Abstract
Competing endogenous RNA (ceRNA) regulations and crosstalk between various types of non-coding RNA in humans 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 article, we defined a new network-based model that incorporates miRNA:ceRNA interactions with expression values. Our approach calculates network-wide effects of perturbations in the expression level of one or more nodes in the presence or absence of miRNA interaction factors such as seed type, binding energy. We carried out the 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 takes the sponge effect into account and helps to unveil the crosstalk between nodes in miRNA:target network. The model has potential to reveal unforeseen regulations that are only evident in the network context. Our tool is scalable and can be plugged in with emerging miRNA effectors such as circRNAs, lncRNAs, and available as R package ceRNAnetsim: https://www.bioconductor.org/packages/release/bioc/html/ceRNAnetsim.html.
Highlights
MicroRNAs are a family of short non-coding RNAs that are key regulators of gene expression through various post-transcriptional mechanisms (Brennecke et al, 2005)
Basic perturbation calculations using Sample network To assess the effects of expression level changes in Competing endogenous RNA (ceRNA) regulation based on miRNA and target abundance we constructed the Sample network given in Fig. 2 and Table 1
The similar finding was shown in an earlier ceRNA hypothesis model (Ala et al, 2013)
Summary
MicroRNAs (miRNAs) are a family of short non-coding RNAs that are key regulators of gene expression through various post-transcriptional mechanisms (Brennecke et al, 2005). The function of miRNAs is not fully understood, miRNAs predominantly repress their targets. Repressive activities of miRNAs vary depending on many factors that are significant to miRNA:target interactions. These factors include miRNA:target binding energy, binding location in target sequence, base pairing types between miRNA and target, the abundance of miRNAs and targets (Grimson et al, 2007). A proteomics study has shown that characteristics of binding, such as seed pairing type and target site location, drastically affect miRNA function (Xu, Wang & Liu, 2014). Another study has revealed that the length of canonical seed base pairing is correlated with the affinity between miRNA and target (Bosson, Zamudio & Sharp, 2014).
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