Using graphical adaptive lasso approach to construct transcription factor and microRNA's combinatorial regulatory network in breast cancer. Issue 3 (1st June 2014)

• Record Type: Journal Article
• Authors: Su, Naifang; Dai, Ding; Deng, Chao; Qian, Minping; Deng, Minghua
• Is Part Of: IET systems biology. Volume 8:Issue 3(2014)
• Page Start: 87
• Page End: 95
• Publication Date: 2014-06-01
• Subjects: cancer -- RNA -- molecular biophysics -- molecular configurations -- genetics -- biological organs -- Gaussian processes -- graphs -- feedforward -- biology computing; graphical adaptive lasso approach -- transcription factor -- microRNA combinatorial regulatory network -- breast cancer -- cancer‐related gene regulation -- cancer biology -- gene expression -- combinatorial regulatory network -- network structure -- molecular pathway -- GALASSO -- Gaussian graphical model -- adaptive lasso penalties -- sequence information -- gene expression proflles -- feedforward loops -- data sources -- metaanalysis problem; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2013.0029
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : Discovering the regulation of cancer‐related gene is of great importance in cancer biology. Transcription factors and microRNAs are two kinds of crucial regulators in gene expression, and they compose a combinatorial regulatory network with their target genes. Revealing the structure of this network could improve the authors' understanding of gene regulation, and further explore the molecular pathway in cancer. In this article, the authors propose a novel approach graphical adaptive lasso (GALASSO) to construct the regulatory network in breast cancer. GALASSO use a Gaussian graphical model with adaptive lasso penalties to integrate the sequence information as well as gene expression profiles. The simulation study and the experimental profiles verify the accuracy of the authors' approach. The authors further reveal the structure of the regulatory network, and explore the role of feedforward loops in gene regulation. In addition, the authors discuss the combinatorial regulatory effect between transcription factors and microRNAs, and select miR‐155 for detailed analysis of microRNA's role in cancer. The proposed GALASSO approach is an efficient method to construct the combinatorial regulatory network. It also provides a new way to integrate different data sources and could find more applications in meta‐analysis problem.