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Additive effect of the AZGP1, PIP, S100A8, and UBE2C molecular biomarkers improves outcome prediction in breast carcinoma

Journal article
Authors Toshima Z Parris
Anikó Kovács
Luaay Aziz
Shahin Hajizadeh
Szilard Nemes
May Semaan
Eva Forssell-Aronsson
Per Karlsson
Khalil Helou
Published in International Journal of Cancer
Volume 134
Issue 7
Pages 1617–1629
ISSN 0020-7136
Publication year 2014
Published at Institute of Clinical Sciences, Department of Radiation Physics
Institute of Clinical Sciences, Department of Oncology
Sahlgrenska Cancer Center
Institute of Clinical Sciences, Department of Otorhinolaryngology
Pages 1617–1629
Language en
Keywords breast cancer;outcome prediction;molecular biomarker;immunohistochemistry;model validation
Subject categories Statistics, Molecular biology, Cancer and Oncology


The deregulation of key cellular pathways is fundamental for the survival and expansion of neoplastic cells, which in turn can have a detrimental effect on patient outcome. To develop effective individualized cancer therapies, we need to have a better understanding of which cellular pathways are perturbed in a genetically defined subgroup of patients. Here, we validate the prognostic value of a 13-marker signature in independent gene expression microarray datasets (n = 1,141) and immunohistochemistry with full-faced FFPE samples (n = 71). The predictive performance of individual markers and panels containing multiple markers was assessed using Cox regression analysis. In the external gene expression dataset, six of the 13 genes (AZGP1, NME5, S100A8, SCUBE2, STC2, and UBE2C) retained their prognostic potential and were significantly associated with disease-free survival (P < 0.001). Protein analyses refined the signature to a four-marker panel (AZGP1, PIP, S100A8, and UBE2C) significantly correlated with cycling, high grade tumors and lower disease-specific survival rates. AZGP1 and PIP were found in significantly lower levels in invasive breast tissue compared with adjacent normal tissue, whereas elevated levels of S100A8 and UBE2C were observed. A predictive model containing the four-marker panel in conjunction with established clinical variables outperformed a model containing the clinical variables alone. Our findings suggest that deregulated AZGP1, PIP, S100A8, and UBE2C are critical for the aggressive breast cancer phenotype, which may be useful as novel therapeutic targets for drug development to complement established clinical variables. © 2013 Wiley Periodicals, Inc.

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