ceRNAR: An R package for identification and analysis of ceRNA-miRNA triplets

Abstract

Competitive endogenous RNA (ceRNA) represents a novel mechanism of gene regulation that controls several biological and pathological processes. Recently, an increasing number of in silico methods have been developed to accelerate the identification of such regulatory events. However, there is still a need for a tool supporting the hypothesis that ceRNA regulatory events only occur at specific miRNA expression levels. To this end, we present an R package, ceRNAR, which allows identification and analysis of ceRNA-miRNA triplets via integration of miRNA and RNA expression data. The ceRNAR package integrates three main steps: (i) identification of ceRNA pairs based on a rank-based correlation between pairs that considers the impact of miRNA and a running sum correlation statistic, (ii) sample clustering based on gene-gene correlation by circular binary segmentation, and (iii) peak merging to identify the most relevant sample patterns. In addition, ceRNAR also provides downstream analyses of identified ceRNA-miRNA triplets, including network analysis, functional annotation, survival analysis, external validation, and integration of different tools. The performance of our proposed approach was validated through simulation studies of different scenarios. Compared with several published tools, ceRNAR was able to identify true ceRNA triplets with high sensitivity, low false-positive rates, and acceptable running time. In real data applications, the ceRNAs common to two lung cancer datasets were identified in both datasets. The bridging miRNA for one of these, the ceRNA for MAP4K3, was identified by ceRNAR as hsa-let-7c-5p. Since similar cancer subtypes do share some biological patterns, these results demonstrated that our proposed algorithm was able to identify potential ceRNA targets in real patients. In summary, ceRNAR offers a novel algorithm and a comprehensive pipeline to identify and analyze ceRNA regulation. The package is implemented in R and is available on GitHub (https://github.com/ywhsiao/ceRNAR).

A comprehensive checklist of computational resources and methodologies for noncoding RNAs in systems medicine

“…miRSM, miRspongeR, CeRNASeek, and ceRNAR are R packages designed for the identification and analysis of ceRNA regulation. opplncRNA is implemented as a MATLAB package (Ge et al, 2022; Hsiao et al, 2022; J. Zhang, Liu, et al, 2019; J. Zhang et al, 2021; M. Zhang et al, 2021). CircMiMi and CRAFT are two standalone software that supports circRNA–miRNA–mRNA interactions investigation (Chiang et al, 2022; Dal Molin et al, 2022).…”

“…miRSM, miRspongeR, CeRNASeek, and ceRNAR are R packages designed for the identification and analysis of ceRNA regulation. opplncRNA is implemented as a MATLAB package (Ge et al, 2022;Hsiao et al, 2022;J. Zhang, Liu, et al, 2019;.…”

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 ].…”