Subtype-specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

Gene expression is tightly controlled by DNA elements called enhancers by associating with lineage-specific transcription factors. These enhancers transcribe non-coding RNAs (called enhancer RNAs or eRNAs). eRNA expression is an early indicator of transcription factor activity and is associated with treatment response and survival in cancer patients. However, the attempts to identify prognostic eRNAs in breast cancers were inadequate, as these studies ignored the heterogenous nature of breast cancers with distinct molecular subtypes. By analysing [~]300,000 eRNA loci profiled using RNA-sequencing datasets from 1,095 breast cancer patients using machine learning approaches, we categorised eRNAs which are specific to breast cancer molecular subtypes and survival. The classified eRNAs were associated with gene pathways related to relevant subtypes. Interestingly, transcription factor analyses highlighted involvement of nuclear receptors other than the estrogen receptor with luminal-specific eRNAs. Basal eRNAs showed association with the transcriptional corepressor TRIM28 and androgen receptor. Luminal eRNAs were associated with better outcomes and Her2 eRNAs with worse outcome in patients. Overall, we demonstrate that machine learning approaches performed on RNA-seq datasets can classify subtype-specific and prognostic eRNAs which can be used to identify critical gene pathways and transcription factor networks in breast cancer.