Int J Cancer. 2020 Jun 23.
Yingcheng Wu,
Yang Yang,
Hongyan Gu,
Baorui Tao,
Erhao Zhang,
Jinhuan Wei,
Zhou Wang,
Aifen Liu,
Rong Sun,
Miaomiao Chen,
Yihui Fan,
Renfang Mao.
Enhancer can transcribe RNAs, however, most of them were neglected in traditional RNA-seq analysis workflow. Here, we developed a Pipeline for Enhancer Transcription (PET, http://fun-science.club/PET) for quantifying enhancer RNAs (eRNAs) from RNA-seq. By applying this pipeline on lung cancer samples and cell lines, we show that the transcribed enhancers are enriched with histone marks and transcription factor motifs (JUNB, Hand1-Tcf3, and GATA4). By training a machine learning model, we demonstrate that enhancers can predict prognosis better than their nearby genes. Integrating the Hi-C, ChIP-seq, and RNA-seq data, we observe that transcribed enhancers associate with cancer hallmarks or oncogenes, among which LcsMYC-1 (Lung cancer-specific MYC eRNA-1) potentially supports MYC expression. Surprisingly, a significant proportion of transcribed enhancers contain small protein-coding open reading frames (sORFs) and can be translated into microproteins. Our study provides a computational method for eRNA quantification and deepens our understandings of the DNA, RNA, and protein nature of enhancers. This article is protected by copyright. All rights reserved.
Keywords: Enhancer RNA; eRNA pipeline; sORF; transcription factor