Comprehensive Pan-Cancer Mutation Density Patterns in Enhancer RNA

Abstract

Significant advances have been achieved in understanding the critical role of enhancer RNAs (eRNAs) in the complex field of gene regulation. However, notable uncertainty remains concerning the biology of eRNAs, highlighting the need for continued research to uncover their exact functions in cellular processes and diseases. We present a comprehensive study to scrutinize mutation density patterns, mutation strand bias, and mutation burden in eRNAs across multiple cancer types. Our findings reveal that eRNAs exhibit mutation strand bias akin to that observed in protein-coding RNAs. We also identified a novel pattern, in which mutation density is notably diminished around the central region of the eRNA, but conspicuously elevated towards both the beginning and end. This pattern can be potentially explained by a mechanism involving heightened transcriptional activity and the activation of transcription-coupled repair. The central regions of the eRNAs appear to be more conserved, hinting at a potential mechanism preserving their structural and functional integrity, while the extremities may be more susceptible to mutations due to increased exposure. The evolutionary trajectory of this mutational pattern suggests a nuanced adaptation in eRNAs, where stability at their core coexists with flexibility at their extremities, potentially facilitating their diverse interactions with other genetic entities.

Keywords: enhancer RNA; mutation; mutation density.

Figure 1

(A) Nucleotide content percentage for protein coding TSS regions. (B) Nucleotide content percentage for lncRNA (eRNA not included) regions. (C) Nucleotide content percentage for DNA replication origin regions. (D) Nucleotide content percentage for retrotransposon regions. (E–P) Analysis of nucleotide content percentage for the 12 eRNA sources, using the midpoint of eRNA as the focal point. (Q) Heatmap demonstrating the pair-wise overlapping proportion for the 12 eRNA sources.

Figure 2

LICA-CN mutation density analysis results; eRNA source: all tissue consensuses; focal point: eRNA midpoint. (A) A pie chart depicts the genome-wide mutation count distribution by the six mutation types. (B) A bar depicts the genome-wide strand bias for six mutation types, along with their complementary mutations. (C) A pie chart depicts the mutation count distribution by the six mutation types within the eRNAs. (D) A bar chart depicts the strand bias for six mutation types and their complementary mutations within the eRNAs. (E–J) mutation density patterns for the six mutation types.

Figure 3

MELA-AU mutation density patterns for four skin-related eRNA sources. (A–C) Mutation density patterns when using the all-tissue consensus eRNA source for the start, midpoint and end, respectively. (D–F) Mutation density patterns when using the A375 eRNA source for the start, midpoint and end, respectively. (G–I) Mutation density patterns when using the melanoma eRNA source for the start, midpoint and end, respectively. (J–L) Mutation density patterns when using the melanocyte eRNA source for the start, midpoint and end, respectively.

Figure 4

Mutation density analyses show that a mutation density dip occurs around the midpoint of the eRNAs. (A–F) Mutation density plots using the STAD-US cohort and the eRNAs’ midpoint as the focal point; (G–L) mutation density plots using the SKCM-US cohort and the eRNAs’ midpoint as the focal point; (M–R) mutation density plots using the GBM-US cohort and the eRNAs’ midpoint as the focal point; (S–X) mutation density plots using the LINC-JP cohort and the eRNAs’ midpoint as the focal point.

Figure 5

Mutation density analyses show that a mutation density peak occurs at the start and end of the eRNAs. (A–F) Mutation density plots using the GBM-US cohort and the eRNAs’ start as the focal point; (G–L) mutation density plots using the GBM-US cohort and the eRNAs’ end as the focal point; (M–R) mutation density plots using the LINC-JP cohort and the eRNAs’ start as the focal point; (S–X) mutation density plots using the LINC-JP cohort and the eRNAs’ end as the focal point.