The new paradigm of network medicine to analyze breast cancer phenotypes

Anna Maria Grimaldi, Federica Conte, Katia Pane, Giulia Fiscon, Peppino Mirabelli, Simona Baselice, Rosa Giannatiempo, Francesco Messina, Monica Franzese, Marco Salvatore, Paola Paci, Mariarosaria Incoronato

Research output: Contribution to journalArticlepeer-review

Abstract

Breast cancer (BC) is a heterogeneous and complex disease as witnessed by the existence of different subtypes and clinical characteristics that poses significant challenges in disease management. The complexity of this tumor may rely on the highly interconnected nature of the various biological processes as stated by the new paradigm of Network Medicine. We explored The Cancer Genome Atlas (TCGA)-BRCA data set, by applying the network-based algorithm named SWItch Miner, and mapping the findings on the human interactome to capture the molecular interconnections associated with the disease modules. To characterize BC phenotypes, we constructed protein–protein interaction modules based on “hub genes”, called switch genes, both common and specific to the four tumor subtypes. Transcriptomic profiles of patients were stratified according to both clinical (immunohistochemistry) and genetic (PAM50) classifications. 266 and 372 switch genes were identified from immunohistochemistry and PAM50 classifications, respectively. Moreover, the identified switch genes were functionally characterized to select an interconnected pathway of disease genes. By intersecting the common switch genes of the two classifications, we selected a unique signature of 28 disease genes that were BC subtype-independent and classification subtype-independent. Data were validated both in vitro (10 BC cell lines) and ex vivo (66 BC tissues) experiments. Results showed that four of these hub proteins (AURKA, CDC45, ESPL1, and RAD54L) were over-expressed in all tumor subtypes. Moreover, the inhibition of one of the identified switch genes (AURKA) similarly affected all BC subtypes. In conclusion, using a network-based approach, we identified a common BC disease module which might reflect its pathological signature, suggesting a new vision to face with the disease heterogeneity.

Original languageEnglish
Article number6690
Pages (from-to)1-21
Number of pages21
JournalInternational Journal of Molecular Sciences
Volume21
Issue number18
DOIs
Publication statusPublished - Sep 2 2020

Keywords

  • Breast cancer
  • Disease modules
  • Network Medicine
  • Network-based algorithm
  • Switch genes and Interactome
  • TCGA

ASJC Scopus subject areas

  • Catalysis
  • Molecular Biology
  • Spectroscopy
  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Inorganic Chemistry

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