A novel computational method for inferring competing endogenous interactions

Sardina, Davide Stefano; Alaimo, Salvatore; Ferro, Alfredo; Pulvirenti, Alfredo; Giugno, Rosalba

doi:10.1093/bib/bbw084

Cited by 37 publications

(33 citation statements)

References 68 publications

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“…The interactions between miRNA and MREs inhibit the activity and expression of RNA molecules (Su et al 2013). RNA molecules, including lncRNA, compete with corresponding miRNA through common MREs and they act as reciprocal ceRNA (Sardina et al 2017). Our research showed that LncRNA-ROR had MRE directly combined with miR-145 seed sequence.…”

Section: Discussionmentioning

confidence: 75%

Interaction between LncRNA-ROR and miR-145 contributes to epithelial-mesenchymal transition of ovarian cancer cells

Zhang²,

Wu³

et al. 2019

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Emerging evidence has indicated that long noncoding RNAs (LncRNAs) play critical roles in tumor development. LncRNA-regulator of reprogramming (ROR) could enhance the malignance of numerous tumors through promoting cell reprogramming and the chemoresistance of several cancers. Nevertheless, the detailed molecular mechanisms of LncRNA-ROR in ovarian cancer are not well elucidated. In our study, we demonstrated that LncRNA-ROR was high expression in ovarian cancer tissues than in normal ovarian tissues, and LncRNA-ROR level was positively associated with clinical stages and the differentiation grades of malignant cells. Functionally, LncRNA-ROR could induce epithelial-mesenchymal transition (EMT), and regulated ovarian cancer cell migration and invasion by decreasing the expression of tumor suppressive miR-145 and its target gene FLNB. Moreover, the binding site for miR-145 within LncRNA-ROR contributed to the reciprocal negative regulation of LncRNA-ROR and miR-145. Taken together, LncRNA-ROR promoted EMT by the miR-145/FLNB regulatory axis in ovarian cancer, providing a potential therapeutic target for ovarian cancer.

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Section: Discussionmentioning

confidence: 75%

Interaction between LncRNA-ROR and miR-145 contributes to epithelial-mesenchymal transition of ovarian cancer cells

Zhang²,

Wu³

et al. 2019

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“…Thus computational methods [10][11][12][13][14][15][16][17][18] have been proposed to study miRNA sponges.…”

Section: Discussionmentioning

confidence: 99%

“…Tay et al [10] have validated two mRNAs (CNOT6L and VAPA) as miRNA sponges that regulate tumor suppressor gene PTEN, antagonise PI3K/AKT signalling, and show concordant expression patterns and copy number loss with PTEN in human cancers. -4 -Although an increasing number of miRNA sponges have been discovered, the wet lab experiment approach for finding them is very time consuming and involves high cost.Thus computational methods [10][11][12][13][14][15][16][17][18] have been proposed to study miRNA sponges.For the identification of miRNA sponge interactions, the two principles, significant common miRNAs at sequence level and positively correlated at expression level, are commonly used in several computational methods. Based on the identified miRNA sponge interaction network, the miRNA sponge modules are identified by using network clustering algorithms.…”

