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Transcription analysis of primary dermal fibroblasts with different genotypes for CTGF rs9399005 SNP after irradiation or sham irradiation

GSE325569 Homo sapiens (human) RNA-seq (gene expression) 51 samples Submitted 2026/06/25 Platform GPL28038
At a glance

RNA-seq (gene expression) · human · 51 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
51 samples: 51 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
Novel method for risk stratification of radiation-induced breast fibrosis: subgroup hypothesis verified by machine learningNPJ breast cancer · 2026
Summary (written by the submitters)
A predictive model for skin fibrosis following radiotherapy in breast cancer patients was previously developed, and the rs9399005 non-coding candidate SNP associated with reduced risk was identified within a high-risk subgroup, determined by the functional RILA assay. The SNP was located downstream of the CTGF gene, but its biological function is poorly understood.
Read the full summary ↓
The purpose of this work was to investigate its functional impact at the transcriptional level. Patient-derived dermal fibroblasts, previously genotyped for the candidate SNP, were obtained from a local biobank. Initially, six fibroblast strains for each major homozygote (CC), heterozygote (CT), and minor homozygote (TT) genotype were cultured to passage 6 and exposed to 4 Gy of irradiation or sham irradiation, but one TT fibroblast was excluded from the study due to poor quality and slow growth. Total cellular RNA was collected before irradiation (day-0) and on day-2 post-treatment for gene expression quantification using RNA-seq. After data trimming with Trimmomatic, pseudo- alignment with Kallisto and data normalization (voom function, limma), differential gene expression (DGE) analysis was carried out using the limma package (FDR<0.05). Pathway enrichment analysis was conducted using the ReactomePA package in R. Results: Few differentially expressed genes (DEGs) were identified at baseline (day-0) between the genotypes (TT vs CC: n=5; TT vs CT: n=2; CT vs CC: n=1). Transcriptome changes on day-2 were assessed by comparing irradiated and untreated samples of each genotype to the untreated CC genotype at baseline (day-0) as a common control. Significant differences in the number of DEGs were observed between fibroblasts of different genotypes (p<0.001, Chi-squared test). The highest number of DEGs on day-2 was found in CC fibroblasts (0 Gy: n=2372, 4 Gy: n=9576), while fibroblasts with the TT genotype showed significantly fewer DEGs (0 Gy: n=523, 4 Gy: n=3538), with CT fibroblasts showing intermediate levels (0 Gy: n=788, 4 Gy: n=5471). Pathway analysis after irradiation revealed similar expected responses across all genotypes, consistent with previous reports. However, pathways related to cell cycle regulation, metabolism, and signal transduction were underrepresented in TT fibroblasts at baseline but overrepresented on day-2 post-irradiation, compared to CC fibroblasts. Distinct genes were leading these under- and over-representations. A significant influence of the candidate SNP on fibroblast responses to irradiation was demonstrated, primarily through the modulation of pathways related to growth and stress regulation. Personalized approaches to managing fibrosis risk may be guided by these findings.
Published in
Novel method for risk stratification of radiation-induced breast fibrosis: subgroup hypothesis verified by machine learning
Sami A, Seibold P, Sticht C et al. · NPJ breast cancer 2026 · PMID 42285996 · doi:10.1038/s41523-026-00980-7
This dataset
Open GSE325569 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 GSE325569_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 PRJNA1440484 and SRA study SRP685531. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42285996) ↗
Study design
51 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 51 sample titles: 51 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

+ 11 more — browse all 51 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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