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XRN1 deletion induces PKR-dependent cell lethality in interferon-activated cancer cells

GSE248036 Homo sapiens (human) RNA-seq (gene expression) 42 samples Submitted 2024/04/17 Platform GPL21290
At a glance

RNA-seq (gene expression) · human · 42 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
42 samples: 14 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
XRN1 deletion induces PKR-dependent cell lethality in interferon-activated cancer cellsCell reports · 2024
Summary (written by the submitters)
Emerging data suggest that induction of viral mimicry responses through activation of double-stranded RNA (dsRNA) sensors in cancer cells is a promising therapeutic strategy. One approach to induce viral mimicry is to target molecular regulators of dsRNA sensing pathways. Here, we show that the exoribonuclease XRN1 is a negative regulator of the dsRNA sensor protein kinase R (PKR) in cancer cells with high interferon-stimulated gene (ISG) expression.
Read the full summary ↓
XRN1 deletion causes PKR pathway activation and consequent cancer cell lethality. Disruption of interferon signaling with the JAK1/2 inhibitor ruxolitinib can decrease cellular PKR levels and rescue sensitivity to XRN1 deletion. Conversely, interferon-b stimulation can increase PKR levels and induce sensitivity to XRN1 inactivation. Lastly, XRN1 deletion causes accumulation of endogenous complementary sense/anti-sense RNAs, which may represent candidate PKR ligands. Our data demonstrate how XRN1 regulates PKR, and how this interaction creates a vulnerability in cancer cells with an activated interferon cell state.
Published in
XRN1 deletion induces PKR-dependent cell lethality in interferon-activated cancer cells
Zou T, Zhou M, Gupta A et al. · Cell reports 2024 · PMID 38261514 · doi:10.1016/j.celrep.2023.113600
This dataset
Open GSE248036 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 GSE248036_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 PRJNA1041442 and SRA study SRP472467. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38261514) ↗
Study design
14 conditions, mostly in triplicate
HCC366_Day7_control-sg1 ×3 HCC366_Day7_control-sg2 ×3 HCC366_Day7_XRN1-sg1 ×3 HCC366_Day7_XRN1-sg2 ×3 H1650_Day7_control-sg1 ×3 H1650_Day7_control-sg2 ×3 H1650_Day7_XRN1-sg1 ×3 H1650_Day7_XRN1-sg2 ×3 +6 more

Supports a between-group comparison across 42 samples.

How we read this: 14 replicated groups read from 42 sample titles; they account for 42 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

+ 2 more — browse all 42 samples with per-sample file links →

Datasets on a similar topic

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