ECM Remodeling in Breast Cancer with Different Grade: Contribution of 2D‐DIGE Proteomics
Tumor extracellular matrix (ECM) plays a pivotal role in outcome of breast cancer (BC) patients. Overexpression of 58 genes, encoding 43 structural ECM proteins, has been identified to determine a specific cluster of BC with accelerated metastatic potential only in the undifferentiated (grade III) p...
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Published in | Proteomics (Weinheim) Vol. 18; no. 24; pp. e1800278 - n/a |
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Main Authors | , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Germany
01.12.2018
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Subjects | |
Online Access | Get full text |
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Summary: | Tumor extracellular matrix (ECM) plays a pivotal role in outcome of breast cancer (BC) patients. Overexpression of 58 genes, encoding 43 structural ECM proteins, has been identified to determine a specific cluster of BC with accelerated metastatic potential only in the undifferentiated (grade III) phenotype. The scope of this study is to characterize protein repertoire able to predict patient outcome in BC according to ECM gene expression pattern and histological grade. The differential proteomic analysis is based on 2D‐differential gel electrophoresis, MALDI–MS, bioinformatics, and immunoblotting. Results suggest a relationship among ECM remodeling, signal mechanotransduction, and metabolic rewiring in BCs characterized by a specific mRNA ECM signature and identified a set of dysregulated proteins characteristic of hormone receptors expression as fibrinogen‐β chain, collagen α‐1(VI) chain, and α‐1B‐glycoprotein. Furthermore, in triple negative tumors with ECM signature, the FGG and α5β1/αvβ3 integrins increase whereas detyrosinated α‐tubulin and mimecan decrease leading to unorganized integrin presentation involving focal adhesion kinase, activation of Rho GTPases associated to epithelial mesenchymal transition. In hormone receptors negative BCs characterized by a specific ECM gene cluster, the differentially regulated proteins, identified in the present study, can be potentially relevant to predict patient's outcome. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1615-9853 1615-9861 |
DOI: | 10.1002/pmic.201800278 |