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Gene regulation profiling by KMT2D under different glucose conditions in HNSCC (RNAseq)

GSE237454 Homo sapiens (human) RNA-seq (gene expression) 14 samples Submitted 2024/07/01 Platform GPL21290Platform GPL21697
At a glance

RNA-seq (gene expression) · human · 14 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)
Samples
14 samples: 2 × (+Glc) KO1 RNAseq exp vs 2 × (+Glc) KO2 RNAseq exp vs 2 × (-Glc) KO1 RNAseq exp vs 2 × (-Glc) KO2 RNAseq expGroups 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
Histone-methyltransferase KMT2D deficiency impairs the Fanconi anemia/BRCA pathway upon glycolytic inhibition in squamous cell carcinomaNature communications · 2024
Summary (written by the submitters)
Histone H3 lysine (H3K4) methyltransferase KMT2D is a key regulator of gene expression, mainly through promoting H3K4 methylation and activating enhancers, and plays critical roles in development, differentiation, metabolism, and tumor suppression. To investigate the mechanisms by which KMT2D loss promotes HNSCC and to identify potential therapeutic targets, we generated KMT2D wild-type (KMT2D-WT) and KMT2D knock-out (KMT2D-KO) SCC23 HNSCC cells and performed RNA-seq under different glucose conditions.
Published in
Histone-methyltransferase KMT2D deficiency impairs the Fanconi anemia/BRCA pathway upon glycolytic inhibition in squamous cell carcinoma
Liu W, Cao H, Wang J et al. · Nature communications 2024 · PMID 39117659 · doi:10.1038/s41467-024-50861-5
This dataset
Open GSE237454 in finder → Find more human RNA-seq datasets →
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 GSE237454_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 PRJNA994965 and SRA study SRP449623. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39117659) ↗
Study design
2 × (+Glc) KO1 RNAseq exp vs 2 × (+Glc) KO2 RNAseq exp vs 2 × (-Glc) KO1 RNAseq exp vs 2 × (-Glc) KO2 RNAseq exp

Supports a between-group comparison across 8 samples.

How we read this: 4 replicated groups read from 14 sample titles; they account for 8 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
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.