Editorial: Computational Identification of ceRNA Regulation

Computational Identification of ceRNA RegulationIn molecular biology, gene regulation is a fundamental biological process essential to organisms.Generally, there are two broad levels of gene regulation: transcriptional and post-transcriptional control.In gene regulation, the competing endogenous RNA (ceRNA) regulation (Salmena et al., 2011) mediated by microRNAs (miRNAs) is one of the most commonly studied mechanisms.At both transcriptional and post-transcriptional levels, ceRNA regulation has been shown to be involved in many biological processes, including the initiation and progression of human cancers (Tay et al., 2014).As a novel layer of gene regulation, ceRNA regulation is higher than miRNA regulation in terms of breadth, precision, and complexity (Smillie et al., 2018).Of the many types of ceRNAs, the four most widely investigated are long non-coding RNAs (lncRNAs), pseudogenes, circular RNAs (circRNAs), and messenger RNAs (mRNAs).Heretofore, numerous studies (Tay et al., 2014;Qi et al., 2015;Wang et al., 2016;Misir et al., 2020) have revealed that ceRNAs can act as potential diagnostic biomarkers in clinical applications.In terms of cost, efficiency, and time consumption, computational methods are useful to guide biological experiments in many areas of biology, and help us derive novel biological insights (Lloyd, 2000; Editors of Nature Methods, 2021).With regard to ceRNA regulation, computational methods have been demonstrated to greatly reduce the time and cost of biological experiments (Le et al., 2017;Li et al., 2018;List et al., 2019;Zhang et al., 2022).Novel computational methods or tools are being presented to shortlist high-confidence ceRNAs for subsequent biological experiments.It is expected that the development of computational methods or tools will drive novel biological insights into the study of ceRNA regulation, and further speed up the research on ceRNA (Figure 1).This Research Topic of Frontiers in Molecular Biosciences features a collection of Research articles on the computational or in silico identification of ceRNA regulation.It is anticipated that this Research Topic will motivate researchers in the field to accelerate their research on ceRNA and attempt to assist in subsequent experimental design.Sabaie et al. applied a Positive Correlation (PC) method (Zhou et al., 2014;Xu et al., 2015) to investigate the role of lncRNA-related ceRNAs in Autism Spectrum Disorder (ASD), and found that four potential ceRNA axes (LINC00472/hsa-miR-221-3p/PTPN11, ANP32A-IT1/hsa-miR-182-5p/S100A2, LINC00472/hsa-miR-132-3p/S100A2, and RBM26-AS1/hsa-miR-182-5p/S100A2) may be involved in ASD pathogenesis.To understand the potential prognostic and immunological roles of CCNA2 in pan-cancer, Chen et al. performed a pan-cancer analysis to identify the upstream regulatory networks of CCNA2 and CCNA2-related ceRNAs in 33 tumor types.Moreover, Guo et al. systematically analyzed and integrated chromosomal instability-related dysregulated ceRNAs characteristics in lung adenocarcinoma (LUAD), and discovered that the

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   Complexities of post-transcriptional regulation and the modeling of ceRNA crosstalk · 2018

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