Network based multifactorial modelling of miRNA-target interactions

Abstract

Competing endogenous RNA (ceRNA) regulations and crosstalk between various types of non-coding RNA in human is an important and under-explored subject. Several studies have pointed out that an alteration in miRNA:target interaction can result in unexpected changes due to indirect and complex interactions. In this paper, we defined a new network-based model that incorporates miRNA:ceRNA interactions with expression values and then calculates network-wide effects after perturbation in expression level of element(s) while utilizing miRNA interaction factors such as seed type, binding energy. We have carried out analysis of large scale miRNA:target networks from breast cancer patients. Highly perturbing genes identified by our approach coincide with breast cancer associated genes and miRNAs. Our network-based approach helps unveiling the crosstalk between node elements in miRNA:target network where abundance of targets leading to sponge effect is taken into account. The model has potential to reveal unforeseen and unpredicted regulations which are only evident when considered in network context. Our tool is scalable and can be plugged in with emerging miRNA effectors such as circRNAs, lncRNAs and available as R package ceRNAnet-sim https://www.bioconductor.org/packages/release/bioc/html/ceRNAnetsim.html.