Abordagem holística de expressão gênica na oncologia: aplicações de análises de redes ponderadas

Abstract

Cancer is a complex, heterogeneous disease with a broad clinical spectrum, resulting from genetic and epigenetic alterations that generate a diversity of metabolic pathways and consequently cellular phenotypes. These characteristics represent one of the main obstacles to therapeutic interventions, known as tumor heterogeneity. Research in the field of tumor biology seeks to understand cancer genetics by integrating molecular sequencing data made available in databases such as The Cancer Genome Atlas (TCGA). The availability and organization of these data have revolutionized oncological research, aiding in the discovery of subtypes and molecular markers associated with specific tumors. Weighted Gene Co-Expression Network Analysis (WGCNA) is employed to identify co-expression clusters that may represent shared biological pathways. The objective of the study focused on obtaining, selecting, preprocessing, analyzing, and interpreting the identified coexpression modules from weighted network analysis, contextualizing them with corresponding metabolic pathways, and clinical variables of interest. Initially, 479 breast cancer samples, divided into two molecular subtypes, triple-negative (115), and those with hormone receptor-positive (ER+/PR+) (364), were downloaded from the TCGA database. Nineteen co-expression modules were identified, with the red module (MERed) strongly correlated with the ER+/PR+ subtype. Important coexpression modules were identified and related to their respective molecular subtypes, as well as genes with greater intramodular relevance. These findings may corroborate with a better prognosis and potential effective indicators of altered metabolic pathways, becoming potential therapeutic targets.