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

Abstract

Recent studies have suggested that long non-coding RNAs (lncRNAs) can interact with microRNAs (miRNAs) and indirectly regulate miRNA targets though competing interactions. However, the molecular mechanisms underlying these interactions are still largely unknown. In this study, these lncRNA-miRNA-gene interactions were defined as lncRNA-associated competing triplets (LncACTs), and an integrated pipeline was developed to identify lncACTs that are active in cancer. Competing lncRNAs had sponge features distinct from non-competing lncRNAs. 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. The construction of global competing activity profiles revealed that lncACTs had high activity specific to cancers. Analyses of clustered lncACTs revealed that they were enriched in various cancer-related biological processes. 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. Disease-associated lncACTs also showed variable competing patterns across normal and cancer patient samples. In summary, this study uncovered and systematically characterized global properties of human lncACTs that may have prognostic value for predicting clinical outcome in cancer patients.

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.

Figure 2.

Cross-talk between different functional lncACTs and their network properties. (A) A global lncACT cross-talk network consisting of 335 lncRNAs, 212 miRNAs and 1312 mRNAs was constructed and are arranged as inner, intermediate and outer layers, respectively. Layers were connected through interactions, as seen from the high density of elements at layer interfaces. (B) Disease-associated genes mapped to the global network constituted nodes that exhibited specific topological characteristics compared to other nodes. In A and B, lncRNAs, miRNAs and coding-genes were blue, red and yellow colored. The node degree was indicated by the node size. Disease associated nodes were marked by black circles. (C) The network revealed a power law distribution with slope −1.32 and R² = 0.87. X axis indicating nodes degree distribution. Y axis indicating frequency of nodes according to X axis. (D and E) Disease-associated nodes had a higher degree and betweenness centrality than other nodes. Data are shown as mean ± SEM. Disease-associated and other nodes were indicated in red and blue along X axis. Average degrees of these two groups of nodes were indicated by Y axis.

Figure 3.

LncRNAs_in have more extreme properties than lncRNAs_out. In lncRNAs_in, (A) transcript length and (B) number of exons was greater; and (C) expression levels (RPKM), (D) miRNA target site density (sites/KB) and (E) conservation scores were higher than in lncRNAs_out. (F) Expression levels of lncRNAs in lncACTs were positively correlated with the number of associated lncACTs. Boxplots depict different groups of properties. From bottom to top in each boxplot, the five vertical lines represent the minimum observed value (Min), the first quartile (Q1), the second quartile (Q2) or median, the third quartile (Q3) and the maximum observed value (Max). P values were determined by the Mann-Whitney U test.

Figure 4.

(A) Dynamic changes in activity profiles of lncACTs (rows) in 12 types of cancer (columns). Rows were ordered by a k-means clustering of lncACTs; 10 lncACT clusters had high activity levels in one or two cancers. These clusters comprised of approximately 83% of the 5119 functional lncACTs that were analyzed. Competing activity is indicated by a red colored bar, ranging from 0 to 1. (B) Cumulative distribution of lncACT specificity score. The majority (>70%) of lncACTs had scores of >0.5. The X axis represents the specificity score for each lncACT. The Y axis represents the cumulative frequency of specificity score. (C and D) KEGG pathways and GO terms enriched for lncACT cluster 3, ranked by −log₁₀(P).

Figure 5.

Sub-network analysis of competing lncACTs. (A) A BRCA-specific sub-network was derived from the global crosstalk network, which consisted of 95 lncRNAs, 68 miRNAs, 197 mRNAs and contained 132 functional modules. Two example modules are demarcated by circles. The lncRNAs, miRNAs and coding-genes are colored blue, red and yellow, respectively. The node degree is indicated by the node size. (B) One module had five nodes, all of which have been experimentally demonstrated to have key roles in the development of breast cancer. The expression heat map is shown on the right. In the heat map, highly expressed genes are shown in red, low expression genes are shown in green. (C) The lncRNA H19 was identified as a hub that connects five competing modules. (D) The lncRNA MIR22HG is functionally complementary to H19 and interacts with the same miRNAs. A genomic representation of miR-22 is shown in the red bar and MIR22HG is shown on the bottom. Predicted miR-22 target sites on MIR22HG are shown in the black bar, with detailed miRNA binding information of the site nearest to the miR-22 locus illustrated on the right. (E) Hierarchical clustering of 248 patients based on H19 and BRCA1/2 lncACT expression. (F) Kaplan-Meier survival analysis of two groups of patients with different clinical outcomes. Those that showed no progression or who were still alive at the time of the last follow-up were censored (+). Survival days are shown along the X axis. Overall survival rates are shown along the Y axis.

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.