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Identification of lncRNA-associated competing triplets reveals global patterns and prognostic markers for cancer.

Wang P, Ning S, Zhang Y, Li R, Ye J, Zhao Z, Zhi H, Wang T, Guo Z, Li X - Nucleic Acids Res. (2015)

Bottom Line: In the lncACT cross-talk network, disease-associated lncRNAs, miRNAs and coding-genes showed specific topological patterns indicative of their competence and control of communication within the network.Based on the global cross-talk network and cluster analyses, nine cancer-specific sub-networks were constructed.H19- and BRCA1/2-associated lncACTs were able to discriminate between two groups of patients with different clinical outcomes.

View Article: PubMed Central - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.

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Related in: MedlinePlus

An integrative pipeline for transcriptome-wide identification of lncACTs. Interactions between miRNAs and lncRNAs were predicted using four computational approaches (TargetScan, miRanda, PITA and RNAhybrid) and combined with CLIP data to extract biologically relevant interactions. Experimental evidence for miRNA–lncRNA interactions was integrated into the pipeline. Human miRNAs and target coding gene pairs were obtained from TarBase and mirTarBase, which were combined and integrated into the pipeline as miRNA–mRNA interactions. MiRNA–lncRNA and miRNA–mRNA pairs sharing the same miRNA were merged into an lncRNA–miRNA–mRNA interaction as a candidate lncACT. Functional lncACTs were identified by evaluating correlations with expression in 12 types of cancer data sets and was defined as functional if the expression of the constituents met specific correlation criteria.
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Figure 1: An integrative pipeline for transcriptome-wide identification of lncACTs. Interactions between miRNAs and lncRNAs were predicted using four computational approaches (TargetScan, miRanda, PITA and RNAhybrid) and combined with CLIP data to extract biologically relevant interactions. Experimental evidence for miRNA–lncRNA interactions was integrated into the pipeline. Human miRNAs and target coding gene pairs were obtained from TarBase and mirTarBase, which were combined and integrated into the pipeline as miRNA–mRNA interactions. MiRNA–lncRNA and miRNA–mRNA pairs sharing the same miRNA were merged into an lncRNA–miRNA–mRNA interaction as a candidate lncACT. Functional lncACTs were identified by evaluating correlations with expression in 12 types of cancer data sets and was defined as functional if the expression of the constituents met specific correlation criteria.

Mentions: MiRNA target prediction methods and restrictive criteria (26,30–32) were applied to filter functional miRNA–lncRNA interactions for constructing the competing network. Functional lncACTs were identified through an integrated pipeline by incorporating seed sequence matching (27), CLIP-Seq data filtering (30), the stringent expression correlation model (27) and experimental evidence. Previous studies have shown that lncRNAs can act as miRNA sponges by competing with endogenous mRNAs for miRNA binding (19,21,22), an interaction that can be identified using traditional miRNA target prediction methods (28,29,45). Other studies have integrated different prediction methods to identify functional miRNA–lncRNA pairs (21,46). As a first step, candidate miRNA–lncRNA interactions were predicted using TargetScan (v.6.0) (47), PITA (March 2007 version) (48), miRanda (Nov. 2010 version) (49) and RNAhybrid (v.2.1.1) (50) with default parameters. To maintain accuracy and consistency, the different methods were applied to the same miRNA and lncRNA data set. The number of miRNA–lncRNA interactions resulting from the four prediction methods differed significantly (Figure 1 and Supplementary Figure S2). To select bona fide targets, 4 625 625 miRNA–lncRNA interactions identified using the four methods were integrated into a comprehensive data set. This strategy has been used by previous studies to enlarge the pool of functional miRNA–target interactions (51,52). A comparative analysis (Supplementary Methods) indicated that integrating different algorithms is superior to using any single algorithm alone (Supplementary Figure S3). As a component of lncACTs, miRNA–mRNA interactions were also obtained from reliable reference databases. A total of 43 497 non-redundant miRNA–target pairs were collected (Figure 1).


