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

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.

Functional impact of multi-omic interactions in breast cancer subtypes

“…Drago-García et al had already reported lower MI values for these types of links ( Drago-García et al, 2017 ). Despite the threshold attempted to incorporate this difference on the MI, our multi-omic pipeline does not recover miRNA interactions as well as other dedicated methods ( Bose et al, 2022 ).…”

“…Although there is a plethora of statistical approximations ( Huang et al, 2017 ), sparse multivariate methods are arguably nearer to the mechanistic explanation goal, given their capacity to pinpoint potential regulators ( Li et al, 2012 ; Sohn et al, 2013 ; Bose et al, 2022 ). These approaches have even identified potential key regulators for each breast cancer subtype ( Huang et al, 2019 ), and for the subgroups of the triple-negative breast cancer subtype ( Chappell et al, 2021 ).…”

Molecular mechanisms of multi-omic regulation in breast cancer

“…In a pan-cancer, computational study of miRNAs that direct genetic expression, it was observed that miRNA-gene interactions are not conserved, even though there are 22 miRNAs that do function as drivers in different types of cancer. Except for miR-5001 in colorectal cancer and miR-2276 in endometrial cancer, in this study all miRNAs are classified as tumor suppressors and the let-7 family functions as a TSG and as an oncomiR at the same time ( 93 ).…”

The miR-146b-3p/TNFAIP2 axis regulates cell differentiation in acute myeloid leukaemia

“…Currently, the competing endogenous RNA (ceRNA) postulate yielded emerging perspectives for leukaemia investigation [ 12 ]. This hypothesis holds that miRNAs inhibit mRNA transcription or protein translation [ 13 , 14 ]. miRNAs constitute a class of non-coding RNAs that are approximately 20-25 nucleotides in length [ 15 ].…”