Priorización de genes y búsqueda de dianas terapéuticas por medio de herramientas informáticas y técnicas de aprendizaje automatizado en cáncer de mama

Abstract

Breast cancer (BC) is the leading cause of cancer-related death among women and the most commonly diagnosed cancer worldwide. BC is a heterogeneous disease where genomic alterations, protein expression deregulation, signaling pathway alterations, hormone disruption, ethnicity and environmental determinants are involved. Despite the technological and scientific advances in recent years, an understanding of molecular processes, the identification of new therapeutic targets and the prediction of proteins involved in immunotherapy, metastasis, and RNA binding is essential for drug development and application of precision medicine in clinical practice. The current thesis proposes the development of a high efficient consensus strategy in the recognition of genes and proteins associated with BC; the oncological validation of these prioritized genes and proteins using the OncoOmics strategy, which consisted of the analysis of outstanding experimental databases; the identification of oncogenic mutations and essential drugs for the development and application of precision medicine; and the prediction of BC proteins associated with immunotherapy, metastasis and RNA-binding using bioinformatics tools and artificial intelligence methods. All results were published in international journals with a significant impact factor.