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A nonenzymatic dependency on inositol-requiring enzyme 1 controls cancer cell cycle progression and tumor growth

GSE285981 Homo sapiens (human) RNA-seq (gene expression) 48 samples Submitted 2025/01/07 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 48 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
48 samples: 16 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
Interferon regulatory factor 4 mediates nonenzymatic IRE1 dependency in multiple myeloma cellsPLoS biology · 2025
Summary (written by the submitters)
Endoplasmic-reticulum resident inositol-requiring enzyme 1a (IRE1) supports protein homeostasis via its cytoplasmic kinase-RNase module. Known cancer dependency on IRE1 entails its enzymatic activation of the transcription factor XBP1s and of regulated RNA decay. We discovered that some cancer cells surprisingly require IRE1 but not its enzymatic activity.
Read the full summary ↓
IRE1 knockdown but not enzymatic IRE1 inhibition or XBP1 disruption attenuated cell cycle progression and tumor growth. IRE1 silencing led to activation of TP53 and CDKN1A/p21 in conjunction with increased DNA damage and chromosome instability, while decreasing heterochromatin as well as DNA and histone H3K9me3 methylation. Immunoelectron microscopy detected endogenous IRE1 at the nuclear envelope. Thus, cancer cells co-opt IRE1 either enzymatically or nonenzymatically, which has significant implications for IRE1’s biological role and therapeutic targeting.
Published in
Interferon regulatory factor 4 mediates nonenzymatic IRE1 dependency in multiple myeloma cells
Oikonomidi I, Kameswaran V, Pham VC et al. · PLoS biology 2025 · PMID 40215234 · doi:10.1371/journal.pbio.3003096
This dataset
Open GSE285981 in finder → Find more human RNA-seq 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 GSE285981_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 PRJNA1207135 and SRA study SRP555631. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40215234) ↗
Study design
16 conditions, mostly in triplicate
shIRE1.cl1.day0 ×3 shIRE1.cl1.day24 ×3 shIRE1.cl1.day48 ×3 shIRE1.cl1.day72 ×3 shIRE1.cl3.day0 ×3 shIRE1.cl3.day24 ×3 shIRE1.cl3.day48 ×3 shIRE1.cl3.day72 ×3 +8 more

Supports a between-group comparison across 48 samples.

How we read this: 16 replicated groups read from 48 sample titles; they account for 48 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

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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