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Title: Machine Learning para caracterizar ARNs circulares en exosomas de sangre periférica como biomarcadores
Author: Gómez Valenzuela, Carmen
Director: Ventura Royo, Carles  
Tutor: Pagès Pinós, Amadís
Others: Universitat Oberta de Catalunya
Keywords: machine learning
exosome complex
Issue Date: Jun-2018
Publisher: Universitat Oberta de Catalunya
Abstract: Covalently closed circular RNA molecules (circRNAs) have recently emerged as a class of RNA isoforms with widespread and tissue-specific expression. circRNAs are remarkably stable and highly expressed molecules. Emerging evidence reveals that they might act as micro RNAs sponges, and also there is evidence of their involvement in different types of cancer. Exosomes are small membrane vesicles of endocytic origin secreted by most cell types and they are thought to play important roles in intercellular communications. It has been shown that tumour cells tend to produce more exosomes than healthy cells and that they can be detected in human peripheral blood. Additionally, in RNA sequencing, in whole blood as well as in exosomes, thousands of circRNAs have been consistently detected. Here we have quantified the circRNAs present in exosomes of peripheral blood of healthy people and with three types of cancer: colorectal, hepatocellular and pancreatic. Using the expression of these circRNAs, we have been able to discriminate, with high precision, between healthy individuals and patients of each cancer group using Machine Learning techniques, reinforcing the hypothesis that circRNAs present in peripheral blood exosomes are very promising biomarkers since they are expressed differently for different types of cancer. Additionally, a review has been made about the micro RNAs on which the most expressed circRNAs could be acting as sponges, finding evidence of the implication of the dysregulation of these micro RNAs in the three types of carcinomas.
Language: Spanish
Appears in Collections:Bachelor thesis, research projects, etc.

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Presentacion_TFM_GomezCarmen.mp4Presentación del trabajo final1.82 GBMP4View/Open
carmengmzTFM0618memoria.pdfMemoria del TFM2.69 MBAdobe PDFView/Open

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