Computational prediction and analysis of histone H3k27me1-associated miRNAs

Abstract

The mono-methylation of histone H3 on lysine 27 (H3K27me1) plays key roles in the cellular processes. The H3K27me1 interacts with the DNA sequence of the miRNAs and regulates the transcription of miRNAs. Therefore, biological roles of the H3K27me1 are closely related to the downstream miRNAs. We proposed a machine learning-based computational method to predict H3K27me1-associated miRNAs and obtained AUCs of 0.6866 and 0.6849 on the leave-one-out and five-fold cross validation, respectively. We also performed enrichment analysis of the transcript factors, GO terms and pathways of H3K27me1-associated miRNAs. Among the top 10 significantly enriched transcription factors, five were unfavorable prognostic marker in renal cancer. The enrichment analysis of molecular function showed that the H3K27me1-associated miRNAs were linked to RNA binding and protein binding which were involved in the transcription and translation regulation. The enrichment of pathway showed that H3K27me1-associated miRNAs were mainly involved in pathways related to cancers, signaling and virus.