CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer

BMC Bioinformatics

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[1] grid.410585.d, College of Information Science and Engineering, Shandong Normal University, 250000, Jinan, China;grid.67293.39, College of Computer Science and Electronic Engineering, Hunan University, 410082, Changsha, China;grid.67293.39, College of Computer Science and Electronic Engineering, Hunan University, 410082, Changsha, China;0000 0001 0089 3695, grid.411427.5, Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, 410081, Changsha, China;

关键词: Regulatory pattern;    Module discovery;    microRNA;    lncRNA function;    ceRNA;    Cancer;    Machine learning;   

DOI  :  10.1186/s12859-019-2654-3

来源: publisher

【 摘 要 】

BackgroundNon-coding RNAs (ncRNAs) are emerging as key regulators and play critical roles in a wide range of tumorigenesis. Recent studies have suggested that long non-coding RNAs (lncRNAs) could interact with microRNAs (miRNAs) and indirectly regulate miRNA targets through competing interactions. Therefore, uncovering the competing endogenous RNA (ceRNA) regulatory mechanism of lncRNAs, miRNAs and mRNAs in post-transcriptional level will aid in deciphering the underlying pathogenesis of human polygenic diseases and may unveil new diagnostic and therapeutic opportunities. However, the functional roles of vast majority of cancer specific ncRNAs and their combinational regulation patterns are still insufficiently understood.ResultsHere we develop an integrative framework called CeModule to discover lncRNA, miRNA and mRNA-associated regulatory modules. We fully utilize the matched expression profiles of lncRNAs, miRNAs and mRNAs and establish a model based on joint orthogonality non-negative matrix factorization for identifying modules. Meanwhile, we impose the experimentally verified miRNA-lncRNA interactions, the validated miRNA-mRNA interactions and the weighted gene-gene network into this framework to improve the module accuracy through the network-based penalties. The sparse regularizations are also used to help this model obtain modular sparse solutions. Finally, an iterative multiplicative updating algorithm is adopted to solve the optimization problem.ConclusionsWe applied CeModule to two cancer datasets including ovarian cancer (OV) and uterine corpus endometrial carcinoma (UCEC) obtained from TCGA. The modular analysis indicated that the identified modules involving lncRNAs, miRNAs and mRNAs are significantly associated and functionally enriched in cancer-related biological processes and pathways, which may provide new insights into the complex regulatory mechanism of human diseases at the system level.

【 授权许可】

CC BY