Identification of Genes for Predicting Prognosis in Pedi

预测足病预后的基因鉴定

• 批准号: 7070810
• 负责人: Javed KHAN
• 金额: --
• 依托单位: DIVISION OF CLINICAL SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7070810
• 关键词: biomarker; cancer information system; diagnosis design /evaluation; gene expression; genetic markers; growth factor receptors; human tissue; microarray technology; neoplasm /cancer classification /staging; neoplasm /cancer diagnosis; neoplasm /cancer genetics; neoplastic growth; neuroblastoma; pediatric neoplasm /cancer; pharmacogenetics; polymerase chain reaction; prognosis; protein tyrosine kinase; tissue /cell culture; transcription factor

Neuroblastomas are cancers of neural crest origin with variable prognoses depending on age at presentation, stage, histology, presence of MYCN amplification, chromosomal ploidy, and deletion status of 1p36. Very little is known of the molecular mechanisms that confer good or poor prognosis in this and other malignancies. We have recently demonstrated that cancers can be diagnosed on the basis of gene expression profiling using cDNA microarrays and sophisticated pattern recognition algorithms such as Artificial Neural Networks. The Oncogenomics Section has expanded this concept further by profiling a series on neuroblastomas of different stages and prognosis. With these methods we are identifying tumor-specific expression patterns, or "fingerprints," that uniquely identify a poor prognostic group, as well as those associated with specific genetic aberrations including MYCN amplification. By these techniques, we hope to classify expression profiles that correlate with prognosis and hence identified the genes that confer these biological properties. Once we have narrowed down the list of genes that defines a particular cancer or diagnostic or prognostic group cluster to a minimum number, we will use this to make smaller microarrays or other multiplex PCR-based assays for diagnostic purposes in the clinic.Isotope-coded affinity tags (ICAT), allows the quantitative measurement of protein expression levels in different cell types and tissues. In this method proteins from two samples can be compared by chemically labeling both samples with the light and heavy isotopic forms of a reagent respectively. With this method we plan to sequence and identify up to 3000-4000 differentially expressed proteins between tumors with poor (death) and good (event free survival > 3yrs) outcome. These proteins represent potential targets for therapy, diagnostic and prognostic markers for high-risk patients as well as provide important clues on the biology of these tumors that fail to respond to conventional therapy.

神经母细胞瘤是神经rest起源的癌症，预后可变，具体取决于演示，阶段，组织学，MYCN扩增的存在，染色体倍性和1P36的缺失状态。对于在这种和其他恶性肿瘤中赋予预后良好或不良的分子机制知之甚少。我们最近证明，可以使用cDNA微阵列和复杂的模式识别算法（如人工神经网络）来诊断癌症。致癌基因组学部分通过分析了有关不同阶段和预后的神经母细胞瘤的系列，从而进一步扩展了这一概念。通过这些方法，我们正在识别肿瘤特异性表达模式或“指纹”，这些模式独特地识别出差的预后群，以及与特定遗传畸变（包括MyCN扩增）相关的群体。通过这些技术，我们希望对与预后相关的表达谱进行分类，从而确定赋予这些生物学特性的基因。一旦我们缩小了将特定癌症或诊断或预后组聚类定义为最小数量的基因列表，我们将使用它来在临床上进行较小的微阵列或其他基于多重PCR的测定，以诊断诊断。与异位座编码的亲和力标签（ICAT）（ICAT），可以在不同的细胞类型和组织中的定量测量表达水平和组织中的定量测量水平。在这种方法中，可以通过分别用试剂的光和重型同位素形式对两个样品进行化学标记，从两个样品中进行比较。通过这种方法，我们计划在肿瘤（死亡）和良好（无事件生存> 3年）的肿瘤之间进行测序和鉴定多达3000-4000个差异表达的蛋白质。这些蛋白质代表了对高危患者的治疗，诊断和预后标记的潜在靶标，并为这些肿瘤的生物学提供了重要的线索，这些线索未能对常规疗法做出反应。