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Whole Blood Transcriptomics Differentiates Circulating Gene Expression Between Coronary Artery Disease and Peripheral Artery Disease

GSE310095 Homo sapiens (human) RNA-seq (gene expression) 71 samples Submitted 2026/04/17 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 71 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
71 samples: 51 × TH vs 20 × PACEGroups 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
Whole-blood transcriptomics differentiates circulating gene expression between coronary artery disease and peripheral artery diseaseVascular medicine (London, England) · 2026
Summary (written by the submitters)
Coronary artery disease (CAD) and peripheral artery disease (PAD) are prevalent atherosclerotic disorders that exhibit distinct clinical and pathological presentations. We used whole-blood RNA sequencing to investigate circulating transcriptomic differences between PAD and CAD. Whole-blood RNA sequencing was performed in 71 subjects 40-65 years of age with symptomatic PAD (n=20) or CAD (n=51).
Read the full summary ↓
Patients with concomitant PAD and CAD were excluded. Differential expression analysis was performed to compare circulating gene expression in patients with PAD and patients with CAD. We identified 106 genes differentially expressed between PAD and CAD (p adj. < 0.1). Pathway analysis identified upregulation of interferon signaling pathways in CAD and upregulation of neutrophil degranulation and toll-like receptor signaling pathways in PAD. Cell type deconvolution revealed greater fractional abundance of T cell subsets in CAD compared to PAD. Patients with PAD had higher neutrophil-lymphocyte ratio that patients with CAD (3.4 vs. 2.3, p=0.01). In conclusion, PAD and CAD have unique circulating transcriptomes characterized by differences in inflammatory gene expression.
Published in
Whole-blood transcriptomics differentiates circulating gene expression between coronary artery disease and peripheral artery disease
Ni R, Muller M, Ruggles KV et al. · Vascular medicine (London, England) 2026 · PMID 41661212 · doi:10.1177/1358863X251406528
This dataset
Open GSE310095 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 GSE310095_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 PRJNA1365126 and SRA study SRP645363. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41661212) ↗
Study design
51 × TH vs 20 × PACE

Supports a between-group comparison across 71 samples.

How we read this: 2 replicated groups read from 71 sample titles; they account for 71 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

+ 31 more — browse all 71 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.