MicroRNA regulatory network inference identifies miR-34a as a novel regulator of TGF-β signaling in glioblastoma

• Verfasst von: Genovese, Giannicola [VerfasserIn]
• Verfasst von: Campos, Benito [VerfasserIn]
• Titel: MicroRNA regulatory network inference identifies miR-34a as a novel regulator of TGF-β signaling in glioblastoma
• Verf.angabe: Giannicola Genovese, Ayla Ergun, Sachet A. Shukla, Benito Campos, Jason Hanna, Papia Ghosh, Steven N. Quayle, Kunal Rai, Simona Colla, Haoqiang Ying, Chang-Jiun Wu, Sharmistha Sarkar, Yonghong Xiao, Jianhua Zhang, Hailei Zhang, Lawrence Kwong, Katherine Dunn, Wolf Ruprecht Wiedemeyer, Cameron Brennan, Hongwu Zheng, David L. Rimm, James J. Collins, and Lynda Chin
• Umfang: 15 S.
• Fussnoten: Gesehen am 04.05.2018
• Titel Quelle: Enthalten in: Cancer discovery
• Jahr Quelle: 2012
• Band/Heft Quelle: 2(2012), 8, S. 736-749
• ISSN Quelle: 2159-8290
• DOI: doi:10.1158/2159-8290.CD-12-0111
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1572630523

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. Significance: 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. Cancer Discov; 2(8); 736-49. ©2012 AACR. Read the Commentary on this article by Babic et al., p. 676. This article is highlighted in the In This Issue feature, p. 653.