TY - JOUR
T1 - The new paradigm of network medicine to analyze breast cancer phenotypes
AU - Grimaldi, Anna Maria
AU - Conte, Federica
AU - Pane, Katia
AU - Fiscon, Giulia
AU - Mirabelli, Peppino
AU - Baselice, Simona
AU - Giannatiempo, Rosa
AU - Messina, Francesco
AU - Franzese, Monica
AU - Salvatore, Marco
AU - Paci, Paola
AU - Incoronato, Mariarosaria
N1 - Funding Information:
Funding: This work was supported by the Ministry of Health under contract “Ricerca Corrente RRC-2020-23669967” and in part under contract “5 per mille 2015 5M-2015-2360353”.
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/9/2
Y1 - 2020/9/2
N2 - 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.
AB - 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.
KW - Breast cancer
KW - Disease modules
KW - Network Medicine
KW - Network-based algorithm
KW - Switch genes and Interactome
KW - TCGA
UR - http://www.scopus.com/inward/record.url?scp=85090561001&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090561001&partnerID=8YFLogxK
U2 - 10.3390/ijms21186690
DO - 10.3390/ijms21186690
M3 - Article
C2 - 32932728
AN - SCOPUS:85090561001
VL - 21
SP - 1
EP - 21
JO - International Journal of Molecular Sciences
JF - International Journal of Molecular Sciences
SN - 1661-6596
IS - 18
M1 - 6690
ER -