Frontiers in GeneticsBioinformaticsPeer-reviewed journal article
Genome-wide Identification and Analysis of Enhancer Regulated microRNAs Across 31 Human Cancers
Fei TangiD, Yin ZhangiD, Qingqing HuangiD, Mingming QianiD, Zhixue LiiD, Yanjing LiiD, Bei-Ping Li, Zhengliang Qiu, Junjie YueiD, Zhiyun GuoiD
Abstract
Enhancers are cis-regulatory DNA elements that positively regulate the transcription of target genes in a tissue-specific manner and dysregulation in various diseases such as cancer. Recent studies showed that enhancers can regulate miRNAs and participate in the biological synthesis of miRNAs. However, the network of enhancer-regulated miRNAs across multiple cancers is still unclear. Here, a total of 2,418 proximal enhancer-miRNA interactions and 1,280 distal enhancer-miRNA interactions were identified through the integration of genomic distance, co-expression, and 3D genome data in 31 cancers. The results showed that both proximal and distal interactions exhibited significant tissue-specific feature and there was a noteworthy positive correlation between the expression of miRNA and the number of regulated enhancers in most tissues. Furthermore, it was found that there was a high correlation between the formation of enhancer-miRNA pairs and the expression of eRNAs whether in distal or proximal regulation. The characteristics analysis showed that miRes (enhancers that regulated miRNAs) and non-miRes presented significant differences in sequence conservation, GC content and histone modification signatures. Notably, GC content, H3K4me1, H3K36me3 were present differently between distal regulation and proximal regulation, suggesting they might participate in chromosome looping of enhancer-miRNA interactions. Finally, we introduced a case study, enhancer: chr1:1186391-1186507[~]miR-200a was highly relevant to the survival of thyroid cancer patients and a cis-eQTL SNP on enhancer affected the expression TNFRSF18 gene as a tumor suppressor.
- organism: human
- assay: DNA methylation
- assay: ChIP-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 8
- FWCI
- 0.43
- Field percentile
- 48th percentile
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