GEO series

Ribosome biogenesis and its regulation by the exportin, CSE1L, are selective 4 dependencies in childhood and young adult acute myeloid leukemia

GSE290584 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2026/02/26 Platform GPL34284

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)
Topic: Acute myeloid leukemiaThis series is part of our curated briefing on it.
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.

Summary (written by the submitters)

Nuclear transport receptors, termed importins and exportins, regulate protein transport between the nucleus and cytoplasm through the nuclear pore. Although genetic alterations in the nuclear pore complex are more common in childhood, adolescent and young adult (AYA) AML, nucleocytoplasmic transport has not been studied extensively in childhood/AYA AML.

Read the full summary ↓

We compared the expression of genes associated with nucleocytoplasmic transport in young and older AML patients and identified enrichment of nucleocytoplasmic transport pathways in childhood/AYA AML. Of the nucleocytoplasmic genes, CSE1L was the top expressed exportin in young AML patients. CSE1L depletion reduced growth, viability and clonogenicity in young (<40 years) AML cell lines and patient samples but spared normal hematopoietic cells. Mechanistically, CSE1L contributed to leukemogenesis by participating in ribosome biogenesis and nuclear export of the pre-ribosomal large subunit (pre-60S). Ribosome biogenesis pathways were the most abundantly expressed gene sets in young AML patients and young primary AML samples demonstrated enhanced rates of ribosome biogenesis and increased sensitivity to ribosome biogenesis inhibitors, compared to older AML samples. Our findings reinforce growing evidence that childhood/AYA AML is a fundamentally different disease to AML in older adults, and is driven by different biologic processes. Through inhibition of CSE1L, or the downstream process it regulates, ribosome biogenesis, we have identified a novel way to target leukemogenesis in childhood/AYA AML.

This dataset

Open GSE290584 in finder → Find more human RNA-seq datasets → Acute myeloid leukemia landscape →

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 GSE290584_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 PRJNA1228668. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

6 conditions, mostly in triplicate

GFP-KD_day1 ×3 shCSE1L_1_day1 ×3 shCSE1L_2_day1 ×3 GFP-KD_day7 ×3 shCSE1L_1_day7 ×3 shCSE1L_2_day7 ×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

GSM8817320 GFP-KD_day1_1 GFP-KD_day1
GSM8817321 GFP-KD_day1_2 GFP-KD_day1
GSM8817322 GFP-KD_day1_3 GFP-KD_day1
GSM8817323 shCSE1L_1_day1_1 shCSE1L_1_day1
GSM8817324 shCSE1L_1_day1_2 shCSE1L_1_day1
GSM8817325 shCSE1L_1_day1_3 shCSE1L_1_day1
GSM8817326 shCSE1L_2_day1_1 shCSE1L_2_day1
GSM8817327 shCSE1L_2_day1_2 shCSE1L_2_day1
GSM8817328 shCSE1L_2_day1_3 shCSE1L_2_day1
GSM8817329 GFP-KD_day7_1 GFP-KD_day7
GSM8817330 GFP-KD_day7_2 GFP-KD_day7
GSM8817331 GFP-KD_day7_3 GFP-KD_day7
GSM8817332 shCSE1L_1_day7_1 shCSE1L_1_day7
GSM8817333 shCSE1L_1_day7_2 shCSE1L_1_day7
GSM8817334 shCSE1L_1_day7_3 shCSE1L_1_day7
GSM8817335 shCSE1L_2_day7_1 shCSE1L_2_day7
GSM8817336 shCSE1L_2_day7_2 shCSE1L_2_day7
GSM8817337 shCSE1L_2_day7_3 shCSE1L_2_day7

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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.