Computing microRNA-gene interaction networks in pan-cancer using miRDriver

Banabithi Bose,Matthew Moravec,Serdar Bozdag

doi:10.1038/s41598-022-07628-z

Banabithi Bose, Matthew Moravec + Show 1 more

Open Access

https://doi.org/10.1038/s41598-022-07628-z

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Abstract

DNA copy number aberrated regions in cancer are known to harbor cancer driver genes and the short non-coding RNA molecules, i.e., microRNAs. In this study, we integrated the multi-omics datasets such as copy number aberration, DNA methylation, gene and microRNA expression to identify the signature microRNA-gene associations from frequently aberrated DNA regions across pan-cancer utilizing a LASSO-based regression approach. We studied 7294 patient samples associated with eighteen different cancer types from The Cancer Genome Atlas (TCGA) database and identified several cancer-specific and common microRNA-gene interactions enriched in experimentally validated microRNA-target interactions. We highlighted several oncogenic and tumor suppressor microRNAs that were cancer-specific and common in several cancer types. Our method substantially outperformed the five state-of-art methods in selecting significantly known microRNA-gene interactions in multiple cancer types. Several microRNAs and genes were found to be associated with tumor survival and progression. Selected target genes were found to be significantly enriched in cancer-related pathways, cancer hallmark and Gene Ontology (GO) terms. Furthermore, subtype-specific potential gene signatures were discovered in multiple cancer types.

Highlights

DNA copy number aberrated regions in cancer are known to harbor cancer driver genes and the short non-coding RNA molecules, i.e., microRNAs
We integrated copy number aberration (CNA), DNA methylation, transcription factors (TFs)-gene interactions, gene, and miRNA expression datasets in the miRDriver tool to compute miRNA-gene interactions based on DNA copy number aberrated regions in eighteen different cancer types from The Cancer Genome Atlas (TCGA)
We developed a computational pipeline called miRDriver, which integrates multi-omics datasets such as CNA, DNA methylation, TFs, gene, and miRNA expression to infer copy number-derived miRNA-gene interactions in cancer

Summary

Introduction

DNA copy number aberrated regions in cancer are known to harbor cancer driver genes and the short non-coding RNA molecules, i.e., microRNAs. In this study, we integrated the multi-omics datasets such as copy number aberration, DNA methylation, gene and microRNA expression to identify the signature microRNA-gene associations from frequently aberrated DNA regions across pan-cancer utilizing a LASSO-based regression approach. We studied 7294 patient samples associated with eighteen different cancer types from The Cancer Genome Atlas (TCGA) database and identified several cancer-specific and common microRNA-gene interactions enriched in experimentally validated microRNA-target interactions. We highlighted several oncogenic and tumor suppressor microRNAs that were cancer-specific and common in several cancer types. Our method substantially outperformed the five state-of-art methods in selecting significantly known microRNA-gene interactions in multiple cancer types. Several studies found miRNAs to be the regulators of cancer driver genes that promote tumor initiation, progression and proliferation[2–4]. Several state-of-the-art methods utilize miRNA and gene expression data to infer miRNA-gene regulatory networks. idaFast and jointIDA use invariant causal relationships, i.e., direct or indirect effects of miRNAs on targets to infer miRNA-gene regulatory networks

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