Identification and characteristic analysis of enhancers across 13 major cancer types

Enhancers are often mutated and dysregulated in various diseases such as cancer. By integrating the FANTOM enhancers expression profiles and RNA-seq data from TCGA of 13 cancers and their corresponding para-cancerous tissues, we systematically identified a total of 4702 significantly differentially expressed enhancers (DE enhancers). Furthermore, a total of 1036 differentially expressed genes (DE genes) regulated by differentially expressed enhancers (DE enhancers) were identified. It was found that in these 13 cancers, most (61.13%) enhancers were ubiquitously expressed, whereas DE enhancers were more likely to be tissue-specific expressed, and the DE genes regulated by DE enhancers were significantly enriched in cancer-related pathways. Finally, it was manifested that 74 SNPs located in 37 DE enhancers, and these SNPs affected the gain and loss of functional transcription factor binding sites (TFBS) of 758 transcription factors, which had been shown to be highly correlated with tumorigenesis and development.

Predicting the effects of SNPs on transcription factor binding affinity

GWAS have revealed that 88% of disease associated SNPs reside in noncoding regions. However, noncoding SNPs remain understudied, partly because they are challenging to prioritize for experimental validation. To address this deficiency, we developed the SNP effect matrix pipeline (SEMpl). SEMpl estimates transcription factor binding affinity by observing differences in ChIP-seq signal intensity for SNPs within functional transcription factor binding sites genome-wide. By cataloging the effects of every possible mutation within the transcription factor binding site motif, SEMpl can predict the consequences of SNPs to transcription factor binding. This knowledge can be used to identify potential disease-causing regulatory loci.