GEO series

IRF2 is an Essential Transcription Factor with Pathogenic and Prognostic Impact in Multiple Myeloma

GSE301631 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 16 samples Submitted 2026/07/29 Platform GPL18573

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 16 samples

What it is: ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism: Homo sapiens (human)
Samples: 16 samples: 12 distinct titles, 2 of them repeatedGroups 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: IRF2 is an essential transcription factor with pathogenic and prognostic impact in multiple myelomaBlood · 2026

Summary (written by the submitters)

Multiple Myeloma (MM), the second most prevalent hematologic malignancy, remains incurable, highlighting the need to identify molecular drivers of disease progression and new therapies. Using a CRISPR-Cas9 library screening approach in MM cells, we identified 22 essential transcription factors (TFs), including members of the interferon regulatory factor (IRF) family.

Read the full summary ↓

Remarkably, in addition to the well-known IRF4, IRF2 emerged as a critical TF in MM. Cut&Run experiments demonstrated that IRF2 binds extensively to chromatin, both independently and in cooperation with IRF1 and IRF4. While IRF2-unique regions were predominantly associated with active promoters, regions bound by IRF2/1/4 were biased towards introns. Functionally, IRF2 contributes to MM cell survival by suppressing necroptosis and promoting cell migration. Notably, IRF2-dependent transcriptional dysregulation was evident in precursor conditions such as MGUS and SMM, suggesting a role in early disease evolution. In addition to its role as early factor, IRF2 levels also seem to influence disease progression, as MMs with higher expression showed worse progression-free survival (PFS) and overall survival (OS) in both univariate and multivariate analyses, even after adjusting for common MM genetic risk factors. In conclusion, IRF2 constitutes an underappreciated essential TF involved in the pathogenesis and clinical behavior of MM. Its inhibition leads to dysregulation of key signaling pathways in MM pathogenesis, highlighting its potential as a therapeutic target.

Published in

IRF2 is an essential transcription factor with pathogenic and prognostic impact in multiple myeloma

Gómez-Echarte N, Carrasco-León A, Maiqués-Díaz A et al. · Blood 2026 · PMID 42247308 · doi:10.1182/blood.2025029422

This dataset

Open GSE301631 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE301631_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1285848 and SRA study SRP598091. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42247308) ↗

Study design

12 distinct titles, 2 of them repeated

KMS11_IRF ×3 MM.1R_IRF ×3

How we read this: Read from 16 sample titles: 12 distinct titles with little repetition. 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

GSM9086677 KMS11_IRF2
GSM9086678 KMS11_IRF4
GSM9086679 KMS11_IgG
GSM9086680 KMS11_IRF1
GSM9086681 KMS11_IRF2_2
GSM9086682 KMS11_IRF4_2
GSM9086683 KMS11_BRD4
GSM9086684 KMS11_IRF1_2
GSM9086685 MM.1R_IRF1
GSM9086686 MM.1R_IRF4
GSM9086687 MM.1R_IRF4_2
GSM9086688 MM.1R_IRF2
GSM9086689 MM.1R_IgG
GSM9086690 MM.1R_IRF1_2
GSM9086691 MM.1R_IRF2_2
GSM9086692 MM.1R_BRD4

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