Abstract 497: Identification of master microRNA regulatory networks for the mesenchymal glioblastoma subtype
Authors: Zhao Lan , Cho William , Zhu Hu , Shi Bing , Yan Hong 
Published: August 2018

Abstract
Introduction. Glioblastoma (GBM) is one of the most aggressive cancer types, accounting for more than 60% of malignant brain tumors in adults. Genomic profiling defined four subtypes of GBM termed proneural, neural, classical, and mesenchymal. The mesenchymal subtype is characterized by epithelial-mesenchymal-transition (EMT) and is associated with poor prognosis. Our aim in this study is to identify the mesenchymal subtype-specific biomarkers that may drive EMT, resulting in worse clinical outcome.
Materials and methods. We investigated 433 primary GBM patients from TCGA that have both gene and microRNA (miRNA) expression profiles. A prediction analysis for microarrays (PAM) classifier was built based on the predefined 141 patient labels and 738 gene signatures. The classifier was then used to classify the remaining unlabeled patients into one of the four subtypes. Comparisons between the mesenchymal and non-mesenchymal subtypes were performed using two-sided t-test statistics. Anti-correlated miRNA-mRNA interactions were used for network inferred by ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks), and the network were visualized by RedeR package. We tested the statistically significant over-representation of EMT signature genes in the regulon of each miRNA. MiRNAs of top significance (false discovery rate, FDR &lt; 0.05) were selected as master regulators.
Results and Discussion. We classified the remaining unlabeled 292 GBM patients by using the PAM classifier. Gene set enrichment analysis (GSEA) and survival analysis were performed to do molecular and clinical characterization of the four subtypes, respectively, which all align with previous characteristics. Such as, mesenchymal subtype is enriched for EMT signatures, and proneural subtype has the best clinical outcomes. We also classified the 43 GBM cell lines from the Cancer Cell Line Encyclopedia (CCLE) into one of the four subtypes. In total, we identified 54 miRNAs downregulated, and 2,520 genes upregulated in mesenchymal compared with the non-mesenchymal subtypes. ARACNe predicted 28,586 miRNA-mRNA interactions, 2,784 of which were stable and therefore used for network construction. A miRNA regulatory network was subsequently built to describe the regulatory relations among miRNAs and target genes. Master regulator analysis identified 6 miRNAs, namely miR-9, miR-488, miR-95, miR-551b, miR-30c and miR-101, which may functioning as master regulators of the EMT program.
Conclusion. We used a relatively large GBM cohort to infer a miRNA-mRNA regulatory network associated with EMT. Further in vitro and in vivo experiments are ongoing to access the 6 miRNAs role in the EMT program.
Citation Format: Lan Zhao, William Cho, Hu Zhu, Bing Shi, Hong Yan. Identification of master microRNA regulatory networks for the mesenchymal glioblastoma subtype [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 497.