Korean J Gynecol Oncol.  2006 Jun;17(2):147-156.

Identification of biomarker for ovarian cancer by serum proteomic analysis using SELDI-ToF-MS

  • 1Department of Obstetrics and Gynecology, Inje University Sanggye Paik Hospital, Korea. morula@chol.com
  • 2Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea.


SELDI-ToF-MS is an affinity-based mass spectrometric method. This study was performed to determine feasibility of serum proteomic pattern analysis using SELDI-ToF-MS for the detection of ovarian cancer.
Forty-three epithelial ovarian cancer patients and seventy-seven controls were included in the study from October 2003 to March 2005 in Sanggye Paik Hospital. Potential tumor biomarkers in sixty serum samples were screened, including twenty-one ovarian cancers and thirty-nine controls. Proteomic pattern was analyzed by SELDI-ToF-MS and optimal discriminating m/z value with proper cutoff of log-normalized intensity was determined by decision tree analysis (Phase I). Another sixty serum samples were obtained from twenty-two ovarian cancers and thirty-eight controls. Through analysis using SELDI-ToF-MS, the performance of diagnosing ovarian cancer was determined by applying previously adopted cutoff log-normalized intensity of m/z value determined in Phase I experiment (Phase II).
A biomarker of 3501.23 kDa was selected based on the collective contribution to the optimal separation between ovarian cancers and controls. Sensitivity of 90.9% and specificity of 84.2% was achieved by SELDI-ToF-MS in Phase II experiment. Age, stage, and histologic type did not affect performance of SELDI-ToF-MS for diagnosing ovarian cancer.
Serum proteomic analysis by biochip and mass spectrometry is a feasible method in diagnosing ovarian cancer.


SELDI-ToF-MS; Ovarian cancer; Biomarker; Proteomics; Bioinformatics

MeSH Terms

Computational Biology
Decision Trees
Mass Spectrometry
Ovarian Neoplasms*
Sensitivity and Specificity
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization*
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