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

Abstract

To understand driving biological factors for 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, collective regulation, and groupwise effect of ceRNAs are not considered. In this study, we developed a computational pipeline named Crinet to infer cancer-associated ceRNA networks addressing critical drawbacks. Crinet considers lncRNAs, pseudogenes, and all mRNAs as potential ceRNAs and incorporates a network deconvolution method to exclude the amplifying effect of ceRNA pairs in terms of ceRNA regulation. We tested Crinet on breast cancer data in TCGA. Crinet inferred reproducible ceRNA interactions and groups, which were significantly enriched in cancer-related genes and processes. We validated our results using protein expression benchmarks. Crinet outperformed other tool predicting gene expression change in knockdown assays, and showed better performance for antibodies following miRNA transfection. Top high-degree genes in the inferred network included known suppressor/oncogene lncRNAs of breast cancer showing the importance of non-coding RNA's inclusion for ceRNA inference. Materials and MethodsCrinet is a computational tool to infer cancer-associated ceRNA network genome-wide with pairwise and groupwise interactions. Briefly, the first step is data preparation. The second step is computing miRNA-target interactions integrating with expression datasets and considering sufficient abundance. Starting with final miRNA-target 2/16

SUPREME: A cancer subtype prediction methodology integrating multiomics data using Graph Convolutional Neural Network

“…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 [45]. In our recent work, we inferred ceRNA interactions in breast cancer [46]. 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 based multifactorial modelling of miRNA-target interactions

“…Such ncRNAs play role in the regulation of crucial genes. For instance, critical genes extracted from a study involving lncRNA-miRNA-mRNA interactions overlapped with genes important for cancer (Kesimoglu & Bozdag, 2020). More specifically, the involvement of lncRNA-miRNA-mRNA interactions with breast cancer and the role of circRNA-miRNA-mRNA interactions in cervical cancer were shown in recent studies (Tuersong et al, 2019;Wang et al, 2020;Gong et al, 2019).…”