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. 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. 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. 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.