De Giorgi, Valeria (2010) Transcriptional patterns and pathways characterizing HCV-associated neoplastic lesions. [Tesi di dottorato] (Unpublished)


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Item Type: Tesi di dottorato
Uncontrolled Keywords: Genomica , Microarray, Epatocarcinoma
Date Deposited: 02 Dec 2010 09:01
Last Modified: 30 Apr 2014 19:45


The use of gene expression microarrays is particularly important in cancer. This is because the accumulation and combinatorial effects of abnormalities that drive the initiation and malignant progression of cancer result from the altered sequence or expression level of cancer-causing genes. Biological research on HCC mainly concentrates on early detection and diagnosis, elucidation of hepatocarcinogenesis by varieties of etiological factors, and prognosis prediction. Investigations have been conducted at different molecular levels including DNA level, RNA level and protein level, with regard to chromosomal imbalance and genetic instability, epigenetic alteration, gene expression, and gene regulation and translation. Numbers of omics-based methods have been developed and applied. Hepatitis C virus (HCV) infection is a major cause of hepatocellular carcinoma (HCC) worldwide. The precise molecular mechanism underlying the progression of chronic hepatitis viral infections to HCC is currently unknown. The direct or indirect HCV role in HCC pathogenesis is still a controversial issue and additional efforts need to be made aimed to specifically dissect the relationship between stages of HCV chronic infection and progression to HCC. The present study has been focused on investigating the genes/protein and pathways involved in viral carcinogenesis and progression to HCC in HCV-chronically infected patients, to elucidate the molecular mechanisms underlying cancer progression and to identify possible marker for diagnostic purposes trough DNA microarray. In a first approach a pair of liver biopsies from fourteen HCV-positives HCC patients and seven HCV-negative non-liver cancer control patients (during laparoscopic cholecystectomy) were obtained, to investigate genes and pathways involved in viral carcinogenesis and progression to HCC in HCV-chronically infected patients. In a second approach to verify the consistency of the previous data obtained in a very limited sample and to identify a set of genes sufficient for the molecular signature of liver diseases, a pair of liver biopsies from twenty HCV-positive HCC patients, fifteen metastatic patients and six HCV-negative non-liver cancer control patients were collected. Gene expression profiling of liver tissues has been performed using a high-density microarray containing 36'000 oligos, representing 90% of the human genes. Transcriptional profiles identified in liver biopsies from HCC nodules and paired non-adjacent non HCC liver tissue of the same HCV-positive patients and from metastatic patients were compared to those from HCV-negative controls by the Cluster program. The pathway analysis was performed using the BRB-Array- Tools based on the "Ingenuity System Database". Significance threshold of t-test was set at 0.001. The top canonical pathways in HCV-related HCC samples include protein ubiquitination (p=1.67E-05), antigen presentation (p=9.52E-04) and Aryl Hydrocarbon receptor signaling pathway (p=1.37E-03). The top canonical pathways in HCV-related non HCC samples include Interferon Signaling Genes(p=1.12E-05), SAPK/JNK Signaling (p=1.07E-03) and NF-kB Activation by viruses pathway (p=1.19E-03). The top canonical pathways in metastatic samples include Integrin Signaling (p=7.75E-04) and Actin Cytoskeleton Signaling Pathway (p=4.43E-04) In addition a time course analysis was performed to identify markers of tumoral progression between normal liver samples, HCV-related non HCC and HCV-related HCC liver samples. Several molecular markers for early HCC diagnosis have been recognized. In this study, informative data on the global gene expression pattern in HCV-related HCC as well as HCV-related non-HCC counterpart liver tissues have been obtained compared to normal controls. A traditional HCC diagnosis has relied on the use of a single biomarker approach (e.g., AFP). The use of multiple markers in combination to improve the accuracy of identifying HCC cases has been proposed. All these data altogether suggested developing a specific gene-chip along with genes showing the highest fold up-regulation in common in two group of analysed samples representing the different stage of disease. The identification of the lesions and the evaluation of their neoplastic progression will be based on the gene pattern expression on the gene-chip.

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