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.
Institute of Brain Protection Sciences, Johns Hopkins All Children's Hospital, 600 5th St. South, St. Petersburg, FL 33701 USA.
Neurobiol Dis. 2020 Jul;141:104873. doi: 10.1016/j.nbd.2020.104873. Epub 2020 Apr 19.
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.
髓母细胞瘤是最常见的儿童脑恶性肿瘤，死亡率高。最近利用全基因组技术的大规模研究将髓母细胞瘤分为四个主要亚组：无翅（WNT）、 sonic hedgehog（SHH）、group 3 和 group 4。然而，目前还没有对髓母细胞瘤中调控转录组的重要组成部分——长非编码 RNA 进行全面分析。在这里，我们对 175 名髓母细胞瘤患者的 RNA-seq 数据进行了生物信息学分析。差异 lncRNA 表达将髓母细胞瘤分为四个主要的分子亚组。其中一些 lncRNAs 是亚组特异性的，基于随机森林的机器学习算法确定了一个 11-lncRNA 诊断特征。我们还在患者来源的异种移植（PDX）模型中验证了该诊断特征。我们进一步使用基于 LASSO 的惩罚 Cox' PH 模型（Score HR = 13.6301，95%CI = 8.857-20.98，logrank p 值≤ 2e-16）确定了一个 17-lncRNA 预后模型。我们的分析代表了髓母细胞瘤中第一个全局 lncRNA 分析。我们的结果确定了一些潜在的候选 lncRNAs，它们可以作为髓母细胞瘤发生和进展的功能研究的候选，也可以作为诊断和预后的生物标志物。
