MicroRNA and Transcription Factor Co-regulation in Cancer

癌症中的 MicroRNA 和转录因子共同调控

基本信息

项目摘要

 DESCRIPTION (provided by applicant): Recent studies have implicated the critical roles of microRNAs (miRNAs) in the pathogenesis of cancer, suggesting that miRNAs can be clinically useful as biomarkers for cancer prognosis, diagnosis and treatment. To date, the miRNA information in cancer studies has varied greatly due to data heterogeneity and disease complexity. In this application, in Aim 1, we will develop novel statistical methods to systematically perform meta- analysis of miRNA expression in the first four cancers (glioblastoma, ovarian cancer, colorectal cancer, and lung cancer) reported by The Cancer Genome Atlas (TCGA) project. For each of these cancers, more than 300 dysregulated miRNAs have been reported, which makes this aim not only feasible but immediately needed. In Aim 2, we will develop innovative strategies to explore miRNAs' functions in cancer through miRNA and transcription factor (TF) co-regulatory network analysis. For each cancer, we will build cancer-specific regulatory networks using miRNA/mRNA co-expression profiling and TF/gene regulation derived from the corresponding TCGA dataset. We will then identify network modules that reflect miRNA and TF co-regulation in cancer. We will investigate both common regulatory modules among four types of cancer and unique modules for each specific cancer. In Aim 3, we will experimentally validate selected miRNAs and their targets in common regulatory modules from Aim 2 using already available tissue and matched normal samples as well as established cell lines. This application will be the first systematic investigation of all available miRNA studes in the first four TCGA cancers. The successful completion of Aim 1 will provide us with a list of evidence-based miRNAs in glioblastoma, ovarian cancer, colorectal cancer, and lung cancer; the successful completion of Aim 2 will provide us with a comprehensive exploration of miRNA and TF co-regulation at the regulatory network level in these cancers; the successful completion of Aim 3 will validate our meta- and network- approaches, help us understand the miRNA regulatory mechanisms, and provide us with potential therapeutic targets in these cancers. Although quite exploratory, we expect this project is highly feasible and timely due to the large amount of data available in literature and from TCGA. This pioneering effort to detect functionally important miRNAs in complex diseases will greatly enhance our understanding of the regulatory systems in cancer, which will likely lead to the development of effective prevention, diagnosis, and treatment strategies.
 描述(由申请人提供):最近的研究已经暗示了微小RNA(miRNA)在癌症发病机制中的关键作用,表明miRNA可以在临床上用作癌症预后、诊断和治疗的生物标志物。到目前为止,由于数据的异质性和疾病的复杂性,癌症研究中的miRNA信息变化很大。在本申请中,在目的1中,我们将开发新的统计方法来系统地进行癌症基因组图谱(TCGA)项目报道的前四种癌症(胶质母细胞瘤、卵巢癌、结直肠癌和肺癌)中miRNA表达的Meta分析。对于这些癌症中的每一种,已经报道了超过300种失调的miRNA,这使得这一目标不仅可行,而且迫切需要。在目标2中,我们将开发创新策略,通过miRNA和转录因子(TF)共调控网络分析来探索miRNA在癌症中的功能。对于每种癌症,我们将使用来自相应TCGA数据集的miRNA/mRNA共表达谱和TF/基因调控来构建癌症特异性调控网络。然后,我们将确定反映癌症中miRNA和TF共调节的网络模块。我们将研究四种类型癌症中的共同调控模块和每种特定癌症的独特模块。在Aim 3中,我们将使用已有的组织和匹配的正常样本以及已建立的细胞系,通过实验验证Aim 2中常见调节模块中选定的miRNA及其靶点。该应用将是对前四种TCGA癌症中所有可用miRNA研究的首次系统研究。Aim 1的成功完成将为我们提供胶质母细胞瘤、卵巢癌、结直肠癌和肺癌中基于证据的miRNA列表; Aim 2的成功完成将为我们提供在这些癌症中在调控网络水平上对miRNA和TF共调控的全面探索;目标3的成功完成将验证我们的Meta方法和网络方法,帮助我们理解miRNA的调控机制,并为我们提供这些癌症的潜在治疗靶点。虽然是探索性的,但由于文献和TCGA中的大量数据,我们预计该项目是高度可行和及时的。这种在复杂疾病中检测功能重要的miRNAs的开创性努力将极大地增强我们对癌症调控系统的理解,这将可能导致有效预防,诊断和治疗策略的发展。

项目成果

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Zhongming Zhao其他文献

Zhongming Zhao的其他文献

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{{ truncateString('Zhongming Zhao', 18)}}的其他基金

Constructing A Transcriptomic Atlas of Retrotransposon in Alzheimer's Disease
构建阿尔茨海默病逆转录转座子转录组图谱
  • 批准号:
    10431366
  • 财政年份:
    2022
  • 资助金额:
    $ 16.75万
  • 项目类别:
Deep learning methods to predict the function of genetic variants in orofacial clefts
深度学习方法预测口颌裂遗传变异的功能
  • 批准号:
    9764346
  • 财政年份:
    2018
  • 资助金额:
    $ 16.75万
  • 项目类别:
Predicting Phenotype by Deep Learning Heterogeneous Multi-Omics Data
通过深度学习异构多组学数据预测表型
  • 批准号:
    10318084
  • 财政年份:
    2017
  • 资助金额:
    $ 16.75万
  • 项目类别:
Predicting Phenotype by Deep Learning Heterogeneous Multi-Omics Data
通过深度学习异构多组学数据预测表型
  • 批准号:
    10640868
  • 财政年份:
    2017
  • 资助金额:
    $ 16.75万
  • 项目类别:
Predicting Phenotype by Using Transcriptomic Alteration as Endophenotype
使用转录组改变作为内表型预测表型
  • 批准号:
    9980998
  • 财政年份:
    2017
  • 资助金额:
    $ 16.75万
  • 项目类别:
Transforming dbGaP genetic and genomic data to FAIR-ready by artificial intelligence and machine learning algorithms
通过人工智能和机器学习算法将 dbGaP 遗传和基因组数据转变为 FAIR-ready
  • 批准号:
    10842954
  • 财政年份:
    2017
  • 资助金额:
    $ 16.75万
  • 项目类别:
Predicting Phenotype by Deep Learning Heterogeneous Multi-Omics Data
通过深度学习异构多组学数据预测表型
  • 批准号:
    10449376
  • 财政年份:
    2017
  • 资助金额:
    $ 16.75万
  • 项目类别:
Predicting Phenotype by Using Transcriptomic Alteration as Endophenotype
使用转录组改变作为内表型预测表型
  • 批准号:
    9750105
  • 财政年份:
    2017
  • 资助金额:
    $ 16.75万
  • 项目类别:
Mapping the Genetic Architecture of Complex Disease via RNA-seq and GWAS
通过 RNA-seq 和 GWAS 绘制复杂疾病的遗传结构
  • 批准号:
    9212507
  • 财政年份:
    2016
  • 资助金额:
    $ 16.75万
  • 项目类别:
MicroRNA and Transcription Factor Co-regulation in Cancer
癌症中的 MicroRNA 和转录因子共同调控
  • 批准号:
    9329385
  • 财政年份:
    2016
  • 资助金额:
    $ 16.75万
  • 项目类别:

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