Utilização de uma rede complexa na avaliação do microambiente do câncer de mama com hiperexpressão de HER-2

Abstract

Breast cancer is the most common type of neoplasm in women worldwide. In recent years, knowledge about breast cancer has advanced significantly, and we are now aware of the multifactorial nature of its pathophysiology. Multiple molecular and cellular factors are involved in a complex network of interactions, reflecting the difficulty in advancing effective oncological treatments. One theoretical model used to study these interactions is the Graph Model, also known as Complex Networks. This method creates graphical representations that connect vertices (factors) through edges (interactions), utilizing a matrix-based approach. Using this methodology, we aimed to define the complex interactions between the molecular and cellular factors involved in the tumor microenvironment of HER2-positive breast cancer, followed by manipulating elements of the network to assess the impact of each factor. Through this analysis, we sought to identify potential therapeutic targets and mechanisms for cancer treatment. A narrative literature review was conducted to select elements for constructing a complex network, resulting in 37 molecular and cellular factors. These factors were incorporated into the network, with HER-2 as the starting vertex. The impact of each vertex on the network was determined by calculating the relative error. The network was manipulated by performing knockout analyses on the vertices to identify potential targets that could influence the network. The Wilcoxon test was used to analyze the statistical significance of each knockout. Among the vertices whose knockouts significantly altered the network structure were MMP 2 and 9, CDK4/6, TWIST, VEGF, and TGF-Beta. We highlight the impact of knocking out the matrix metalloproteinases MMP 2 and 9, which caused a downregulation of the HER-2 vertex, the starting point of the complex network. Despite the limitations of a theoretical experimental model, the results obtained from this complex network model may predict the development of new oncological therapies for the treatment of HER-2 positive breast cancer in the near future.