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Molecular Pathogenic Mechanism of Rhabdomyosarcoma

Molecular Pathogenic Mechanism of Rhabdomyosarcoma
横纹肌肉瘤的分子发病机制
批准号:
8157684
负责人:
Liang Cao
金额:
$25.47万
依托单位国家:
美国
项目类别:
财政年份:
--
资助国家:
美国
项目状态:
未结题
起止时间:

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中文摘要
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英文摘要
To understand the molecular pathogenic mechanism of PAX3-FKHR in the development of RMS, we used ChIP-seq to map PAX3-FKHR genomic binding sites associated with 1072 genes in RMS cells. I. The data shows that PAX3-FKHR binds to the same sites as PAX3 at enhancers for MYF5, FGFR4, as well as the MYOD core enhancer previously shown to be downstream of PAX3 regulation. Moreover, our dataset has the precision for a rapid identification and validation of novel and specific sequences required for the enhancer activity for MYOD and FGFR4. II. The genome wide analysis reveals that the vast majority of PAX3-FKHR sites are: 1) distal to transcription start sites; 2) conserved; 3) enriched for PAX3 motifs; 4) strongly associated with genes over-expressed in PAX3-FKHR positive RMS cells and tumors. There is little evidence in our dataset for PAX3-FKHR binding at the promoters. In one instance, our data establishes two intronic enhancer elements for MET, rather than at the previously described promoter. The genome-wide analysis further illustrates a strong association between PAX3 and E-box motifs in these binding sites, suggestive of a common co-regulation for many target genes. III. The map of PAX3-FKHR binding sites provides new links for PAX3 and PAX3-FKHR functions and new targets for RMS therapy. Our study identifies genes that are critically important for different aspects of limb-genesis, as direct PAX3-FKHR targets. Further, we identify IGF1R as a direct target for PAX3-FKHR. The dependence on IGF1R for survival in some RMS cells with PAX3-FKHR makes it an ideal therapeutic target for this cancer.
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Preclinical Development of Novel Targeted Therapeutics Against Pediatric Sarcoma
Molecular Pathogenic Mechanism of Rhabdomyosarcoma
Omics Technology facility
Biomarker Investigations for Clinical Trials
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
  • 批准号:
    --
  • 项目类别:
    --
  • 资助金额:
    40万元
  • 批准年份:
    2020
  • 负责人:
    Vikrant Gupta
  • 依托单位:
基于Linked Open Data的Web服务语义互操作关键技术
  • 批准号:
    61373035
  • 项目类别:
    面上项目
  • 资助金额:
    77.0万元
  • 批准年份:
    2013
  • 负责人:
    冯志勇
  • 依托单位: