Current ovarian cancer biomarkers are inadequate because of their relatively low diagnostic sensitivity and specificity. There is a need to discover and validate novel ovarian cancer biomarkers that are suitable for early diagnosis, monitoring, and prediction of therapeutic response. We performed an in-depth proteomics analysis of ovarian cancer ascites fluid. Size exclusion chromatography and ultrafiltration were used to remove high abundance proteins with molecular mass >/=30 kDa. After trypsin digestion, the subproteome (</=30 kDa) of ascites fluid was determined by two-dimensional liquid chromatography-tandem mass spectrometry. Filtering criteria were used to select potential ovarian cancer biomarker candidates. By combining data from different size exclusion and ultrafiltration fractionation protocols, we identified 445 proteins from the soluble ascites fraction using a two-dimensional linear ion trap mass spectrometer. Among these were 25 proteins previously identified as ovarian cancer biomarkers. After applying a set of filtering criteria to reduce the number of potential biomarker candidates, we identified 52 proteins for which further clinical validation is warranted. Our proteomics approach for discovering novel ovarian cancer biomarkers appears to be highly efficient because it was able to identify 25 known biomarkers and 52 new candidate biomarkers that warrant further validation.