Objectives.: The purpose of this study is to discover potential biomarkers for the detection and monitoring of adjuvant chemotherapy for ovarian cancer. Methods.: Serum samples from ovarian cancers and non-cancer controls were analyzed using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry(SELDI-TOF-MS). To discover the possible diagnostic biomarker for ovarian cancer, a preliminary training set of spectra derived from 31 primary ovarian cancer patients, 16 patients with benign ovarian diseases, and 25 healthy women was used to develop a proteomic model that discriminated cancer from non-cancer effectively. A blind test set, including 43 new cases, was used to validate the sensitivity and specificity of this multivariate model. To explore treatment-induced serum protein change, the protein profiles generated from 16 postoperative patients before chemotherapy are compared with those obtained after chemotherapy. Results.: A Four-peak model was established in the training set that discriminated cancer from non-cancer with sensitivity of 90.8%and specificity of 93.5%. A sensitivity of 87.0%and a specificity of 95.0%for the blind test were obtained, compared with 60.7%, 55%for CA125 for the same samples. These 4 markers performed significantly better than the current standard marker, CA125(P< 0.05). One protein peak(mass/charge ratio[m/z], 4475) was identified in 12 of 16(75%) postoperative patients after chemotherapy, but was absent before chemotherapy. Conclusion.: The proteins represented by these peaks are candidate biomarkers for ovarian cancer diagnosis and/or monitoring treatment response. Objectives .: The purpose of this study is to discover potential biomarkers for the detection and monitoring of adjuvant chemotherapy for ovarian cancer. Methods .: Serum samples from ovarian cancers and non-cancer controls were analyzed using surface-enhanced laser desorption / ionization time- of-flight mass spectrometry (SELDI-TOF-MS). To discover the possible diagnostic biomarker for ovarian cancer, a preliminary training set of spectra derived from 31 primary ovarian cancer patients, 16 patients with benign ovarian diseases, and 25 healthy women was used To develop a proteomic model that discriminated cancer from non-cancer effectively. A blind test set, including 43 new cases, was used to validate the sensitivity and specificity of this multivariate model. To explore treatment-induced serum protein change, the protein profiles generated from 16 postoperative patients before chemotherapy are compared with those obtained after chemotherapy. Results .: A Four-peak model was established in The sensitivity of the training set that discriminated cancer from non-cancer with 90.8% and specificity of 93.5%. A sensitivity of 87.0% and a specificity of 95.0% for the blind test were obtained, compared with 60.7%, 55% for CA125 for the One protein peak (mass / charge ratio [m / z], 4475) was identified in 12 of 16 (75%) postoperative patients after chemotherapy, but was absent before chemotherapy. Conclusion .: The Proteins represented by these peaks are candidate biomarkers for ovarian cancer diagnosis and / or monitoring treatment response.