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Targeting CERT sensitizes AML Cells to FLT3 Inhibitors through activating GRP78-ATF6-CHOP Axis

GSE280391 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2024/12/01 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 12 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
12 samples: 3 × DMSO vs 3 × HPA-12 vs 3 × Creon vs 3 × ComboGroups 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
Targeting ceramide transfer protein sensitizes AML to FLT3 inhibitors via a GRP78-ATF6-CHOP axisNature communications · 2025
Summary (written by the submitters)
Ceramide Transfer Protein (CERT) determines the ratio of ceramide and Sphingomyelin in cells. Targeting CERT sensitizes solid cancer cells to chemotherapy. However, the role of CERT in AML remains elusive. Knockdown CERT or its inhibitor HPA-12 inhibited growth and promoted apoptosis in AML cells carrying FLT3-ITD mutation. HPA-12 alone or with the FLT3 inhibitor Crenolanib synergistically inhibited proliferation and triggered apoptosis in FLT3-ITD mutated AML cells in vitro and in vivo.
Read the full summary ↓
Interestingly, co-treatment of HPA-12 and Crenolanib is effective in FLT3-ITD+ and FLT3-TKD+ AML patients. The synergistic effects were mediated by the endoplasmic reticulum (ER) stress-GRP78/ATF6/CHOP axis and mitophagy. Our data suggest a possible strategy to enhance the efficacy of FLT3 inhibitors in AML.
Published in
Targeting ceramide transfer protein sensitizes AML to FLT3 inhibitors via a GRP78-ATF6-CHOP axis
Sun X, Li Y, Du J et al. · Nature communications 2025 · PMID 39905002 · doi:10.1038/s41467-025-56520-7
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 GSE280391_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 PRJNA1178294 and SRA study SRP541436. Searching any of these in the dataset finder brings you back here.

Study design
3 × DMSO vs 3 × HPA-12 vs 3 × Creon vs 3 × Combo

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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
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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.