The New Paradigm of Network Medicine to Analyze Breast Cancer Phenotypes
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 proc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/21/18/6690 |
| _version_ | 1797553830518325248 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Anna Maria Grimaldi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T16:22:13Z |
| format | Article |
| id | doaj.art-0edbf0c6bb6b486e8225c5d99e954e41 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-10T16:22:13Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-0edbf0c6bb6b486e8225c5d99e954e412023-11-20T13:33:06ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672020-09-012118669010.3390/ijms21186690The New Paradigm of Network Medicine to Analyze Breast Cancer PhenotypesAnna Maria Grimaldi0Federica Conte1Katia Pane2Giulia Fiscon3Peppino Mirabelli4Simona Baselice5Rosa Giannatiempo6Francesco Messina7Monica Franzese8Marco Salvatore9Paola Paci10Mariarosaria Incoronato11IRCCS SDN, Via Emanuele Gianturco 113, 80143 Naples, ItalyInstitute for Systems Analysis and Computer Science “Antonio Ruberti”, National Research Council, 00185 Rome, ItalyIRCCS SDN, Via Emanuele Gianturco 113, 80143 Naples, ItalyInstitute for Systems Analysis and Computer Science “Antonio Ruberti”, National Research Council, 00185 Rome, ItalyIRCCS SDN, Via Emanuele Gianturco 113, 80143 Naples, ItalyIRCCS SDN, Via Emanuele Gianturco 113, 80143 Naples, ItalyOspedale Evangelico Betania, Via Argine 604, 80147 Naples, ItalyOspedale Evangelico Betania, Via Argine 604, 80147 Naples, ItalyIRCCS SDN, Via Emanuele Gianturco 113, 80143 Naples, ItalyIRCCS SDN, Via Emanuele Gianturco 113, 80143 Naples, ItalyDepartment of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, ItalyIRCCS SDN, Via Emanuele Gianturco 113, 80143 Naples, ItalyBreast 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.https://www.mdpi.com/1422-0067/21/18/6690breast cancerNetwork MedicineTCGANetwork-based algorithmDisease modulesSwitch genes and Interactome |
| spellingShingle | 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 The New Paradigm of Network Medicine to Analyze Breast Cancer Phenotypes International Journal of Molecular Sciences breast cancer Network Medicine TCGA Network-based algorithm Disease modules Switch genes and Interactome |
| title | The New Paradigm of Network Medicine to Analyze Breast Cancer Phenotypes |
| title_full | The New Paradigm of Network Medicine to Analyze Breast Cancer Phenotypes |
| title_fullStr | The New Paradigm of Network Medicine to Analyze Breast Cancer Phenotypes |
| title_full_unstemmed | The New Paradigm of Network Medicine to Analyze Breast Cancer Phenotypes |
| title_short | The New Paradigm of Network Medicine to Analyze Breast Cancer Phenotypes |
| title_sort | new paradigm of network medicine to analyze breast cancer phenotypes |
| topic | breast cancer Network Medicine TCGA Network-based algorithm Disease modules Switch genes and Interactome |
| url | https://www.mdpi.com/1422-0067/21/18/6690 |
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