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A novel translational in-silico indication discovery framework identifies indications and predictive biomarkers for cenerimod

GSE293404 Mus musculus (mouse) RNA-seq (gene expression) 72 samples Submitted 2025/05/31 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 72 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
Mus musculus (mouse)
Samples
72 samples
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.
Summary (written by the submitters)
To fully explore a drug candidate's therapeutic potential, assessing its effectiveness across all possible diseases is essential. While traditional approaches match drug mechanisms to disease pathophysiology, they are limited by the high costs and slow progress of preclinical and clinical trials. This study introduces a novel in silico framework to identify new indications for drug candidates or repurpose approved drugs by analyzing their effects on gene expression in patients or animal models compared to controls.
Read the full summary ↓
The framework integrate data from 13,602 patient samples across 146 diseases with drug candidate tested in preclinical models and use a neural network to reduce noise and improve sensitivity. The framework was exemplified with cenerimod, a S1P1 receptor modulator, which predicted its efficacy in immune-related diseases such as SLE, Psoriasis, and Crohn’s disease and kidney transplantation complications. Additionally, it identified six genes predictive of maximal clinical response in SLE patients, validated using RNA-seq data from a phase 2b cenerimod trial.
This dataset
Open GSE293404 in finder → Find more mouse 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 GSE293404_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 PRJNA1244372 and SRA study SRP575490. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Samples in this study

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+ 72 more — browse all 72 samples with per-sample file links →

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