A disease annotation study of gene signatures in a breast cancer microarray dataset.

Abstract

Breast cancer is a complex disease with heterogeneity between patients regarding prognosis and treatment response. Recent progress in advanced molecular biology techniques and the development of efficient methods for database mining lead to the discovery of promising novel biomarkers for prognosis and prediction of breast cancer. In this paper, we applied three computational algorithms (RFE-LNW, Lasso and FSMLP) to one microarray dataset for breast cancer and compared the obtained gene signatures with a recently described disease-agnostic tool, the Genotator. We identified a panel of 152 genes as a potential prognostic signature and the ERRFI1 gene as possible biomarker of breast cancer disease.

• Authors: Foivos Gypas, Ekaterini S Bei, Michalis Zervakis, Stelios Sfakianakis
• Journal: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference (Annu Int Conf IEEE Eng Med Biol Soc) Vol. 2011 Pg. 5551-4 ( 2011) ISSN: 2694-0604 [Electronic] United States
• PMID: 22255596 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Chemical References

• Biomarkers, Tumor
• Neoplasm Proteins

Topics

• Algorithms
• Biomarkers, Tumor (metabolism)
• Breast Neoplasms (diagnosis, metabolism)
• Female
• Gene Expression Profiling (methods)
• Humans
• Neoplasm Proteins (metabolism)
• Protein Array Analysis (methods)
• Reproducibility of Results
• Sensitivity and Specificity