Crinet: A computational tool to infer genome-wide competing endogenous RNA (ceRNA) interactions

Abstract

To understand driving biological factors for complex diseases like cancer, regulatory circuity of genes needs to be discovered. Recently, a new gene regulation mechanism called competing endogenous RNA (ceRNA) interactions has been discovered. Certain genes targeted by common microRNAs (miRNAs) “compete” for these miRNAs, thereby regulate each other by making others free from miRNA regulation. Several computational tools have been published to infer ceRNA networks. In most existing tools, however, expression abundance sufficiency, collective regulation, and groupwise effect of ceRNAs are not considered. In this study, we developed a computational tool named Crinet to infer genome-wide ceRNA networks addressing critical drawbacks. Crinet considers all mRNAs, lncRNAs, and pseudogenes as potential ceRNAs and incorporates a network deconvolution method to exclude the spurious ceRNA pairs. We tested Crinet on breast cancer data in TCGA. Crinet inferred reproducible ceRNA interactions and groups, which were significantly enriched in the cancer-related genes and processes. We validated the selected miRNA-target interactions with the protein expression-based benchmarks and also evaluated the inferred ceRNA interactions predicting gene expression change in knockdown assays. The hub genes in the inferred ceRNA network included known suppressor/oncogene lncRNAs in breast cancer showing the importance of non-coding RNA’s inclusion for ceRNA inference. Crinet-inferred ceRNA groups that were consistently involved in the immune system related processes could be important assets in the light of the studies confirming the relation between immunotherapy and cancer. The source code of Crinet is in R and available at https://github.com/bozdaglab/crinet.

SUPREME: multiomics data integration using graph convolutional networks

“…In addition to multiomics datatypes, there are some regulatory relations such as competing endogenous RNA (ceRNA) regulation, which has been recently discovered with important insights into cancer ( 43 ). In our recent work, we inferred ceRNA interactions in breast cancer ( 44 ). To adopt this kind of regulatory relations, SUPREME could be improved to utilize patient similarity networks based on gene regulatory interactions and more complex patient relations.…”

Network Approaches to Study Endogenous RNA Competition and Its Impact on Tissue-Specific microRNA Functions

“…It then applies sensitivity correlation coefficients that consider the role of more than one miRNA in a competing interaction between two target genes. Crinet (the ceRNA interaction network) aims to identify genome-wide ceRNA networks, trying to determine the drawbacks of existing methods [ 67 ]. Finally, LaceModule attempts to identify competing endogenous RNA modules based on the integration of the conventional Pearson correlation coefficient with a dynamic correlation measure called liquid association.…”

Decoding dynamic miRNA:ceRNA interactions unveils therapeutic insights and targets across predominant cancer landscapes

“…As a result, miRNA:ceRNA interaction networks have been established by integrated analysis of co-expressed RNAs that are negatively correlated with expression of shared miRNAs, usually based on differential expression analysis, thus elucidating disease- or tissue-specific ceRNA functions and critical players [ 9 , 15 , 62 ]. Several statistical methods have also been suggested for detecting ceRNA interactions based on various correlation methods (i.e., sparse partial correlation (SPC), sum correlation, correlation-based network, and dynamic correlation) [ 20 , 23 , 31 , 52 ]. As a result, databases and distinct web interfaces providing ceRNA interactions in cancer tissues have been suggested [ 19 , 48 , 50 ].…”