Lei Tian and Shu-Lin Wang* Pages 385 - 394 ( 10 )
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.
miRNA sponge networks, miRNA sponge modules, breast cancer, biological enrichment, clustering algorithm, prognostication.
School of Information Science and Engineering, Hunan University, Changsha, School of Information Science and Engineering, Hunan University, Changsha