GEO series

Senescence inhibition by rapamycin mitigates radiation-induced atherosclerotic characteristics in human coronary endothelial cells

GSE323983 Homo sapiens (human) RNA-seq (gene expression) 23 samples Submitted 2026/06/24 Platform GPL28975

At a glance

RNA-seq (gene expression) · human · 23 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: 23 samples: 23 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.

Summary (written by the submitters)

Ionising radiation (IR) is a recognised risk factor for cardiovascular disease (CVD), yet the mechanisms linking it to exposure remain incompletely understood. We show that IR drives a pro-atherogenic phenotype in human coronary artery endothelial cells (HCAECs) through the induction of cellular senescence, and that rapamycin attenuates these effects.

Read the full summary ↓

IR triggered hallmark senescence features, including elevated SA-β-galactosidase activity, nuclear enlargement, and increased CDKN1A and p53 expression. Functionally, irradiated HCAECs displayed impaired barrier integrity and heightened monocyte adhesion. Transcriptomic and proteomic profiling revealed broad IR-induced alterations enriched in DNA damage response, cell-cycle arrest, senescence, proteostasis, and immune-related pathways. These findings establish a mechanistic link between radiation-induced endothelial senescence and early atherogenic-associated dysfunction, demonstrating that senescence is a driver of vascular injury. Importantly, identifying mTOR inhibition as a promising strategy to counteract radiation-associated endothelial dysfunction. This work positions senescence as a tractable therapeutic target in radiation-induced vascular injury.

This dataset

Open GSE323983 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 GSE323983_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 PRJNA1432876 and SRA study SRP681243. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

23 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 23 sample titles: 23 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

GSM9566072 HCAEC, Ctrl, 23YO, Male
GSM9566074 HCAEC, Rapa, 23YO, Male
GSM9566076 HCAEC, IR, 23YO, Male
GSM9566078 HCAEC, IR with Rapa, 23YO, Male
GSM9566080 HCAEC, Ctrl, 26YO, Male
GSM9566082 HCAEC, Rapa, 26YO, Male
GSM9566084 HCAEC, IR, 26YO, Male
GSM9566086 HCAEC, IR with Rapa, 26YO, Male
GSM9566087 HCAEC, Ctrl, 54YO, Male
GSM9566089 HCAEC, Rapa, 54YO, Male
GSM9566091 HCAEC, IR, 54YO, Male
GSM9566093 HCAEC, IR with Rapa, 54YO, Male
GSM9566095 HCAEC, Ctrl, 17YO,Female
GSM9566097 HCAEC, Rapa, 17YO, female
GSM9566099 HCAEC, IR with Rapa, 17YO, Female
GSM9566101 HCAEC, Ctrl, 54YO(2), Male
GSM9566103 HCAEC, Rapa, 54YO(2), Male
GSM9566105 HCAEC, IR, 54YO(2), Male
GSM9566107 HCAEC, IR with Rapa, 54YO(2), Male
GSM9566108 HCAEC, Ctrl, 55YO, Female
GSM9566110 HCAEC, Rapa, 55YO, Female
GSM9566112 HCAEC, IR, 55YO, Female
GSM9566114 HCAEC, IR with Rapa, 55YO, Female

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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.