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

• 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Cancer och onkologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cancer and Oncology (hsv//eng)

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)

NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

NATURVETENSKAP  -- Biologi -- Biokemi och molekylärbiologi (hsv//swe)

NATURAL SCIENCES  -- Biological Sciences -- Biochemistry and Molecular Biology (hsv//eng)

Nyckelord

breast cancer;outcome prediction;molecular biomarker;immunohistochemistry;model validation

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