptomic variations in ESCC. Our study provides a resource to explore the relationships between genetic variation and transcriptional or translational regulations.
Published in
Proteogenomic characterization reveals that lipid droplet formation promotes esophageal squamous cell cancer progression
Qin Z, Jiang D, Yu Z et al. · Science translational medicine 2025 · PMID 40498854 · doi:10.1126/scitranslmed.adt0214
This dataset
Open GSE180791 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 GSE180791_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 PRJNA749571 and SRA study SRP329790. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40498854) ↗
Study design
102 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 102 sample titles: 102 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

+ 62 more — browse all 102 samples with per-sample file links →

More Esophageal 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.