Avaliação in sílico do perfil de expressão genica dos tipos e subtipos de câncer de mama

Abstract

Breast cancer is the most common neoplasm among women in Brazil, with notable incidence and mortality rates in the South and Southeast regions. The triple-negative subtype (TNBC) is one of the most aggressive and difficult to treat due to the absence of hormone receptors. This study aims to identify gene co-expression modules associated with TNBC using the Weighted Gene Co-Expression Network Analysis (WGCNA) methodology, based on publicly available data from The Cancer Genome Atlas (TCGA-BRCA). The research was conducted with a quantitative and exploratory approach, utilizing bioinformatics analysis of transcriptomes from 1,032 tumor samples, including 115 TNBC cases and 917 hormonal subtypes. The applied methods involved data filtering and normalization using the DESeq2 package, differential gene expression analysis, and gene co-expression network identification. A total of 21 co-expression modules were identified, with two standing out: "MEblack", associated with hormonal subtypes, and "MEcyan", correlated with TNBC. Functional enrichment analysis revealed that the MEblack module is related to hormonal signaling and glandular development, whereas the MEcyan module is associated with cellular organization and proliferation. Protein-protein interaction (PPI) networks identified key genes involved in tumorigenesis, such as ESR1, ERBB4, PGR, and GATA3, which play a critical role in cell growth regulation and tissue differentiation. This study highlights the importance of gene network analysis in understanding the molecular differences between hormonal and non-hormonal breast cancer subtypes, providing insights into novel biomarkers and therapeutic targets. The application of bioinformatics approaches advances the development of personalized diagnostic and treatment strategies for TNBC, while also contributing to public health policies focused on oncology.