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RNA-seq data of MV4-11, Kasumi-1, and KG-1 cells treated with HDAC8 inhibitor 22d 10 μM and control

GSE305655 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2026/04/25 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 18 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
18 samples: 6 conditions, mostly in triplicateGroups 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
HDAC8 inhibition targets STAT3-MYC axis and synergizes with Venetoclax in KMT2A-rearranged acute myeloid leukemiaLeukemia · 2026
Summary (written by the submitters)
We performed RNA sequencing on MV4-11, Kasumi-1, and KG-1 leukemia cell lines treated with HDAC8 inhibitor 22d at 10 μM and their respective control groups. The sequencing reads were processed to generate gene-level count matrices. This dataset provides processed count data for downstream gene expression analysis and comparison between treated and control conditions.
Published in
HDAC8 inhibition targets STAT3-MYC axis and synergizes with Venetoclax in KMT2A-rearranged acute myeloid leukemia
Zhang L, Guo W, Fu YH et al. · Leukemia 2026 · PMID 42014858 · doi:10.1038/s41375-026-02950-1
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 GSE305655_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 PRJNA1307418 and SRA study SRP610037. Searching any of these in the dataset finder brings you back here.

Study design
6 conditions, mostly in triplicate
Kasumi-1 DMSO 12h ×3 Kasumi-1 HDAC8i 10μM 12h ×3 MV4-11 DMSO 12h ×3 MV4-11 HDAC8i 10μM 12h ×3 KG-1 DMSO 12h ×3 KG-1 HDAC8i 10μM 12h ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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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