OBJECTIVE: To screen relatively specific biomarkers in serum of ovarian cancer patients using surface-enhanced laser desorption and ionization time of flight mass spectrometry and protein chip technology. METHODS: Serum samples from 99 ovarian cancer patients, 87 healthy volunteers and 21 patients with other ovarian diseases were analyzed using metal affinity chromatography protein chip IMAC3 and cation exchange protein chip WCX2, which can specifically bind the metal-combining-proteins. Proteomic spectra were generated by mass spectrometry. The peaks were detected and filtrated by Ciphergen proteinchip software 3.2.0. Using the Biomarker Pattern 5.0 software, a diagnostic system was developed for separating ovarian cancer from the healthy group. RESULTS: Thirty-one protein peaks were significantly different between ovarian cancer patients and healthy controls (P < 0.05). A diagnostic model consisting of protein peaks from IMAC3 and WCX2 was established. Large scale blind test generated a sensitivity of 100% and specificity of 98% respectively. A differentially expressed protein with a mass-to-charge rate of 7769 was identified as potential biomarkers to distinguish ovarian cancer from other ovarian diseases. CONCLUSIONS: Time of flight mass spectrometry is a useful tool for the detection and identification of new protein markers in serum. It will provide a highly accurate and innovative approach for the diagnosis of ovarian cancer.
OBJECTIVE: To screen relatively specific biomarkers in serum of ovarian cancerpatients using surface-enhanced laser desorption and ionization time of flight mass spectrometry and protein chip technology. METHODS: Serum samples from 99 ovarian cancerpatients, 87 healthy volunteers and 21 patients with other ovarian diseases were analyzed using metal affinity chromatography protein chip IMAC3 and cation exchange protein chip WCX2, which can specifically bind the metal-combining-proteins. Proteomic spectra were generated by mass spectrometry. The peaks were detected and filtrated by Ciphergen proteinchip software 3.2.0. Using the Biomarker Pattern 5.0 software, a diagnostic system was developed for separating ovarian cancer from the healthy group. RESULTS: Thirty-one protein peaks were significantly different between ovarian cancerpatients and healthy controls (P < 0.05). A diagnostic model consisting of protein peaks from IMAC3 and WCX2 was established. Large scale blind test generated a sensitivity of 100% and specificity of 98% respectively. A differentially expressed protein with a mass-to-charge rate of 7769 was identified as potential biomarkers to distinguish ovarian cancer from other ovarian diseases. CONCLUSIONS: Time of flight mass spectrometry is a useful tool for the detection and identification of new protein markers in serum. It will provide a highly accurate and innovative approach for the diagnosis of ovarian cancer.