title: microRNA regulatory network inference identifies miR-34a as a novel regulator of TGF-β signaling in glioblastoma.
abstract: Leveraging The Cancer Genome Atlas (TCGA) multidimensional data in glioblastoma, we inferred the putative regulatory network between microRNA and mRNA using the Context Likelihood of Relatedness modeling algorithm. Interrogation of the network in context of defined molecular subtypes identified 8 microRNAs with a strong discriminatory potential between proneural and mesenchymal subtypes. Integrative in silico analyses, a functional genetic screen, and experimental validation identified miR-34a as a tumor suppressor in proneural subtype glioblastoma. Mechanistically, in addition to its direct regulation of platelet-derived growth factor receptor-alpha (PDGFRA), promoter enrichment analysis of context likelihood of relatedness-inferred mRNA nodes established miR-34a as a novel regulator of a SMAD4 transcriptional network. Clinically, miR-34a expression level is shown to be prognostic, where miR-34a low-expressing glioblastomas exhibited better overall survival. This work illustrates the potential of comprehensive multidimensional cancer genomic data combined with computational and experimental models in enabling mechanistic exploration of relationships among different genetic elements across the genome space in cancer.
  
  
  CONCLUSIONS
  We illustrate here that network modeling of complex multidimensional cancer genomic data can generate a framework in which to explore the biology of cancers, leading to discovery of new pathogenetic insights as well as potential prognostic biomarkers. Specifically in glioblastoma, within the context of the global network, promoter enrichment analysis of network edges uncovered a novel regulation of TGF-β signaling via a Smad4 transcriptomic network by miR-34a.
authors: Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
authors: Giannicola
authors: G
authors: Genovese
authors: Ayla
authors: A
authors: Ergun
authors: Sachet A
authors: SA
authors: Shukla
authors: Benito
authors: B
authors: Campos
authors: Jason
authors: J
authors: Hanna
authors: Papia
authors: P
authors: Ghosh
authors: Steven N
authors: SN
authors: Quayle
authors: Kunal
authors: K
authors: Rai
authors: Simona
authors: S
authors: Colla
authors: Haoqiang
authors: H
authors: Ying
authors: Chang-Jiun
authors: CJ
authors: Wu
authors: Sharmistha
authors: S
authors: Sarkar
authors: Yonghong
authors: Y
authors: Xiao
authors: Jianhua
authors: J
authors: Zhang
authors: Hailei
authors: H
authors: Zhang
authors: Lawrence
authors: L
authors: Kwong
authors: Katherine
authors: K
authors: Dunn
authors: Wolf Ruprecht
authors: WR
authors: Wiedemeyer
authors: Cameron
authors: C
authors: Brennan
authors: Hongwu
authors: H
authors: Zheng
authors: David L
authors: DL
authors: Rimm
authors: James J
authors: JJ
authors: Collins
authors: Lynda
authors: L
authors: Chin
mesh_terms: D000818:Animals; D045744:Cell Line, Tumor; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D016147:Genes, Tumor Suppressor; D005909:Glioblastoma / Q000235:genetics* / Q000378:metabolism; D006801:Humans; D051379:Mice; D035683:MicroRNAs / Q000235:genetics* / Q000378:metabolism; D011379:Prognosis; D015398:Signal Transduction; D016212:Transforming Growth Factor beta / Q000235:genetics / Q000378:metabolism*
chemical_list: C522425:MIRN34 microRNA, human; D035683:MicroRNAs; D016212:Transforming Growth Factor beta