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|Issue Date: ||15-Mar-2013|
|Authors: ||Cascione, Luciano|
|Title: ||Application of computational and statistical methods to High-throughput gene and microRNA expression to assess their roles in cancer|
|Abstract: ||High throughput technologies have become a key tool in cancer research. The analysis of gene expression profiles can give insights into changes in proteins pathways that occur during malignant transformation and cancer progression.
Transcriptional expression profiling has proven to be a useful and reliable tool for classifying cancers into subgroups that reflect different histopathological characteristics as well as differential prognostic outcome.
In the last decades several studies have demonstrated the crucial role of microRNAs (miRNAs) in human disease in particular in cancer. MiRNAs are small non-protein coding RNAs, able to regulate gene expression at post-transcriptional level, binding the 3'UTR of target genes. A single miRNA can regulate the expression of hundreds of target genes, resulting in either theirs degradation or translational repression. The genome-wide profiling of gene expression and microRNAs will allow investigation of genomic changes in cancer development. When mRNA and microRNA levels are measured in the same sample, an integrative analysis can be performed to compare both profiles and determine their interactions. Here I present the integrated analysis of mRNA and miRNAs expression in tumor, adjacent non-tumor (normal) and lymph node metastatic lesion (mets) tissues, from 251 women with Triple Negative Breast Cancers (TNBC). Tissue specific deregulated miRNAs and mRNAs were identified for normal vs tumor vs mets comparisons. We linked specific miRNA signatures to patient overall survival (OS) and distant disease free survival (DDFS). By multivariate analysis the signatures were independent predictors for OS and DDFS. We used miRNA/mRNA anti-correlations to identify clinically and genetically different TNBC subclasses. We also identified miRNA signatures as potential regulators of TNBC subclass-specific gene expression networks defined by expression of canonical signal pathways using IPA Ingenuity software. mRNA expression profiling resulted in clustering of genes expression into 4 molecular subclasses with different expression signatures anti-correlated with the prognostic miRNAs.
Our findings suggest that miRNAs have a key role in triple negative breast cancer development probably through their ability to regulate fundamental pathways such as: cellular growth and proliferation, cellular movement and migration. The results also define microRNA expression signatures that characterize and contribute to the phenotypic diversity of TNBC and its metastasis.|
|Appears in Collections:||Area 06 - Scienze mediche|
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