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In silico analysis of long non-coding RNAs in medulloblastoma and its subgroups.

Authors
  • Joshi, Piyush1
  • Jallo, George2
  • Perera, Ranjan J3
  • 1 Cancer and Blood Disorder Institute, Johns Hopkins All Children's Hospital, 600 5th St. South, St. Petersburg, FL 33701, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, 1650 Orleans St., Baltimore, MD 21231, USA.
  • 2 Institute of Brain Protection Sciences, Johns Hopkins All Children's Hospital, 600 5th St. South, St. Petersburg, FL 33701 USA.
  • 3 Cancer and Blood Disorder Institute, Johns Hopkins All Children's Hospital, 600 5th St. South, St. Petersburg, FL 33701, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, 1650 Orleans St., Baltimore, MD 21231, USA; Sanford Burnham Prebys Medical Discovery Institute, 10901 N Torrey Pines Rd, La Jolla, CA 92037, USA. Electronic address: [email protected]
Type
Published Article
Journal
Neurobiology of Disease
Publisher
Elsevier
Publication Date
Jul 01, 2020
Volume
141
Pages
104873–104873
Identifiers
DOI: 10.1016/j.nbd.2020.104873
PMID: 32320737
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Medulloblastoma is the most common malignant pediatric brain tumor with high fatality rate. Recent large-scale studies utilizing genome-wide technologies have sub-grouped medulloblastomas into four major subgroups: wingless (WNT), sonic hedgehog (SHH), group 3, and group 4. However, there has yet to be a global analysis of long non-coding RNAs, a crucial part of the regulatory transcriptome, in medulloblastoma. Here, we performed bioinformatic analysis of RNA-seq data from 175 medulloblastoma patients. Differential lncRNA expression sub-grouped medulloblastomas into the four main molecular subgroups. Some of these lncRNAs were subgroup-specific, with a random forest-based machine-learning algorithm identifying an 11-lncRNA diagnostic signature. We also validated the diagnostic signature in patient derived xenograft (PDX) models. We further identified a 17-lncRNA prognostic model using LASSO based penalized Cox' PH model (Score HR = 13.6301, 95% CI = 8.857-20.98, logrank p-value ≤ 2e-16). Our analysis represents the first global lncRNA analysis in medulloblastoma. Our results identify putative candidate lncRNAs that could be evaluated for their functional role in medulloblastoma genesis and progression or as diagnostic and prognostic biomarkers. Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.

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