期刊
EUROPEAN JOURNAL OF CANCER
卷 47, 期 5, 页码 792-801出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ejca.2010.11.028
关键词
Protein kinase CK2 alpha subunit; Quantitative immunohistochemistry; Breast carcinoma; Tissue micro-arrays; CK2 kinase assay; Western blotting
类别
Background: CK2 alpha is a signalling molecule that participates in major events in solid tumour progression. The aim of this study was to evaluate the prognostic significance of the immunohistochemical expression of CK2 alpha in breast carcinomas. Methods: Quantitative measurements of immunohistochemical expression of 33 biomarkers using high-throughput densitometry, assessed on digitised microscopic tissue microarray images were correlated with clinical outcome in 1000 breast carcinomas using univariate and multivariate analyses. Results: In univariate analysis, CK2 alpha was a significant prognostic indicator (p < 0.001). Moreover, a multivariable model allowed the selection of the best combination of the 33 biomarkers to predict patients' outcome through logistic regression. A nine-marker signature highly predictive of metastatic risk, associating SHARP-2, STAT1, eIF4E, pmap-KAPk-2, pAKT, caveolin, VEGF, FGF-1 and CK2 alpha permitted to classify well 82.32% of patients (specificity 81.59%, sensitivity 92.55%, area under ROC curve 0.939). Importantly, in a node negative subset of patients an even more (86%) clinically relevant association of eleven markers was found predictive of poor outcome. Conclusion: A strong quantitative CK2 alpha immunohistochemical expression in breast carcinomas is individually a significant indicator of poor prognosis. Moreover, an immunohistochemical signature of 11 markers including CK2 alpha accurately (86%) well classifies node negative patients in good and poor outcome subsets. Our results suggest that CK2 alpha evaluation together with key downstream CK2 targets might be a useful tool to identify patients at high risk of distant metastases and that CK2 can be considered as a relevant target for potential specific therapy. (C) 2010 Elsevier Ltd. All rights reserved.
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