- Title
- Biomarkers for epithelial ovarian cancers
- Creator
- Mahata, Pritha
- Relation
- 17th International Conference on Genome Informatics. Genome Informatics 2006 (Genome Informatics Series Vol.17 No. 2) (Yokohama, Japan 18-20 December, 2006) p. 184-193
- Relation
- http://www.jsbi.org/journal/GIW06/GIW06F042.html
- Relation
- http://giw.ims.u-tokyo.ac.jp/giw2006
- Publisher
- Universal Academy Press, Inc / Japanese Society for Bioinformatics
- Resource Type
- conference paper
- Date
- 2006
- Description
- Epithelial carcinoma of the ovary is one of the most common gynecological malignancies and the fifth most frequent cause of cancer death in women. Currently blood test of advanced epithelial tumors are reflected in a high level of CA 125 antigen. However, it is not a good marker for early stage tumors, and may yield false positives. Clearly, there is a need for better understanding of the molecular pathogenesis of epithelial ovarian cancer, so that new drug targets or biomarkers that facilitate early detection can be identified. This work concentrates on finding genetic markers for three epithelial ovarian tumors, using a simple computational method. We give a small set of genetic markers which are able to distinguish clear cell and mucinous ovarian cancers (13 and 26 genes respectively) from other epithelial ovarian tumors with 100% accuracy. We obtain the genes HNF1-beta (TCF2) and GGT1 as the best markers for the clear cell and CEACAM6 (NCA) as the best marker for mucinous ovarian tumors. We employ a feature selection technique based on minimum probability of error for this purpose. We give a ranking of the important genes responsible for these tumors and validate the results using the leave-one-out cross-validation technique. Using this method, we also agree with the common notion that WT1 is one of the best genes to separate serous ovarian tumors from other epithelial ovarian tumors.
- Subject
- epithelial carcinoma; ovarian cancer; genetic markers; detection techniques
- Identifier
- uon:2869
- Identifier
- http://hdl.handle.net/1959.13/31933
- Identifier
- ISBN:4946443991
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