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26 February 2010

Breast cancer relapse prediction based on multi-gene RT-PCR algorithm

Elzbieta PluciennikADEF, Maciej KrolCD, Magdalena NowakowskaAEF, Renata KusinskaBC, Piotr PotemskiBC, Radzislaw KordekABDEG, Andrzej K. BednarekABCDEFG

Med Sci Monit 2010; 16(3): CR132-136 :: ID: 878462

Abstract

Background
Breast cancer is very heterogeneous disease at both the clinical and molecular levels. Most research is based on analysis of a single gene, but only complex investigation of genes involved in different cell processes such as apoptosis or signal transduction can help to better understand the biology of this type of tumour. Novel techniques such as microarrays and real-time RT-PCR allow performance of such complex research. Only this kind of approach can improve cancer treatment through individualisation of disease cases with different molecular backgrounds.
Material and Method
We performed quantitative RT-PCR to analyze levels of expression of 10 genes in 119 patient samples: 4 with known good prognosis signature (WWOX, ESR1, CDH, BAX) and 6 previously reported as bad prognosis markers of breast cancer (KRT5, KRT14, KRT17, CCNE1, BCL2, BIRC5).
Results
The algorithm composed of 10 genes distinguishes 2 statistically significant groups of patients with different rates of disease-free survival. However, when patients were divided into 2 groups according to estrogen receptor status, this algorithm could be applied only for a group with estrogen receptor negative breast cancer. High algorithm value is a good prognostic factor of disease-free survival for patients with estrogen negative breast cancers (HR=0.26; p=0.0039), but not for patients with ER positive tumors (p>0.05).
Conclusions
The presented multigene algorithm may be used for outcome evaluation for estrogen receptor-negative breast cancer patients.

Keywords: Recurrence, Proportional Hazards Models, Prognosis, Genes, Neoplasm - genetics, Disease-Free Survival, Breast Neoplasms - genetics, Algorithms, Aged, 80 and over, Reverse Transcriptase Polymerase Chain Reaction - methods

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Medical Science Monitor eISSN: 1643-3750
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