Title:Exploring MiRNA Sponge Networks of breast cancer by Combining miRNA-disease-lncRNA and miRNA-target Networks
VOLUME: 16 ISSUE: 3
Author(s):Lei Tian and Shu-Lin Wang*
Affiliation:School of Information Science and Engineering, Hunan University, Changsha, School of Information Science and Engineering, Hunan University, Changsha
Keywords:miRNA sponge networks, miRNA sponge modules, breast cancer, biological enrichment, clustering algorithm.
Abstract:Background: Recently, ample researches show that microRNAs (miRNAs) not only
interact with coding genes but interact with a pool of different RNAs. Those RNAs are called
miRNA sponges, including long non-coding RNAs (lncRNAs), circular RNA, pseudogenes and
various messenger RNAs. Understanding regulatory networks of miRNA sponges can better help
researchers to study the mechanisms of breast cancers.
Objective: We develop a new method to explore miRNA sponge networks of breast cancer by
combining miRNA-disease-lncRNA and miRNA-target networks (MSNMDL).
Methods: Firstly, MSNMDL infers miRNA-lncRNA functional similarity networks from miRNAdisease-
lncRNA networks. Secondly, MSNMDL forms lncRNA-target networks by using lncRNA
to replace the role of matched miRNA in miRNA-target networks according to the lncRNA-miRNA
pair of miRNA-lncRNA functional similarity networks. And MSNMDL only retains the genes of
breast cancer in lncRNA-target networks to construct candidate miRNA sponge networks. Thirdly,
MSNMDL merges these candidate miRNA sponge networks with other miRNA sponge interactions
and then selects top-hub lncRNA and its interactions to construct miRNA sponge networks.
Result: MSNMDL is superior to other methods in terms of biological significance and its identified
modules might act as module signatures for prognostication of breast cancer.
Conclusion: MiRNA sponge networks identified by MSNMDL are biologically significant and are
closely associated with breast cancer, which makes MSNMDL a promising way for researchers to
study the pathogenesis of breast cancer.
