A novel two-stage method for identifying microRNA-gene regulatory modules in breast cancer

作者：Min Wenwen; Liu Juan^*; Luo Fei; Zhang Shihua

来源：IEEE International Conference on Bioinformatics and Biomedicine, 2015-11-09 to 2015-11-12.

• miRNA-gene regulatory module
• miRNA cluster
• Multiple-output Sparse Group Lasso (MSGL)
• L-0-penalized Singular Value Decomposition (L-0-SVD)

In this paper, we propose a two-stage method for identifying miRNA-gene regulatory modules by integrating miRNA/mRNA expression profiles and miRNA genomic cluster data. We first adopt a Multiple-output Sparse Group Lasso (MSGL) regression model to predict the miRNA-gene regulatory network. Further, we propose a L-0-penalized Singular Value Decomposition (L-0-SVD) model to identify modules from the predicted network. We apply this method to miRNA and mRNA expression profiles of the breast cancer data from TCGA databases and identify ten miRNA-gene regulatory modules. We find that (1) the modules are significantly associated in a predicted miRNA-gene regulatory network; (2) the modules are significantly enriched in GO biological processes and KEGG pathways, respectively; (3) many miRNAs and genes in the modules are related with breast cancer. On average, 51% of the miRNAs and 30% of the genes are related with breast cancer. The results demonstrate that miRNAgene regulatory modules provide insights into the mechanisms of the combinatorial regulation between miRNAs and genes.

• 出版日期2015
• 单位中国科学院; 中国科学院数学与系统科学研究院; 武汉大学