Transcriptomic Profile of Breast Tissue of Premenopausal Women Following Treatment with Progesterone Receptor Modulator: Secondary Outcomes of a Randomized Controlled Trial.
Deborah UtjésNageswara Rao BoggavarapuMohammed Fatih RasulIsabelle KobergAlexander ZulligerParameswaran Grace Luther LalitkumarCarolina von GrothusenParameswaran Grace LalitkumarKiriaki PapaikonomouTwana AlkasaliasKristina Gemzell-DanielssonPublished in: International journal of molecular sciences (2024)
Progesterone receptor antagonism is gaining attention due to progesterone's recognized role as a major mitogen in breast tissue. Limited but promising data suggest the potential efficacy of antiprogestins in breast cancer prevention. The present study presents secondary outcomes from a randomized controlled trial and examines changes in breast mRNA expression following mifepristone treatment in healthy premenopausal women. We analyzed 32 paired breast biopsies from 16 women at baseline and after two months of mifepristone treatment. In total, 27 differentially expressed genes were identified, with enriched biological functions related to extracellular matrix remodeling. Notably, the altered gene signature induced by mifepristone in vivo was rather similar to the in vitro signature. Furthermore, this gene expression signature was linked to breast carcinogenesis and notably linked with progesterone receptor expression status in breast cancer, as validated in The Cancer Genome Atlas dataset using the R2 platform. The present study is the first to explore the breast transcriptome following mifepristone treatment in normal breast tissue in vivo, enhancing the understanding of progesterone receptor antagonism and its potential protective effect against breast cancer.
Keyphrases
- gene expression
- breast cancer risk
- genome wide
- polycystic ovary syndrome
- single cell
- dna methylation
- immune response
- pregnant women
- high throughput
- metabolic syndrome
- machine learning
- transcription factor
- pregnancy outcomes
- skeletal muscle
- copy number
- squamous cell carcinoma
- deep learning
- protein kinase
- drug induced
- data analysis