Identification of Long Non-coding RNAs as Novel Biomarkers for Heterogeneous Glioblastomas

项目总结 多形性胶质母细胞瘤(GBM)是最恶性的脑肿瘤，超过18,000例 在美国，每年有新诊断的患者和13,000人死亡。GBM的预后仍然存在 GBM患者确诊后中位生存期为14~16个月。恶性的标志 基底膜是它们在肿瘤内的高度异质性。这种独特的特征表现在分子亚型上。 表现出独特的发病机制、生物学和预后模式的基底膜。而特定的分子标记 在临床护理中有价值(例如，IDH1/2突变，1p/19q共同缺失)，显著改善预后 迫切需要分层和靶向治疗。最终目标是实现全部潜力 对于个性化和精确化的医学，我们建议在一个 临床基底膜标本队列。LncRNAs是一类非编码的RNAs，已经成为关键的 通过基因调控在不同的细胞过程中发挥调节作用。之前的研究包括癌症 基因组图谱(TCGA)虽然受到不是为非编码RNA设计的图谱技术的限制，但有 提示lncRNAs在人类癌症中含量丰富，并具有高度的癌症类型特异性。特别是， LncRNAs与脑功能和胶质瘤的发生有关。具体地说，在这个探索中 项目，我们将把基于Ribo-Zero的转录片段测序(RNA-seq)全面应用于 西北大学收集的100例临床肾小球基底膜标本中的lncRNA特征 脑肿瘤组织库(目标1a)。与之前使用的基于寡聚(DT)的RNA-SEQ形成对比 包括TCGA在内的研究表明，基于Ribo-Zero的技术针对非编码RNA转录本进行了优化，因此 为分析GBM中所有潜在功能的lncRNA提供了巨大的优势。值得注意的是，一种新的检测 将开发基于机器学习的算法，以提供更灵活和通用的框架 用RNA-seq.检测LncRNA虽然仅限于我们的GBM数据和 基于寡核苷酸(DT)的TCGA，我们将使用TCGA数据评估在GBM中检测到的lncRNAs的组织特异性 在几个实体瘤上(目标1b)。在描述了GBM中lncRNAs的特征之后，我们将评估 LncRNAs是否与GBM患者的临床结局相关，并评估其可行性 将lncRNAs和基因水平的转录本整合为一个预后工具(目标2a)。这项提议将使我们能够 将这些新的生物标志物应用于GBM的预后以及未来的功能研究。此外，我们 将利用共表达网络分析来为GBM中检测到的LncRNAs分配功能。一个完整的、 将建立基于互联网的目录，以提供GBM中的LncRNA资源，这将使 这一新领域的一般研究界(目标2b)。最后，私家侦探们组装了一个杰出的 在相关研究领域取得重大成就并具有互补专业知识的研究团队，因此 确保这一多学科和高度创新的项目的成功。

PROJECT SUMMARY Glioblastoma multiforme (GBM) is the most malignant form of brain tumors with more than 18,000 newly diagnosed patients and 13,000 deaths annually in the United States. The prognosis for GBM remains dismal with a median survival time of GBM patients of 14 to 16 months after diagnosis. A hallmark of malignant GBM is their high heterogeneity within the tumors. This unique characteristic manifests to molecular subtypes of GBM that display unique patterns of pathogenesis, biology, and prognosis. While specific molecular markers are of value in clinical care (e.g., IDH1/2 mutations, 1p/19q co-deletion), significant improvement in prognostic stratification and targeted therapeutics are urgently needed. With the ultimate goal of realizing the full potential of personalized and precision medicine, we propose to interrogate long non-coding RNAs (lncRNAs) in a cohort of clinical GBM specimens. LncRNAs are a class of non-coding RNAs that have emerged as critical modulators in various cellular processes through gene regulation. Previous studies including The Cancer Genome Atlas (TCGA), though limited by their profiling technique not designed for non-coding RNAs, have suggested that lncRNAs are abundant in human cancers and are highly cancer-type-specific. In particular, lncRNAs have been implicated in brain function and glioma development. Specifically, in this exploratory project, we will apply the Ribo-Zero-based transcriptomic sequencing (RNA-seq) to comprehensively characterize lncRNAs in 100 clinical GBM samples that have been collected at the Northwestern University Brain Tumor Tissue Bank (Aim 1a). In contrast to the oligo(dT)-based RNA-seq that was used by previous studies, including TCGA, the Ribo-Zero-based technique is optimized for non-coding RNA transcripts, thus offering a great advantage for profiling all potentially functional lncRNAs in GBM. Notably, a novel detection algorithm based on machine learning will be developed to provide a more flexible and universal framework of lncRNA detection using RNA-seq. Though restricted to those lncRNAs shared between our GBM data and the oligo(dT)-based TCGA, we will evaluate the tissue-specificity of lncRNAs detected in GBM using TCGA data on several solid tumors (Aim 1b). After characterizing the landscape of lncRNAs in GBM, we will evaluate whether lncRNAs are associated with the clinical outcomes of GBM patients, and evaluate the feasibility of integrating lncRNAs and gene-level transcripts into a prognostic tool (Aim 2a). This proposal will enable us to employ these novel biomarkers for the prognosis of GBM as well as future functional studies. In addition, we will utilize co-expression network analysis to assign functions to the detected lncRNAs in GBM. An integrated, internet-based catalog will be constructed to provide a resource of lncRNAs in GBM that will benefit the general research community in this new area (Aim 2b). Finally, the PIs have assembled an outstanding research team with significant achievements in relevant research areas and complimentary expertise, therefore ensuring the success of this multidisciplinary and highly innovative project.