Characterization and modeling of m6A RNA methylation in cancer

癌症中 m6A RNA 甲基化的表征和建模

• 批准号: 10027689
• 负责人: Han Liang
• 金额: $ 56.55万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10027689
• 关键词: Alternative Splicing; Bioinformatics; Biological; Biological Markers; Biology; Biomedical Research; Cancer Biology; Cancer Patient; Cancer cell line; Chicago; Classification; Clinical; Clinical assessments; Colorectal Cancer; Communities; Computational Biology; Computer Analysis; Data; Databases; Development; Disease; Event; Expression Profiling; Genomics; Goals; Human; Investigation; Knowledge; Life Cycle Stages; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of urinary bladder; Messenger RNA; Mission; Modeling; Modification; Molecular; Molecular Abnormality; Outcome; Pathogenesis; Patients; Pattern; Phase; Play; Polyadenylation; Process; Protein Array; Proteins; Proteomics; Protocols documentation; Public Health; RNA; RNA Editing; RNA methylation; Reader; Research; Resources; Role; Sampling; Skin Cancer; Standardization; The Cancer Genome Atlas; Therapeutic; Tumor Subtype; United States National Institutes of Health; Universities; University of Texas M D Anderson Cancer Center; anticancer research; base; bioinformatics resource; biomedical resource; cancer biomarkers; cancer genomics; cancer initiation; cancer therapy; cancer type; clinically relevant; cohort; deep learning; disability; effective therapy; genome-wide; innovation; malignant breast neoplasm; methylation pattern; novel; potential biomarker; predictive modeling; prognostic; protein biomarkers; protein expression; therapeutic target; tumor; user-friendly

ABSTRACT The most abundant internal mRNA modification is N6-methyladenosine (m6A), and growing evidence has suggested its critical roles in cancer. However, the global patterns of m6A RNA modification and its regulators over large patient cohorts are not available. It remains unclear how m6A RNA modification contributes to cancer initiation/progression and how it may be used in cancer therapy. The objective is to systematically characterize the genome-wide patterns of m6A RNA modification and its regulators using well-characterized The Cancer Genome Atlas (TCGA) patient cohorts, elucidate their interactions with other molecular aberrations, and assess their potential clinical utility. The working hypothesis is that the dysregulation of m6A RNA methylation plays critical roles in cancer development and may represent potential biomarkers and therapeutic targets. We will pursue three specific aims: Aim #1. Generate the genome-wide profiles of m6A RNA methylation using TCGA sample cohorts. As part of an NCI Functional Proteomic Center, our team has unique access to these samples. We have developed a sensitive, robust m6A-seq protocol, and will apply it to ~1,000 patient samples from diverse cancer types, and generate high-quality, standardized m6A genome-wide profile data. Aim #2. Generate the protein expression profiles of m6A regulators using TCGA sample cohorts. Using the MD Anderson reverse-phase protein array platform, we will characterize the expression levels of ~30 protein markers (including both total and phosphorylated proteins) of 15 m6A regulators (five writers, two readers, and eight erasers) over ~8,000 samples of 31 cancer types as well as ~400 common cancer cell lines. Aim #3. Perform the integrative analysis and modeling of m6A RNA methylation data in a rich TCGA context. Using TCGA multi-dimensional molecular data, we will develop predictive models that quantify the effects of various factors involved in m6A RNA modification by deep learning. We will perform analyses to define m6A-based tumor subtypes, assess the clinical utility of m6A-related markers, and study the interactions of m6A with other molecular aberrations in diverse tumor contexts. Finally, we will build a publicly available, user-friendly database that will contain comprehensive information of the m6A data generated through Aim #1 and Aim #2. The expected outcome of this project is (i) the establishment of an integrated resource of m6A-related genomic and proteomic data based on the most widely used cancer patient cohorts, so that further investigation of such data can be conducted by the cancer research community fluently; and (ii) assessment of the biological and clinical utility of m6A RNA methylation for cancer therapy in a comprehensive way. This project is innovative because it will systematically assess the clinical relevance and functions of a key class of RNA modifications that are currently understudied in cancer research. These results will have an important positive impact because the knowledge gained will not only greatly advance our understanding of the role of m6A RNA methylation in cancer development, but also directly facilitate the development of a novel class of cancer biomarkers and therapeutic targets.

抽象的 最丰富的内部mRNA修饰是N6-甲基二胺（M6A），越来越多的证据已经 提出了其在癌症中的关键作用。但是，M6A RNA修饰及其调节器的全球模式 大型患者队列不可用。目前尚不清楚M6A RNA修饰如何有助于癌症 起始/进展及其如何用于癌症治疗。目的是系统地表征 M6A RNA修饰及其调节剂的全基因组模式使用癌症良好 基因组图集（TCGA）患者队列，阐明他们与其他分子畸变的相互作用，并评估 他们的潜在临床实用性。工作假设是M6A RNA甲基化功能的失调 在癌症发展中的关键作用，可能代表潜在的生物标志物和治疗靶标。我们将 追求三个特定目标：目标＃1。使用使用M6A RNA甲基化的全基因组轮廓 TCGA样品队列。作为NCI功能性蛋白质组学中心的一部分，我们的团队可以独特地访问这些中心 样品。我们已经开发了一种敏感，强大的M6A-SEQ方案，并将其应用于约1,000个患者样本 来自各种癌症类型，并产生高质量的标准化M6A基因组概况数据。目标＃2。 使用TCGA样品队列生成M6A调节剂的蛋白质表达谱。使用MD Anderson反相蛋白阵列平台，我们将表征30个蛋白质标记的表达水平 （包括总蛋白质和磷酸化蛋白）15 M6A调节剂（5名作家，2名读者和8个 橡皮擦）超过31种癌症类型以及约400种常见癌细胞系的约8,000个样本。目标＃3。履行 M6A RNA甲基化数据在丰富的TCGA环境中的综合分析和建模。使用TCGA 多维分子数据，我们将开发预测模型来量化各种因素的影响 通过深度学习参与M6A RNA修饰。我们将进行分析以定义基于M6A的肿瘤 亚型，评估与M6A相关标记的临床效用，并研究M6A与其他分子的相互作用 在各种肿瘤环境中的畸变。最后，我们将构建一个公开可用的，用户友好的数据库 包含通过AIM＃1和AIM＃2生成的M6A数据的全面信息。预期 该项目的结果是（i）建立与M6A相关基因组和蛋白质组学的综合资源 基于最广泛使用的癌症患者队列的数据，因此可以进一步研究此类数据 癌症研究界流利地进行； （ii）评估生物学和临床实用性的 M6A RNA甲基化以全面的方式进行癌症治疗。这个项目具有创新性，因为它将 系统地评估当前是关键RNA修改类别的临床相关性和功能 在癌症研究中进行了研究。这些结果将产生重要的积极影响，因为知识 获得的不仅会大大提高我们对M6A RNA甲基化在癌症发展中的作用的理解， 但也直接促进了一类新型的癌症生物标志物和治疗靶标的发展。