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Long Noncoding VIM-AS1: Biomarker of Breast Fibrosis Susceptibility After Radiation Therapy and Promoter of Transforming Growth Factor Beta1–Driven Fibrosis

GSE256420 Homo sapiens (human) RNA-seq (gene expression) 27 samples Submitted 2024/10/24 Platform GPL20301Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 27 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
27 samples: 10 × Fibroblasts from Fibrosis patient vs 9 × Fibroblasts from No Fibrosis patient vs 4 × Scrambled Fibroblasts vs 4 × VIM-AS1 KD FibroblastsGroups 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
Long Noncoding VIM-AS1: Biomarker of Breast Fibrosis Susceptibility After Radiation Therapy and Promoter of Transforming Growth Factor Beta1-Driven FibrosisInternational journal of radiation oncology, biology, physics · 2025
Summary (written by the submitters)
Purpose: Fibrosis is a common late complication of radiation therapy. Molecular dysregulations leading to fibrosis have been characterized for the coding part of the genome, notably those involving the TGFB1 gene network. However, because a large part of the human genome encodes RNA transcripts that are not translated into proteins, exploring the involvement of the noncoding part of the genome in fibrosis susceptibility and development was the aim of this work.
Read the full summary ↓
Methods and materials: Breast cancer patients having or not having developed severe breast fibrosis after radiation therapy were retrospectively selected from the COPERNIC collection. Exome sequencing and RNA-seq transcriptomic profiling were performed on 19 primary dermal fibroblast strains isolated from the patients' nonirradiated skin. Functional experiments were based on fibrogenic induction by transforming growth factor-Beta1 (TGFB1) and gene knockdown in healthy donor fibroblasts. Results: Coding and noncoding transcriptomes discriminated fibrosis from nonfibrosis conditions, and a signature of breast fibrosis susceptibility comprising 15 long noncoding RNAs (lncRNAs) was identified. A hazard ratio validation showed that the lncRNA vimentin antisense long noncoding RNA 1 (VIM-AS1) was the best biomarker associated with fibrosis risk. This lncRNA has not been previously associated with any fibrotic disorder, but we found it upregulated in data sets from cardiac fibrosis and scleroderma, suggesting a general role in tissue fibrosis. Functional experiments demonstrated a profibrotic action of VIM-AS1 because its knockdown reduced myofibroblast activation, collagen matrix production, and dermal organoid contraction. RNA-seq data analysis after VIM-AS1 silencing also pointed out the regulation of replication, cell cycle, and DNA repair. Mechanistically, because VIM-AS1 was found coregulated with the vimentin gene, these data support a profibrotic function of the TGFB1/VIM-AS1/vimentin axis, targeting the dynamics of fibroblast-myofibroblast transition. Conclusions: Noncoding RNA analysis can provide specific biomarkers relevant to the prediction of normal tissue responses after radiation therapy, which opens perspectives of next-generation approaches for treatment, in the frame of the recent developments of RNA-based technologies.
Published in
Long Noncoding VIM-AS1: Biomarker of Breast Fibrosis Susceptibility After Radiation Therapy and Promoter of Transforming Growth Factor Beta1-Driven Fibrosis
Vinasco-Sandoval T, Moratille S, Crechet F et al. · International journal of radiation oncology, biology, physics 2025 · PMID 39436333 · doi:10.1016/j.ijrobp.2024.09.049
This dataset
Open GSE256420 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 GSE256420_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 PRJNA1079387 and SRA study SRP491215. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39436333) ↗
Study design
10 × Fibroblasts from Fibrosis patient vs 9 × Fibroblasts from No Fibrosis patient vs 4 × Scrambled Fibroblasts vs 4 × VIM-AS1 KD Fibroblasts

Supports a case/control comparison: 19 samples read as cases, 4 as controls.

How we read this: 4 replicated groups read from 27 sample titles; they account for 27 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
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