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SOX2 Drives Esophageal Squamous Carcinoma by Reprogramming Lipid Metabolism and Histone Acetylation Landscape (ChIP-seq)

GSE256473 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 10 samples Submitted 2025/07/21 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 10 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
10 samples: 10 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
SOX2 drives esophageal squamous carcinoma by reprogramming lipid metabolism and histone acetylation landscapeNature communications · 2025
Summary (written by the submitters)
SOX2 is not only a pioneer transcription factor with critical roles in stem cell function and cell reprogramming but also a potent initiating oncodriver for a multitude of squamous cancers. How SOX2 exerts a potent oncogenic activity in squamous lineage is largely unknown. Here we uncovered a unique role of SOX2 in driving global histone acetylation and demonstrated that SOX2 is required for the formation of about half of the super-enhancers in esophageal squamous cancer cells (ESCCs).
Read the full summary ↓
Combined metabolic, chromatin and transcriptional analysis revealed at least two mechanisms by which SOX2 drives histone acetylation in ESCCs: promoting the expression of a panel of histone acetyltransferases and program fatty acid metabolism, which channels acetyl-CoA to histone acetylation by promoting the expression of multiple genes in fatty acid -oxidation and by repressing the expression of long-chain acyl-CoA synthase 5 (ACSL5) involved in fatty acid synthesis. We demonstrated that ACSL5 knockdown downregulated fatty acid synthesis and enhanced histone acetylation, whereas overexpression of ACSL5 resulted in lipid accumulation, histone hypoacetylation, and tumor growth inhibition. Finally, we showed that SOX2 expression correlates negatively with ACSL5 and positively with histone acetylation in clinical esophageal squamous tumors. Altogether, our study uncovers a role of SOX2 in reprogramming lipid metabolism and driving histone hyperacetylation and super-enhancer function, providing new mechanistic insights of SOX2 acting as a potent oncodriver.
Published in
SOX2 drives esophageal squamous carcinoma by reprogramming lipid metabolism and histone acetylation landscape
Wang Z, Dai R, Kang L et al. · Nature communications 2025 · PMID 40897722 · doi:10.1038/s41467-025-63591-z
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 GSE256473_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1079751 and SRA study SRP491390. Searching any of these in the dataset finder brings you back here.

Study design
10 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 10 sample titles: 10 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
Datasets on a similar topic

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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.