title: Using graphical adaptive lasso approach to construct transcription factor and microRNA's combinatorial regulatory network in breast cancer.
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.
authors.affiliation: School of Mathematical Sciences, Peking University, Beijing 100871, People's Republic of China.
authors.forename: Naifang
authors.initials: N
authors.lastname: Su
authors.affiliation: Quantitative Biology, Peking University, Beijing 100871, People's Republic of China.
authors.forename: Ding
authors.initials: D
authors.lastname: Dai
authors.affiliation: School of Mathematical Sciences, Peking University, Beijing 100871, People's Republic of China.
authors.forename: Chao
authors.initials: C
authors.lastname: Deng
authors.affiliation: Quantitative Biology, Peking University, Beijing 100871, People's Republic of China.
authors.forename: Minping
authors.initials: M
authors.lastname: Qian
authors.affiliation: Center for Statistical Sciences, Peking University, Beijing 100871, People's Republic of China. dengmh@pku.edu.cn.
authors.forename: Minghua
authors.initials: M
authors.lastname: Deng
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism; D019295:Computational Biology / Q000379:methods; D003196:Computer Graphics; D003198:Computer Simulation; D005260:Female; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D035683:MicroRNAs / Q000235:genetics* / Q000378:metabolism; D009369:Neoplasms / Q000235:genetics; D016011:Normal Distribution; D012372:ROC Curve; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: C524942:MIRN155 microRNA, human; D035683:MicroRNAs; D014157:Transcription Factors