Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/99007
Title: Study of the transcriptional function of cyclin D1 in leukemia
Author: Milán Otero, Antonio
Casas-Roma, Jordi  
BARCELÓ, PhD, CARLES  
Abstract: Leukemia is a type of cancer that starts in blood-forming tissue, such as the bone marrow. It causes the production of large numbers of abnormal blood cells that end up entering into the bloodstream. Within the different types of leukemia, Mantle Cell Lymphoma (MCL) is the one with the worst prognosis due to the short survival average of a patient, which is close to three years. This tumor is characterized by the over-expression of Cyclin D1, a protein that helps control cell division. MCL is also characterized by the binding of this protein to certain regions of DNA involved in the regulation of DNA-damage response (DDR). The presented study aims to identify similarities between the gene expression regulated by Cyclin D1 in lymphomas and the gene expression in DNA damage. That knowledge will allow the exploration of essential mechanisms of carcinogenesis and help in the identification of genes that could be an interesting therapeutic target in the process of tumor progression. Additionally, new biomarkers that could be used in early diagnosis can be found. With the addition of machine learning algorithms to the biology analysis pipeline, this project explores new ways to improve the traditional methodologies and boost the identification of significantly enriched genes that will serve the purposes mentioned above. The result of such a pipeline is the accurate selection of genes correlated with Cyclin D1, involved in MCL and DDR, and its posterior analysis and identification of significantly enriched gene sets. As a conclusion, the results obtained in this study suggested that targeting of Notch pathway and studying potential common mechanisms of hypoxia and apoptosis resistance would be of great interest for possible future studies on treatments of MCL.
Keywords: cyclin D1
leukemia
machine learning
Type: info:eu-repo/semantics/masterThesis
Issue Date: Jun-2019
Publication license: http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Appears in Collections:Bachelor thesis, research projects, etc.

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