Multi‐omics analysis reveals the functional transcription and potential translation of enhancers

Abstract

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 showed 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.

Integrating the Epigenome and Transcriptome of Hepatocellular Carcinoma to Identify Systematic Enhancer Aberrations and Establish an Aberrant Enhancer-Related Prognostic Signature

“…There are still several potential limitations. First of all, RNA-seq is still commonly used in the literature ( Chen et al, 2018a ; Wu et al, 2020b ; Chen and Liang, 2020 ) but is not one of the best choices for the comprehensive detection of eRNA; for example, GRO-seq would be a better approach ( Danko et al, 2015 ; Zhang et al, 2020c ). Second, aberrant genic enhancers might be only partially captured by identifying genic DMEs, especially considering the relatively low ratio of DNA methylation-associated DEEs in total intergenic DEEs.…”

Comprehensive characterization of enhancer RNA in hepatocellular carcinoma reveals three immune subtypes with implications for immunotherapy

“…A previously published pipeline, Pipeline for Enhancer Transcription (PET) ( http://fun-science.club/PET/ ), was adopted for eRNA identification and quantification in our study. 64 Briefly, the trimmed fastq reads were aligned to the hg19 reference genome using STAR, followed by quantification of eRNA using featureCounts with an eRNA annotation file from PET. We calculated reads per kilobase of transcript per million mapped reads (RPKM) values from raw count data and filtered out eRNAs whose length was less than 50 bp to avoid mismatches and multi-mapped transcripts, as reported previously.…”

“…We calculated reads per kilobase of transcript per million mapped reads (RPKM) values from raw count data and filtered out eRNAs whose length was less than 50 bp to avoid mismatches and multi-mapped transcripts, as reported previously. 64 The detected eRNAs were defined as RPKM > 0 in more than 10% of tumor or normal samples and subjected to analyses. The coordinate distribution of eRNAs was illustrated by the R circlize package.…”

Multi-omics analysis reveals the genetics and immune landscape of dexamethasone responsive genes in cancer microenvironment

“…We analyzed RNA-seq data cancers with matched normal samples in The Cancer Genome Atlas (THCA, KIRP, LIHC, STAD, BRCA, COAD, UCEC, BLCA, KIRC, KICH, and PRAD). We performed the differential expression analysis based on the count data by using DESeq2 (14) as is described before (15,16). Genes with fold change over two and P value less than 0.05 were considered as the significantly differentially expressed genes.…”