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Computational modeling integrating transcriptomic and vulnerability responses can predict suppressors of cell death as candidate targets for cancer therapy

GSE255459 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2024/09/05 Platform GPL24676
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)
Samples
18 samples: 18 samples, each with a distinct title — no replicated groupsGroups 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
Computational pipeline predicting cell death suppressors as targets for cancer therapyiScience · 2024
Summary (written by the submitters)
Identification of novel target genes for cancer therapy is a significant challenge of biomedical research. Here, we describe a computational pipeline, which integrates transcriptomic and vulnerability responses to cell-death inducing drugs, to predict repressors of cell-death as candidate targets for cancer therapy. The candidate target genes were predicted based on two modules: the transcriptomic similarity and the correlation modules.
Read the full summary ↓
The transcriptomic similarity module identified genes whose targeting results in similar transcriptomic responses of the death-inducing drugs, while the correlation module identified candidate genes whose expression was correlated to the vulnerability to the death-inducing drugs. The combined predictors generated by these two modules were integrated into a single ranked metric. As a proof-of-concept, we selected ferroptosis inducers as death-inducing drugs, and triple negative breast cancer as a cancer model. The pipeline could predict candidate genes as ferroptosis repressors, as demonstrated by computational and experimental validation, including experimental data of 9 representative genes, thus, highlighting the robustness and power of this pipeline. The described pipeline can be used to identify repressors of different cell-death pathways as potential therapeutic targets for various cancer types.
Published in
Computational pipeline predicting cell death suppressors as targets for cancer therapy
Vinik Y, Maimon A, Raj H et al. · iScience 2024 · PMID 39310772 · doi:10.1016/j.isci.2024.110859
This dataset
Open GSE255459 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 GSE255459_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 PRJNA1075062 and SRA study SRP489119. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39310772) ↗
Study design
18 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 18 sample titles: 18 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
Datasets on a similar topic

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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.