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Exosomal circ_0006896 promotes AML progression via interaction with HDAC1 and restriction of antitumor immunity

GSE285301 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2024/12/31 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 16 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
16 samples: 6 × AML vs 4 × healthy vs 3 × molm-13 NC-circ vs 3 × molm-13 sh-circGroups 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
Exosomal circ_0006896 promotes AML progression via interaction with HDAC1 and restriction of antitumor immunityMolecular cancer · 2025
Summary (written by the submitters)
Drug resistance and immune escape continue to contribute to poor prognosis in AML. Increasing evidence suggests that exosomes play a crucial role in AML immune microenvironment. We aimed to find a functional exosomal circRNAs/lncRNA/mRNA correlating with the progression of AML patients and further analyze its underlying mechanism in AML cells and tumor microenvironment immune cells.
Read the full summary ↓
We find a new crucial exosomal circRNA, circ_0006896, is upregulated in both AML cells and exosomes and correlates with the prognosis and relapse of AML. In vitro and in vivo studies suggest that circ_0006896 significantly promotes AML cell proliferation, reduces chemotherapy sensitivity, and more importantly, impairs the efficacy of adoptive T cell-transfer immunotherapy.
Published in
Exosomal circ_0006896 promotes AML progression via interaction with HDAC1 and restriction of antitumor immunity
Can C, Yang X, Jia H et al. · Molecular cancer 2025 · PMID 39762891 · doi:10.1186/s12943-024-02203-8
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 GSE285301_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 PRJNA1202189 and SRA study SRP553436. Searching any of these in the dataset finder brings you back here.

Study design
6 × AML vs 4 × healthy vs 3 × molm-13 NC-circ vs 3 × molm-13 sh-circ

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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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