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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

Disease-associated lncACTs exhibit specific expression patterns in cancer and matched normal samples. Illustrations of lncACT expression in cancer and normal states are shown in the left panel. The lncRNAs, miRNAs and coding-genes are colored blue, red and yellow, respectively. Corresponding expression profiles in KICH are shown in the right panel. Highly expressed genes are shown in red, low expression genes are shown in green. (A) The lncACT competing activities were observed in normal but not cancer samples. (B) The lncACT competing activities were not observed in normal samples but were present in cancer samples. (C) The lncACT competing activities were observed in normal and cancer samples, but some constituents showed expression levels that changed in opposite directions.
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Figure 6: Disease-associated lncACTs exhibit specific expression patterns in cancer and matched normal samples. Illustrations of lncACT expression in cancer and normal states are shown in the left panel. The lncRNAs, miRNAs and coding-genes are colored blue, red and yellow, respectively. Corresponding expression profiles in KICH are shown in the right panel. Highly expressed genes are shown in red, low expression genes are shown in green. (A) The lncACT competing activities were observed in normal but not cancer samples. (B) The lncACT competing activities were not observed in normal samples but were present in cancer samples. (C) The lncACT competing activities were observed in normal and cancer samples, but some constituents showed expression levels that changed in opposite directions.

Mentions: TCGA data have provided a catalog of genomic alterations identified in tumor and matched normal samples (44), enabling the identification of lncACTs associated with cancers. LncRNAs, miRNAs and genes as well as lncACTs differentially expressed in cancer and matched normal RNA sequence data were identified using the DEGseq R package (79). At the expression level, genes in each cancer-specific cluster were significantly associated with corresponding cancers (fold-change > 1.5; P < 0.01 by DEGseq). 878 lncACTs were associated with at least one differently expressed lncRNA or gene and of these, 371 (42.3%) showed differences in competing activity score of at least 1.5-fold between the two sets of samples. Two patterns emerged from the analysis of lncRNA, miRNA and gene expression and variations in competitive activity in these lncACTs: (1) 252 lncACTs showed competing activities in normal but not cancer samples (Figure 6A); and (2) 119 lncACTs had no competitive interactions in normal samples but did in cancer samples (Figure 6B). In these lncACTs, the difference in competing activity score implied that lncRNA–miRNA-gene interactions were impaired by pathological changes in the cellular environment, resulting in the dysregulation of gene expression possibly through the loss of competitive ability in lncRNAs. For example, the lncRNA DLEU2 indirectly regulated the downstream gene C1QBP in normal samples by binding miR-375. However, the DLEU2/miR-375 interaction was lost in KICH samples, leading to the downregulation of C1QBP expression (Figure 6A). In contrast, 507 (57.7%) of lncACTs showed competing activities in both cancer and normal samples, but with opposite expression patterns (Figure 6C). In these lncACTs, lncRNAs played prominent roles in the competitive interactions, resulting in corresponding changes in miRNA and gene expression. For example, the lncRNA DANCR binds miR-222 and regulates the downstream gene TCEAL1. As proposed by the ceRNA hypothesis (19), in normal samples, DANCR transcripts are highly expressed to maintain its capacity for acting as a sponge for miR-222 molecules. This can indirectly result in the derepression of TCEAL1. Conversely, in cancer samples, the downregulation of DANCR reduces its competitive activity for miR-222, leading to the inhibition of TCEAL1 by miR-222. The suggested regulatory mechanism is illustrated in Supplementary Figure S17. In fact, the TCEAL1 gene is significantly downregulated in esophageal cancer tissue (80). Our findings suggest potential tumor suppressor pathways that are regulated by competing lncRNAs.


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)

Disease-associated lncACTs exhibit specific expression patterns in cancer and matched normal samples. Illustrations of lncACT expression in cancer and normal states are shown in the left panel. The lncRNAs, miRNAs and coding-genes are colored blue, red and yellow, respectively. Corresponding expression profiles in KICH are shown in the right panel. Highly expressed genes are shown in red, low expression genes are shown in green. (A) The lncACT competing activities were observed in normal but not cancer samples. (B) The lncACT competing activities were not observed in normal samples but were present in cancer samples. (C) The lncACT competing activities were observed in normal and cancer samples, but some constituents showed expression levels that changed in opposite directions.
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Figure 6: Disease-associated lncACTs exhibit specific expression patterns in cancer and matched normal samples. Illustrations of lncACT expression in cancer and normal states are shown in the left panel. The lncRNAs, miRNAs and coding-genes are colored blue, red and yellow, respectively. Corresponding expression profiles in KICH are shown in the right panel. Highly expressed genes are shown in red, low expression genes are shown in green. (A) The lncACT competing activities were observed in normal but not cancer samples. (B) The lncACT competing activities were not observed in normal samples but were present in cancer samples. (C) The lncACT competing activities were observed in normal and cancer samples, but some constituents showed expression levels that changed in opposite directions.
Mentions: TCGA data have provided a catalog of genomic alterations identified in tumor and matched normal samples (44), enabling the identification of lncACTs associated with cancers. LncRNAs, miRNAs and genes as well as lncACTs differentially expressed in cancer and matched normal RNA sequence data were identified using the DEGseq R package (79). At the expression level, genes in each cancer-specific cluster were significantly associated with corresponding cancers (fold-change > 1.5; P < 0.01 by DEGseq). 878 lncACTs were associated with at least one differently expressed lncRNA or gene and of these, 371 (42.3%) showed differences in competing activity score of at least 1.5-fold between the two sets of samples. Two patterns emerged from the analysis of lncRNA, miRNA and gene expression and variations in competitive activity in these lncACTs: (1) 252 lncACTs showed competing activities in normal but not cancer samples (Figure 6A); and (2) 119 lncACTs had no competitive interactions in normal samples but did in cancer samples (Figure 6B). In these lncACTs, the difference in competing activity score implied that lncRNA–miRNA-gene interactions were impaired by pathological changes in the cellular environment, resulting in the dysregulation of gene expression possibly through the loss of competitive ability in lncRNAs. For example, the lncRNA DLEU2 indirectly regulated the downstream gene C1QBP in normal samples by binding miR-375. However, the DLEU2/miR-375 interaction was lost in KICH samples, leading to the downregulation of C1QBP expression (Figure 6A). In contrast, 507 (57.7%) of lncACTs showed competing activities in both cancer and normal samples, but with opposite expression patterns (Figure 6C). In these lncACTs, lncRNAs played prominent roles in the competitive interactions, resulting in corresponding changes in miRNA and gene expression. For example, the lncRNA DANCR binds miR-222 and regulates the downstream gene TCEAL1. As proposed by the ceRNA hypothesis (19), in normal samples, DANCR transcripts are highly expressed to maintain its capacity for acting as a sponge for miR-222 molecules. This can indirectly result in the derepression of TCEAL1. Conversely, in cancer samples, the downregulation of DANCR reduces its competitive activity for miR-222, leading to the inhibition of TCEAL1 by miR-222. The suggested regulatory mechanism is illustrated in Supplementary Figure S17. In fact, the TCEAL1 gene is significantly downregulated in esophageal cancer tissue (80). Our findings suggest potential tumor suppressor pathways that are regulated by competing lncRNAs.

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