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Differential Gene Expression related to Telomere Length in Arterial Wall Tissues and Granulocytes of Patients with Coronary Artery Disease

GSE289957 Homo sapiens (human) RNA-seq (gene expression) 34 samples Submitted 2026/06/03 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 34 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
34 samples: 9 × ArteryTL:long, BloodTL:long vs 9 × ArteryTL:short, BloodTL:short vs 8 × ArteryTL:short, BloodTL:long vs 8 × ArteryTL:long, BloodTL:shortGroups 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
Arterial Tissue Gene Expression Associated With Telomere Length Attrition in Coronary Artery DiseaseJournal of lipid and atherosclerosis · 2026
Summary (written by the submitters)
Shortened telomere length (TL) in blood cells is associated with atherosclerotic coronary artery disease (CAD). However, the mechanistic pathways underlying TL attrition in arterial wall tissues for patients with CAD remain unclear. In this study, we evaluated TL in arterial wall tissues and granulocytes and correlated these measurements with data on gene expressions in the arterial wall tissues of patients with CAD.
Published in
Arterial Tissue Gene Expression Associated With Telomere Length Attrition in Coronary Artery Disease
Lau ZC, Naaz A, Muniasamy U et al. · Journal of lipid and atherosclerosis 2026 · PMID 42211145 · doi:10.12997/jla.2026.15.2.297
This dataset
Open GSE289957 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 GSE289957_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 PRJNA1225647 and SRA study SRP564951. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42211145) ↗
Study design
9 × ArteryTL:long, BloodTL:long vs 9 × ArteryTL:short, BloodTL:short vs 8 × ArteryTL:short, BloodTL:long vs 8 × ArteryTL:long, BloodTL:short

Supports a between-group comparison across 34 samples.

How we read this: 4 replicated groups read from 34 sample titles; they account for 34 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

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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.