Open AccessBayesian networks established functional differences between breast cancer subtypes
Author(s) -
Lucía Trilla-Fuertes,
Angelo GámezPozo,
Jorge M. Arevalillo,
Rocío López-Vacas,
Elena López-Camacho,
Guillermo Prado-Vázquez,
Andrea Zapater-Moros,
Mariana Díaz-Almirón,
María Ferrer-Gómez,
Hilario Navarro,
Paolo Nanni,
Pilar Zamora,
Enrique Espinosa,
Paloma Maı́n,
Juan Ángel Fresno Vara
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0234752
Subject(s) - breast cancer , context (archaeology) , biology , oncology , proteomics , cancer , bioinformatics , triple negative breast cancer , computational biology , medicine , genetics , gene , paleontology
Breast cancer is a heterogeneous disease. In clinical practice, tumors are classified as hormonal receptor positive, Her2 positive and triple negative tumors. In previous works, our group defined a new hormonal receptor positive subgroup, the TN-like subtype, which had a prognosis and a molecular profile more similar to triple negative tumors. In this study, proteomics and Bayesian networks were used to characterize protein relationships in 96 breast tumor samples. Components obtained by these methods had a clear functional structure. The analysis of these components suggested differences in processes such as mitochondrial function or extracellular matrix between breast cancer subtypes, including our new defined subtype TN-like. In addition, one of the components, mainly related with extracellular matrix processes, had prognostic value in this cohort. Functional approaches allow to build hypotheses about regulatory mechanisms and to establish new relationships among proteins in the breast cancer context.