title: Identify signature regulatory network for glioblastoma prognosis by integrative mRNA and miRNA co-expression analysis.
abstract: Glioblastoma multiforme (GBM) is the most common and aggressive type of primary brain tumor in adults. Patients with this disease have a poor prognosis. The objective of this study is to identify survival-related individual genes (or miRNAs) and miRNA -mRNA pairs in GBM using a multi-step approach. First, the weighted gene co-expression network analysis and survival analysis are applied to identify survival-related modules from mRNA and miRNA expression profiles, respectively. Subsequently, the role of individual genes (or miRNAs) within these modules in GBM prognosis are highlighted using survival analysis. Finally, the integration analysis of miRNA and mRNA expression as well as miRNA target prediction is used to identify survival-related miRNA -mRNA regulatory network. In this study, five genes and two miRNA modules that significantly correlated to patient's survival. In addition, many individual genes (or miRNAs) assigned to these modules were found to be closely linked with survival. For instance, increased expression of neuropilin-1 gene (a member of module turquoise) indicated poor prognosis for patients and a group of miRNA -mRNA regulatory networks that comprised 38 survival-related miRNA -mRNA pairs. These findings provide a new insight into the underlying molecular regulatory mechanisms of GBM.
authors.affiliation: Department of Computational Physics, Institute of Modern Physics of Chinese Academy of Sciences, Lanzhou 730000, People's Republic of China.
authors.forename: Zhi-Tong
authors.initials: ZT
authors.lastname: Bing
authors.affiliation: Department of Physics, Graduate School of Chinese Academy of Sciences, Beijing 100049, People's Republic of China.
authors.forename: Guang-Hui
authors.initials: GH
authors.lastname: Yang
authors.affiliation: Department of Internal Medicine, College of Medicine, Hunan Normal University, Changsha 410006, People's Republic of China.
authors.forename: Jie
authors.initials: J
authors.lastname: Xiong
authors.affiliation: College of Electrical Engineering, Northwest University for Nationalities, Lanzhou 730030, People's Republic of China.
authors.forename: Ling
authors.initials: L
authors.lastname: Guo
authors.affiliation: Department of Computational Physics, Institute of Modern Physics of Chinese Academy of Sciences, Lanzhou 730000, People's Republic of China. lyang@impcas.ac.cn.
authors.forename: Lei
authors.initials: L
authors.lastname: Yang
mesh_terms: D000368:Aged; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000235:genetics* / Q000401:mortality; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D005909:Glioblastoma / Q000175:diagnosis* / Q000235:genetics* / Q000401:mortality; D006801:Humans; D053208:Kaplan-Meier Estimate; D017567:Karnofsky Performance Status; D008297:Male; D035683:MicroRNAs / Q000235:genetics*; D008875:Middle Aged; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D016016:Proportional Hazards Models; D020796:Receptor, Platelet-Derived Growth Factor alpha / Q000235:genetics; D016896:Treatment Outcome
chemical_list: C543845:MIRN24 microRNA, human; C522425:MIRN34 microRNA, human; D035683:MicroRNAs; D020796:Receptor, Platelet-Derived Growth Factor alpha