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      GCNCMI: A Graph Convolutional Neural Network Approach for Predicting circRNA-miRNA Interactions

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          Abstract

          The interactions between circular RNAs (circRNAs) and microRNAs (miRNAs) have been shown to alter gene expression and regulate genes on diseases. Since traditional experimental methods are time-consuming and labor-intensive, most circRNA-miRNA interactions remain largely unknown. Developing computational approaches to large-scale explore the interactions between circRNAs and miRNAs can help bridge this gap. In this paper, we proposed a graph convolutional neural network-based approach named GCNCMI to predict the potential interactions between circRNAs and miRNAs. GCNCMI first mines the potential interactions of adjacent nodes in the graph convolutional neural network and then recursively propagates interaction information on the graph convolutional layers. Finally, it unites the embedded representations generated by each layer to make the final prediction. In the five-fold cross-validation, GCNCMI achieved the highest AUC of 0.9312 and the highest AUPR of 0.9412. In addition, the case studies of two miRNAs, hsa-miR-622 and hsa-miR-149-5p, showed that our model has a good effect on predicting circRNA-miRNA interactions. The code and data are available at https://github.com/csuhjhjhj/GCNCMI.

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          Most cited references41

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          Circular RNAs are a large class of animal RNAs with regulatory potency.

          Circular RNAs (circRNAs) in animals are an enigmatic class of RNA with unknown function. To explore circRNAs systematically, we sequenced and computationally analysed human, mouse and nematode RNA. We detected thousands of well-expressed, stable circRNAs, often showing tissue/developmental-stage-specific expression. Sequence analysis indicated important regulatory functions for circRNAs. We found that a human circRNA, antisense to the cerebellar degeneration-related protein 1 transcript (CDR1as), is densely bound by microRNA (miRNA) effector complexes and harbours 63 conserved binding sites for the ancient miRNA miR-7. Further analyses indicated that CDR1as functions to bind miR-7 in neuronal tissues. Human CDR1as expression in zebrafish impaired midbrain development, similar to knocking down miR-7, suggesting that CDR1as is a miRNA antagonist with a miRNA-binding capacity ten times higher than any other known transcript. Together, our data provide evidence that circRNAs form a large class of post-transcriptional regulators. Numerous circRNAs form by head-to-tail splicing of exons, suggesting previously unrecognized regulatory potential of coding sequences.
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            Natural RNA circles function as efficient microRNA sponges.

            MicroRNAs (miRNAs) are important post-transcriptional regulators of gene expression that act by direct base pairing to target sites within untranslated regions of messenger RNAs. Recently, miRNA activity has been shown to be affected by the presence of miRNA sponge transcripts, the so-called competing endogenous RNA in humans and target mimicry in plants. We previously identified a highly expressed circular RNA (circRNA) in human and mouse brain. Here we show that this circRNA acts as a miR-7 sponge; we term this circular transcript ciRS-7 (circular RNA sponge for miR-7). ciRS-7 contains more than 70 selectively conserved miRNA target sites, and it is highly and widely associated with Argonaute (AGO) proteins in a miR-7-dependent manner. Although the circRNA is completely resistant to miRNA-mediated target destabilization, it strongly suppresses miR-7 activity, resulting in increased levels of miR-7 targets. In the mouse brain, we observe overlapping co-expression of ciRS-7 and miR-7, particularly in neocortical and hippocampal neurons, suggesting a high degree of endogenous interaction. We further show that the testis-specific circRNA, sex-determining region Y (Sry), serves as a miR-138 sponge, suggesting that miRNA sponge effects achieved by circRNA formation are a general phenomenon. This study serves as the first, to our knowledge, functional analysis of a naturally expressed circRNA.
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              Overview of MicroRNA Biogenesis, Mechanisms of Actions, and Circulation

              MicroRNAs (miRNAs) are a class of non-coding RNAs that play important roles in regulating gene expression. The majority of miRNAs are transcribed from DNA sequences into primary miRNAs and processed into precursor miRNAs, and finally mature miRNAs. In most cases, miRNAs interact with the 3′ untranslated region (3′ UTR) of target mRNAs to induce mRNA degradation and translational repression. However, interaction of miRNAs with other regions, including the 5′ UTR, coding sequence, and gene promoters, have also been reported. Under certain conditions, miRNAs can also activate translation or regulate transcription. The interaction of miRNAs with their target genes is dynamic and dependent on many factors, such as subcellular location of miRNAs, the abundancy of miRNAs and target mRNAs, and the affinity of miRNA-mRNA interactions. miRNAs can be secreted into extracellular fluids and transported to target cells via vesicles, such as exosomes, or by binding to proteins, including Argonautes. Extracellular miRNAs function as chemical messengers to mediate cell-cell communication. In this review, we provide an update on canonical and non-canonical miRNA biogenesis pathways and various mechanisms underlying miRNA-mediated gene regulations. We also summarize the current knowledge of the dynamics of miRNA action and of the secretion, transfer, and uptake of extracellular miRNAs.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                05 August 2022
                2022
                : 13
                : 959701
                Affiliations
                [1] 1 School of Computer Science and Engineering , Central South University , Changsha, China
                [2] 2 Department of Electrical Engineering , University of California, San Diego , San Diego, CA, United States
                Author notes

                Edited by: Pingjian Ding, Case Western Reserve University, United States

                Reviewed by: Guanghui Li, East China Jiaotong University, China

                Weidun Xie, City University of Hong Kong, Hong Kong SAR, China

                *Correspondence: Lei Deng, leideng@ 123456csu.edu.cn

                This article was submitted to RNA, a section of the journal Frontiers in Genetics

                Article
                959701
                10.3389/fgene.2022.959701
                9389118
                35991563
                c587c49b-0657-4a96-a8f5-dc42c8d883e5
                Copyright © 2022 He, Xiao, Chen, Zhu, Zhang and Deng.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 02 June 2022
                : 23 June 2022
                Funding
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Award ID: 61972422
                Categories
                Genetics
                Original Research

                Genetics
                circrna,mirna,deep learning,graph convolution neural network,circrna-mirna interaction
                Genetics
                circrna, mirna, deep learning, graph convolution neural network, circrna-mirna interaction

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