Identification of lncRNA-associated competing triplets reveals global patterns and prognostic markers for cancer.

Wang P, Ning S, Zhang Y, Li R, Ye J, Zhao Z, Zhi H, Wang T, Guo Z, Li X - Nucleic Acids Res. (2015)

An integrative pipeline for transcriptome-wide identification of lncACTs. Interactions between miRNAs and lncRNAs were predicted using four computational approaches (TargetScan, miRanda, PITA and RNAhybrid) and combined with CLIP data to extract biologically relevant interactions. Experimental evidence for miRNA–lncRNA interactions was integrated into the pipeline. Human miRNAs and target coding gene pairs were obtained from TarBase and mirTarBase, which were combined and integrated into the pipeline as miRNA–mRNA interactions. MiRNA–lncRNA and miRNA–mRNA pairs sharing the same miRNA were merged into an lncRNA–miRNA–mRNA interaction as a candidate lncACT. Functional lncACTs were identified by evaluating correlations with expression in 12 types of cancer data sets and was defined as functional if the expression of the constituents met specific correlation criteria.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4402541&req=5

Figure 1: An integrative pipeline for transcriptome-wide identification of lncACTs. Interactions between miRNAs and lncRNAs were predicted using four computational approaches (TargetScan, miRanda, PITA and RNAhybrid) and combined with CLIP data to extract biologically relevant interactions. Experimental evidence for miRNA–lncRNA interactions was integrated into the pipeline. Human miRNAs and target coding gene pairs were obtained from TarBase and mirTarBase, which were combined and integrated into the pipeline as miRNA–mRNA interactions. MiRNA–lncRNA and miRNA–mRNA pairs sharing the same miRNA were merged into an lncRNA–miRNA–mRNA interaction as a candidate lncACT. Functional lncACTs were identified by evaluating correlations with expression in 12 types of cancer data sets and was defined as functional if the expression of the constituents met specific correlation criteria.
Mentions: MiRNA target prediction methods and restrictive criteria (26,30–32) were applied to filter functional miRNA–lncRNA interactions for constructing the competing network. Functional lncACTs were identified through an integrated pipeline by incorporating seed sequence matching (27), CLIP-Seq data filtering (30), the stringent expression correlation model (27) and experimental evidence. Previous studies have shown that lncRNAs can act as miRNA sponges by competing with endogenous mRNAs for miRNA binding (19,21,22), an interaction that can be identified using traditional miRNA target prediction methods (28,29,45). Other studies have integrated different prediction methods to identify functional miRNA–lncRNA pairs (21,46). As a first step, candidate miRNA–lncRNA interactions were predicted using TargetScan (v.6.0) (47), PITA (March 2007 version) (48), miRanda (Nov. 2010 version) (49) and RNAhybrid (v.2.1.1) (50) with default parameters. To maintain accuracy and consistency, the different methods were applied to the same miRNA and lncRNA data set. The number of miRNA–lncRNA interactions resulting from the four prediction methods differed significantly (Figure 1 and Supplementary Figure S2). To select bona fide targets, 4 625 625 miRNA–lncRNA interactions identified using the four methods were integrated into a comprehensive data set. This strategy has been used by previous studies to enlarge the pool of functional miRNA–target interactions (51,52). A comparative analysis (Supplementary Methods) indicated that integrating different algorithms is superior to using any single algorithm alone (Supplementary Figure S3). As a component of lncACTs, miRNA–mRNA interactions were also obtained from reliable reference databases. A total of 43 497 non-redundant miRNA–target pairs were collected (Figure 1).

Bottom Line: In the lncACT cross-talk network, disease-associated lncRNAs, miRNAs and coding-genes showed specific topological patterns indicative of their competence and control of communication within the network.Based on the global cross-talk network and cluster analyses, nine cancer-specific sub-networks were constructed.H19- and BRCA1/2-associated lncACTs were able to discriminate between two groups of patients with different clinical outcomes.

View Article: PubMed Central - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.

Show MeSH
Related in: MedlinePlus