Análisis de variantes genéticas asociadas al cáncer de mama y su impacto en la prevalencia de la enfermedad estudiando diferentes poblaciones

Abstract

Breast cancer is a clinically relevant neoplasm due to its marked molecular heterogeneity, reflected in transcriptomic, regulatory, and genetic differences that influence tumor progression, therapeutic response, prognosis, and biological behavior of the disease. Therefore, its characterization using bioinformatics approaches allows for a more precise understanding of the variability among tumor subtypes, the recognition of mechanisms associated with aggressiveness, and provides useful information for the identification of potential biomarkers. Samples corresponding to the estrogen receptor-positive, progesterone receptor-positive, human epidermal growth factor receptor type 2- positive, and triple-negative breast cancer subtypes, obtained from NCBI, were analyzed using a bioinformatics workflow that included quality control, read preprocessing, alignment to the reference human genome, gene quantification, differential expression analysis, functional enrichment, detection of alternative splicing events, identification of genetic variants, functional annotation, and complementary review of prioritized candidate genes. The results showed that triple-negative breast cancer presented the most divergent transcriptomic profile, with a greater number of differentially expressed genes and processes associated with cell proliferation, DNA repair, RNA processing, extracellular matrix, tumor metabolism, and alternative splicing. The integration of differential expression, splicing, and variants allowed prioritizing genes such as QSOX1, MTCH2, and PTPRN2, providing relevant bioinformatics evidence for future research aimed at the experimental validation of biomarkers and the development of precision medicine strategies with possible application in the personalized management of breast cancer.