Cancerin: A computational pipeline to infer cancer-associated ceRNA interaction networks

Abstract

MicroRNAs (miRNAs) inhibit expression of target genes by binding to their RNA transcripts. It has been recently shown that RNA transcripts targeted by the same miRNA could “compete” for the miRNA molecules and thereby indirectly regulate each other. Experimental evidence has suggested that the aberration of such miRNA-mediated interaction between RNAs—called competing endogenous RNA (ceRNA) interaction—can play important roles in tumorigenesis. Given the difficulty of deciphering context-specific miRNA binding, and the existence of various gene regulatory factors such as DNA methylation and copy number alteration, inferring context-specific ceRNA interactions accurately is a computationally challenging task. Here we propose a computational method called Cancerin to identify cancer-associated ceRNA interactions. Cancerin incorporates DNA methylation, copy number alteration, gene and miRNA expression datasets to construct cancer-specific ceRNA networks. We applied Cancerin to three cancer datasets from the Cancer Genome Atlas (TCGA) project. Our results indicated that ceRNAs were enriched with cancer-related genes, and ceRNA modules in the inferred ceRNA networks were involved in cancer-associated biological processes. Using LINCS-L1000 shRNA-mediated gene knockdown experiment in breast cancer cell line to assess accuracy, Cancerin was able to predict expression outcome of ceRNA genes with high accuracy.

miRDriver: A Tool to Infer Copy Number Derived miRNA-Gene Networks in Cancer

“…To evaluate the selected miRNA-gene interactions by miRDriver, we use the list of experimentally-verified miRNA-gene interactions used in [34] as our ground truth. Since miRDriver could identify indirect downstream targets (i.e., target of a direct target) as well as direct targets, we included potential indirect targets to the ground truth dataset.…”

LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer

“…Graph clustering-based strategy [12][13][14][15][16][17] is an alternative approach to identifying lncRNA related miRNA sponge modules. As there is no graph clustering-based strategy specifically designed for finding lncRNA related miRNA sponge modules, so we create a baseline Graph…”

“…It is commonly known that to implement a specific biological function, genes tend to cluster or connect in the form of modules or communities. Consequently, based on the identified lncRNA related miRNA sponge interaction networks, several methods [12][13][14][15][16][17] using graph clustering algorithms were developed to identify lncRNA related miRNA sponge modules. For the identification of sponge lncRNA-mRNA pairs, most of existing methods only consider pair-wise correlation of them.…”

LAceModule: Identification of Competing Endogenous RNA Modules by Integrating Dynamic Correlation

“…And List et al (2019) further improved this method. All these methods predict ceRNA pairs based on two aspects: (1) ceRNAs should share a sufficient number of microRNAs, which can be evaluated through statistical tests, such as the hyper-geometric test (Salmena et al, 2011;Le et al, 2017); and (2) the expression of ceRNAs should be positively correlated, which can be estimated using the PCC (Chiu et al, 2015), SI (Paci et al, 2014;Do and Bozdag, 2018), or conditional mutual information (Sumazin et al, 2011). In addition, Zhang et al (2018) proposed LncmiRSRN to construct lncRNA-mRNA ceRNA network via estimating the causal effects of lncRNAs on mRNAs with the IDA method.…”