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Ovarian cancer is the most lethal gynecological malignancy, accounting up to 6% of death by cancer in women, yet there is no diagnostic marker with sufficient specificity to enable non-invasive diagnosis. Moreover, there is an increasing need for non-histological diagnosis to define the various tumor types, as it significantly affects the choice of post-operative chemotherapy. This study was aimed at identifying novel biomarkers for ovarian cancer, while focusing on making molecular profiles of tumor types; clear cell, endometrioid, serous and mucinous.

This is the first comprehensive study in human ovarian cancer using tissue samples from clinically diagnosed patients. The total protein contents of tissues were separated and visualized by 2-DE and individual protein spots were identified by peptide mass fingerprinting or MS/MS by AXIMA-QIT.

We have so far analyzed total proteins extracted from 40 clinical ovarian tumor samples and compared the expression patterns across the four tumor types. Meanwhile we identified, by peptide mass fingerprint, more than 10,000 spots harvested from 2D-E of 11 tumor samples to generate an integrated 2D-map database. Several proteins were identified that displayed tumor type specific expression pattern, collectively comprising protein expression profiles that may be used as a molecular diagnostic indicator. The result was validated by conventional bioassay as well as our custom-designed cDNA microarray. The list of candidates we demonstrated here together with the elaborate database will contribute to development of a new diagnostic tool for patients with early stage of ovarian cancers.

98th AACR Annual Meeting-- Apr 14-18, 2007; Los Angeles, CA