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Drug Repurposing Using Molecular Network Analysis Identifies Janus Kinase as Targetable Driver in Necrobiosis Lipoidica

GSE267132 Homo sapiens (human) RNA-seq (gene expression) 22 samples Submitted 2024/05/15 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 22 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
22 samples: 22 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
Drug Repurposing Using Molecular Network Analysis Identifies Jak as Targetable Driver in Necrobiosis LipoidicaJID innovations : skin science from molecules to population health · 2024
Summary (written by the submitters)
Drug repurposing is an attractive strategy for therapy development, particularly in rare diseases where traditional drug development approaches may be challenging due to high cost and small numbers of patients. Here we used a novel drug identification and repurposing pipeline to identify candidate targetable drivers of disease and corresponding therapies through application of causal reasoning using a combination of open-access resources and transcriptomics data.
Read the full summary ↓
We optimized our approach on psoriasis as a disease model, demonstrating the ability to identify known and novel molecular drivers of psoriasis and link them to current and emerging therapies. Application of our approach to a cohort of tissue samples of necrobiosis lipoidica, an unrelated, rare, and to date molecularly poorly characterized cutaneous inflammatory disorder, identified a unique set of upstream regulators, particularly highlighting the role of IFNG and the JAK-STAT pathway as a likely driver of disease pathogenesis and linked it to JAK inhibitors as potential therapy. Analysis of an independent cohort of NL samples validated these findings with the overall agreement of drug matched upstream regulators above 96%. These data highlight utility of our approach in rare diseases and offer a novel opportunity for drug discovery in other rare diseases in dermatology and beyond.
Published in
Drug Repurposing Using Molecular Network Analysis Identifies Jak as Targetable Driver in Necrobiosis Lipoidica
Hughes AN, Li X, Lehman JS et al. · JID innovations : skin science from molecules to population health 2024 · PMID 39391813 · doi:10.1016/j.xjidi.2024.100296
This dataset
Open GSE267132 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 GSE267132_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 PRJNA1109817 and SRA study SRP506834. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39391813) ↗
Study design
22 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 17 samples read as cases, 5 as controls.

How we read this: Read from 22 sample titles: 22 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.