Computational analysis identifies a sponge interaction network between long non-coding RNAs and messenger RNAs in human breast cancer

BMC Syst Biol. 2014 Jul 17;8:83. doi: 10.1186/1752-0509-8-83.

Abstract

Background: Non-coding RNAs (ncRNAs) are emerging as key regulators of many cellular processes in both physiological and pathological states. Moreover, the constant discovery of new non-coding RNA species suggests that the study of their complex functions is still in its very early stages. This variegated class of RNA species encompasses the well-known microRNAs (miRNAs) and the most recently acknowledged long non-coding RNAs (lncRNAs). Interestingly, in the last couple of years, a few studies have shown that some lncRNAs can act as miRNA sponges, i.e. as competing endogenous RNAs (ceRNAs), able to reduce the amount of miRNAs available to target messenger RNAs (mRNAs).

Results: We propose a computational approach to explore the ability of lncRNAs to act as ceRNAs by protecting mRNAs from miRNA repression. A seed match analysis was performed to validate the underlying regression model. We built normal and cancer networks of miRNA-mediated sponge interactions (MMI-networks) using breast cancer expression data provided by The Cancer Genome Atlas.

Conclusions: Our study highlights a marked rewiring in the ceRNA program between normal and pathological breast tissue, documented by its "on/off" switch from normal to cancer, and vice-versa. This mutually exclusive activation confers an interesting character to ceRNAs as potential oncosuppressive, or oncogenic, protagonists in cancer. At the heart of this phenomenon is the lncRNA PVT1, as illustrated by both the width of its antagonist mRNAs in normal-MMI-network, and the relevance of the latter in breast cancer. Interestingly, PVT1 revealed a net binding preference towards the mir-200 family as the bone of contention with its rival mRNAs.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Breast Neoplasms / genetics*
  • Breast Neoplasms / metabolism*
  • Computational Biology / methods*
  • Gene Regulatory Networks*
  • Humans
  • MicroRNAs / genetics
  • MicroRNAs / metabolism
  • RNA, Long Noncoding / genetics*
  • RNA, Long Noncoding / metabolism*
  • RNA, Messenger / genetics
  • RNA, Messenger / metabolism

Substances

  • MicroRNAs
  • RNA, Long Noncoding
  • RNA, Messenger