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.
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