← BioTransfer GEO Dataset Finder
GEO series

Doxorubicin resistance involves modulation of interferon signaling, transcriptional bursting, and gene co-expression patterns of U-ISGF3-related genes

GSE275330 Homo sapiens (human) Single-cell RNA-seq 10 samples Submitted 2024/10/09 Platform GPL34284Platform GPL24676
At a glance

Single-cell RNA-seq · human · 10 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
10 samples: 3 × HCT_mRNA_UTR vs 3 × HCT_mRNA_DOX vs 2 × scRNA_HCT_UTR vs 2 × scRNA_HCT_DOXGroups 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
Doxorubicin resistance involves modulation of interferon signaling, transcriptional bursting, and gene co-expression patterns of U-ISGF3-related genesNeoplasia (New York, N.Y.) · 2024
Summary (written by the submitters)
we investigate the transcriptomic alterations induced by doxorubicin (DOX), a commonly used chemotherapeutic agent, in human colon cancer cells. Using single-cell RNA sequencing, we identified distinct cell populations and their transcriptional profiles following subtoxic dose DOX treatment, revealing cell clusters characterized by differential expression of genes involved in cell cycle regulation and interferon (IFN) signaling.
Read the full summary ↓
We observed that expression of JAK/STAT pathway genes, activated by IFNs, did not correlate with expression of senescence markers. In contrast, DOX-persisting proliferating cells exhibited upregulation of genes induced by the unphosphorylated form of ISGF3 (U‐ISGF3) transcription factor. Further analysis revealed that U‐ISGF3 drives the expression of target genes by modulating the number of mRNA produced per transcriptional burst, potentially contributing to chemoresistance. Importantly, we found high upregulation of HSH2D, a poor prognostic marker, in doxorubicin-resistant U-ISGF3-dependent cells. Moreover, analysis of gene co-expression networks demonstrated that the altered network in DOX-treated cells, rather than individual gene expression levels, may dictate cancer cell fate after chemotherapy.
Published in
Doxorubicin resistance involves modulation of interferon signaling, transcriptional bursting, and gene co-expression patterns of U-ISGF3-related genes
Trzaskoma P, Jung S, Kanno Y et al. · Neoplasia (New York, N.Y.) 2024 · PMID 39405604 · doi:10.1016/j.neo.2024.101071
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 GSE275330_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1150261 and SRA study SRP527643. Searching any of these in the dataset finder brings you back here.

Study design
3 × HCT_mRNA_UTR vs 3 × HCT_mRNA_DOX vs 2 × scRNA_HCT_UTR vs 2 × scRNA_HCT_DOX

Supports a between-group comparison across 10 samples.

How we read this: 4 replicated groups read from 10 sample titles; they account for 10 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
Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.