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confidence: 99%

miRsponge: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules

Zhang

Liu

et al. 2018

Preprint

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BackgroundA microRNA (miRNA) sponge is an RNA molecule with multiple tandem miRNA response elements that can sequester miRNAs from their target mRNAs. Despite growing appreciation of the importance of miRNA sponges, our knowledge of their complex functions remains limited. Moreover, there is still a lack of miRNA sponge research tools that help researchers to quickly compare their proposed methods with other methods, apply existing methods to new datasets, or select appropriate methods for assisting in subsequent experimental design. ResultsTo fill the gap, we present an R/Bioconductor package, miRspongeR, for simplifying the procedure of identifying and analyzing miRNA sponge interaction networks and modules. It provides seven popular methods and an integrative method to identify miRNA sponge interactions. Moreover, it supports the validation of miRNA sponge interactions and the identification of miRNA sponge modules, as well as functional enrichment and survival analysis of miRNA sponge modules. ConclusionsThis package enables researchers to quickly evaluate their new methods, apply existing methods to new datasets, and consequently speed up miRNA sponge research. Keywords miRNA -ceRNA -miRNA sponge -miRNA sponge interaction networks -miRNA sponge modules -human breast invasive carcinoma Background MicroRNAs (miRNAs) are small non-coding RNAs with ~22 nucleotides. They usually induce repression or translational inhabitation of target genes through partial -3 -complementarities with multiple miRNA response elements (MREs) of the target RNA transcripts [1]. Previous studies [2, 3] have shown that miRNAs are involved in a broad range of biological processes, such as cell cycle control, cell apoptosis, cell differentiation and a diverse range of human cancers. The competing endogenous RNA (ceRNA) hypothesis [4] considers that all types of coding and non-coding RNA transcripts may crosstalk with each other by sharing common miRNAs. By competing with each other, those ceRNAs (also known as miRNA sponges or decoys), including long non-coding RNAs (lncRNAs), pseudogenes, mRNAs and circular RNAs (circRNAs), can release parental target mRNAs from miRNAs' control [5]. The ceRNA hypothesis challenges the traditional knowledge that coding RNAs only act as targets of miRNAs, and it provides a starting point for the investigation of the biological functions and mechanisms of miRNA sponges. Recently, several lncRNAs, pseudogenes, circRNAs and mRNAs acting as miRNA sponges have been experimentally identified and confirmed, together with their important biological functions. For example, linc-MD1, a muscle-specific lncRNA, activates muscle-specific gene expression by regulating the expression of MAML1 and MEF2C via sponging miR-133 and miR-135 [6]. PTENP1, a pseudogene of the PTEN tumor suppression gene, can act as a sponge of PTEN-targeting miRNAs to play a growth-suppressive role [7]. As for circRNAs, CDR1as/ciRS-7 is encoded in the genome antisense to the human CDR1 (gene) locus, and affects the activity of miR-7 target genes...

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“…Some of the Machine learning algorithms employed in ceRNA prediction are Bayesian Classification, Random Forest, Artificial Neural Networks (ANN), Hidden Markow Model (HMM) and Support Vector Machine (SVM). The ceRNAs are predicted based on scoring methods like 'confidence score' used in TraceRNA [59] or DT hybrid algorithm used in CERNIA [60]. For evaluating the performance of machine learning tasks, different performance measures are used.…”

Section: Cerna Prediction and Performance Analysismentioning

confidence: 99%

“…Along with these scores the correlations between gene expression values for a specific tissue type was added to form a vector of seven scores. Then by applying SVM a subset of the gene pairs are predicted as putative ceRNAs [60]. CEFINDER predicts ceRNA from conserved human miRNA-mRNA interactions derived from TargetScan, by converting the interactions into a matrix of '1's and '0's.…”

Section: Cerna Prediction Toolsmentioning

confidence: 99%

Review of Computational Prediction of Competing Endogenous RNA

Sherin¹,

Nair²

2019

J Proteomics Bioinform

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MicroRNAs (miRNAs) are the gene regulatory molecules that bind to microRNA response elements (MREs) in the 3' untranslated regions (UTRs) of mRNAs. The repressive activity of miRNA is counteracted by "miRNA sponges" or "competing endogenous RNAs (ceRNAs)" termed because of its competing nature of sequestering miRNA's effect. The ceRNAs with multiple MREs for a miRNA interact more, resulting in a regulatory gene network layer. The perturbation of ceRNA network causes various diseases including cancer. This discovery has increased the analysis of ceRNA interactions, networks and dynamics. Currently, a number of ceRNA-ceRNA interaction analysis and prediction tools are available online. This review focuses on computational prediction of mRNA-mRNA pairs acting as ceRNAs and its significance in RNA therapeutics. The computational prediction tools are compared with respect to the input data retrieved, features considered and the prediction method.

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A novel computational method for inferring competing endogenous interactions

Cited by 37 publications

References 68 publications

Interaction between LncRNA-ROR and miR-145 contributes to epithelial-mesenchymal transition of ovarian cancer cells

Interaction between LncRNA-ROR and miR-145 contributes to epithelial-mesenchymal transition of ovarian cancer cells

miRsponge: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules

Review of Computational Prediction of Competing Endogenous RNA

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