title: 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.
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors: Peng
authors: P
authors: Wang
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors: Shangwei
authors: S
authors: Ning
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors: Yunpeng
authors: Y
authors: Zhang
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors: Ronghong
authors: R
authors: Li
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors: Jingrun
authors: J
authors: Ye
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors: Zuxianglan
authors: Z
authors: Zhao
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors: Hui
authors: H
authors: Zhi
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors: Tingting
authors: T
authors: Wang
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China guoz@ems.hrbmu.edu.cn.
authors: Zheng
authors: Z
authors: Guo
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China lixia@hrbmu.edu.cn.
authors: Xia
authors: X
authors: Li
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism*; D030541:Databases, Genetic; D006801:Humans; D009369:Neoplasms / Q000235:genetics* / Q000473:pathology; D011379:Prognosis; D062085:RNA, Long Noncoding / Q000235:genetics*
chemical_list: D014408:Biomarkers, Tumor; D062085:RNA, Long Noncoding