Publication details

Využití profilů genové exprese primárních kolorektálních karcinomů k predikci jejich progrese a metastatického fenotypu

Title in English Usage of low-density oligonucleotide microarrays for prognosis prediction of colorectal cancer patients.
Authors

SLABÝ Ondřej GARAJOVÁ Ingrid SVOBODA Marek FABIAN Pavel SVOBODA Miroslav SROVNAL Josef VYZULA Rostislav

Year of publication 2006
Type Article in Proceedings
Conference II. Dny diagnostické, prediktivní a experimentální onkologie
MU Faculty or unit

Faculty of Medicine

Citation
web http://www.linkos.cz/kongresy/abstrakta_vypis.php?ID=1614
Field Oncology and hematology
Keywords colorectal cancer; DNA microarray technology; gene expression; pathogenesis; prognosis; prediction
Description Colorectal cancer (CRC) is one of the most common malignancies. Unfortunately a significant proportion of surgically cured patiens in the early stage of the disease develop progression and die from the disease. Twelve patients who had histologically confirmed left-sided colon adenocarcinoma were included. Only stage II-III patients according to IUCC with no prior chemotherapy or radiotherapy were eligible for the study. Six patients were poor prognosis cases width disease free survival (DFS) lower then 36 month and six were good prognosis cases with DFS>36 month. Relative gene expression levels of 128 genes potentially involved in cancer progression and dissemination were obtained by low-density oligonucleotide microarrays (SuperArray Bioscience Corp., Bethesda, MD) from 12 primary colon cancer samples. Gene expression data analysis based on the SAM and t-test methods identified 10 genes with significantly different expression in primary tumors of patients with poor prognosis. Our preliminary data suggest that oligonucleotide microarray technology should contribute to a better understanding of the progression of colorectal cancer, and facilitate prediction of their metastatic potential. Analyzing of gene expression data from larger group of CRC patients will enable us to identify distinct prognostic subsets of patients based on molecular characteristics in the near future.

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