Micro RNA Profiling in Pediatric Cancers

1.确定microRNA谱是儿科癌症的良好生物标志物。2. 识别作为预测预后的生物标志物的microRNA。3.确定基因组背景 microRNA表达，以及它们如何与mRNA表达相关4.识别miRNAs 在神经母细胞瘤中控制关键基因的基因组改变区域。5.功能 验证选定的miRNAs 6. microRNA的突变筛选 发现了在植物和动物基因组中编码的小的非编码RNA分子。这些 高度保守的21-mer RNA通过与基因的结合来调节基因的表达。 特定mRNA的3 '-非翻译区（3'-UTR），导致翻译抑制或mRNA 降解由于许多mRNA可能共享这个短序列，因此microRNA能够 同时影响大量基因的表达。估计每 microRNA可以靶向数百个基因，多个microRNA可以靶向单个基因。到目前为止 据报道，710种microRNA（版本11.0）在人类细胞中表达 (http：//microrna.sanger.ac.uk/）。由于它们在基因表达中的调节作用， 越来越多的证据表明，microRNA不仅直接参与正常的胚胎发生， 代谢，细胞生长，分化和凋亡，而且在人类的发病机制， 癌的因为大多数儿科恶性肿瘤是发育性肿瘤，即，这类因素来自 异常分化，我们假设儿童肿瘤将表现出癌症和组织 特异性microRNA表达谱，可能与发育相关， 肿瘤发生过程中，可用于诊断和预后。本研究 研究一组儿科细胞系和肿瘤的microRNA表达谱 异种移植物，其mRNA谱是，并且目前用作儿科 用于药物筛选的临床前模型。使用这组代表10种不同 我们研究了不同类型的儿科肿瘤是否差异表达microRNA 根据他们的诊断，通过在内部microRNA阵列上进行microRNA分析。我们 使用机器学习算法和统计学来识别可能被 用作这些癌症类型的生物标志物。我们将在独立的NB上验证这些发现 和RMS肿瘤样品。此外，我们还探讨了microRNA的共调节程度， 与它们所在的宿主基因相关联。我们还研究了关键的microRNAs， 具有生物学相关性，可用作RMS的预后标志物。

1. Identify microRNA profiles that are good biomarkers of pediatric cancers. 2. Identify microRNA that are biomarkers for predicting prognosis. 3. Identify genomic context of microRNA expression, and how they correlate with mRNA expression 4. Identify miRNAs in genomically altered regions that control critical genes in neuroblastoma. 5. Functional validation of selected miRNAs 6. Mutational screening of microRNAs MicroRNAs are recently discovered small, non-coding RNA molecules encoded in the genomes of plants and animals. These highly conserved, 21-mer RNAs regulate the expression of genes by binding to their 3'-untranslated regions (3'-UTR) of specific mRNAs, causing translational inhibition or mRNA degradation. As many mRNAs may share this short sequence, microRNAs are capable of simultaneously influence the expression of a large set of genes. It is estimated that each microRNA can target hundreds of genes, and multiple microRNAs can target a single gene. So far 710 microRNAs (version 11.0) have been reported to be expressed in human cells (http://microrna.sanger.ac.uk/). Due to their regulatory roles in gene expression, there is increasing evidence that microRNAs are directly involved not only in normal embryogenesis, metabolism, cell growth, differentiation, and apoptosis, but also in pathogenesis of human cancers. Because most pediatric malignancies are developmental tumors, i.e., they arise from aberrant differentiation, we hypothesized that pediatric tumors will exhibit cancer and tissue specific microRNA expression profiles that may be associated with development and the tumorigenic process, which can be used in diagnosis and prognosis. In this study we investigate the expression profiles of microRNAs for a panel of pediatric cell lines and tumor xenografts for which mRNAs profiles were, and which are currently used as pediatric pre-clinical models for drug screening. Using this panel of samples representing 10 different types of pediatric tumors we explore if pediatric tumors differentially express microRNAs according to their diagnosis by performing microRNA profiling on in-house microRNA arrays. We use machine learning algorithm and statistical to identify microRNAs that can potentially be used as biomarkers for these cancer types. We will validate these findings on independent NB and RMS tumor samples. In addition, we explore the degree of co-regulation of the microRNAs with the host gene within which they are located. We are also investigate key microRNAs that are biologically relevant and can be used as prognostic markers in RMS.