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Construction of a lncRNA-mediated feed-forward loop network reveals global topological features and prognostic motifs in human cancers

  • Shangwei Ning
  • , Yue Gao
  • , Peng Wang
  • , Xiang Li*
  • , Hui Zhi
  • , Yan Zhang
  • , Yue Liu
  • , Jizhou Zhang
  • , Maoni Guo
  • , Dong Han
  • , Xia Li
  • *Corresponding author for this work
  • Harbin Medical University
  • National Center for Nanoscience and Technology

Research output: Contribution to journalArticlepeer-review

Abstract

Long non-coding RNAs (lncRNAs), transcription factors and microRNAs can form lncRNA-mediated feed-forward loops (L-FFLs), which are functional network motifs that regulate a wide range of biological processes, such as development and carcinogenesis. However, L-FFL network motifs have not been systematically identified, and their roles in human cancers are largely unknown. In this study, we computationally integrated data from multiple sources to construct a global L-FFL network for six types of human cancer and characterized the topological features of the network. Our approach revealed several dysregulated L-FFL motifs common across different cancers or specific to particular cancers. We also found that L-FFL motifs can take part in other types of regulatory networks, such as mRNA-mediated FFLs and ceRNA networks, and form the more complex networks in human cancers. In addition, survival analyses further indicated that L-FFL motifs could potentially serve as prognostic biomarkers. Collectively, this study elucidated the roles of L-FFL motifs in human cancers, which could be beneficial for understanding cancer pathogenesis and treatment.

Original languageEnglish
Pages (from-to)45937-45947
Number of pages11
JournalOncotarget
Volume7
Issue number29
DOIs
StatePublished - 2016
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Feed-forward loop
  • Long non-coding RNA
  • Network motif
  • Prognostic biomarker
  • Topological feature

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