Maria Serena Ciaburri
Computational approaches for the identification of candidate chemotheraphy-related lncRNAs in HGSOvCa.
Rel. Elisa Ficarra, Sampsa Hautaniemi. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2018
|
Preview |
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (8MB) | Preview |
Abstract
High grade serous ovarian cancer (HGSOvCa) is a malignant tumor subtype that originates from the female reproductive system. The standard therapies prescribed to HGSOvCa patients include several chemotherapy cycles based on platinum-taxol drugs and a debulking surgery for removing the cancer tissues. A fundamental characteristic of this disease, that drastically decreases the 5-years survival rates, is the acquisition of chemotherapy resistance by the tumoral cells after the first-line treatment. Both the cancer aggressiveness and the development of the platinum resistance increase the necessity of a more effective and targeted therapy.During the last 10 years, a branch of the cancer research has focused its attention on the genomic components called "long non-coding RNAs".
These elements, originating from RNA molecules, do not encode for proteins and are composed by a number of nucleotides that ranges from 200 to 100000
Relatori
Anno Accademico
Tipo di pubblicazione
Numero di pagine
Corso di laurea
Classe di laurea
Ente in cotutela
URI
![]() |
Modifica (riservato agli operatori) |
