OBJECTIVE: Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy, and early tumor detection is the most promising approach for improving the EOC survival rate. The goal of this study was to identify the biomarkers underlying ovarian carcinogenesis. STUDY DESIGN: To mimic the onset and progression of human ovarian cancer, we established a rat model of ovarian neoplasm by implanting 7,12-dimethylbenz(a)anthracene (DMBA)-coated silk cloth strips onto the ovaries. Sera collected from rats bearing serous ovarian carcinoma (SOC) at baseline, 12 and 24 weeks after DMBA treatment and from controls were analyzed using iTRAQ combined with two-dimensional liquid chromatography and tandem mass spectrometry. The data were analyzed with ProteinPilot software for peptide matching, protein identification, and protein quantitation. Ingenuity pathway analysis software was used to identify the canonical pathways and biological interaction networks of differentially expressed proteins. RESULTS: The cumulative ovarian tumor incidence rate reached 75% at 32 weeks after DMBA treatment. Out of all tumors, 94% were EOC, and 51% of the EOC cases were SOC. A total of 225 unique, non-redundant proteins were identified with 95% confidence. Twenty-seven differentially expressed proteins were significantly up- or down-regulated during the early or advanced carcinogenesis of SOC. Fifteen proteins were previously reported to be involved in ovarian cancer, and 12 proteins, including MMRN1, SERPINC1, TLN1, AHSG, PLG, APOA2, HPX, APOC1, APOC2, FERMT3, FETUB and HBB, were identified for the first time in our study. CONCLUSION: The discovery of these differentially expressed proteins provides valuable clues for understanding the molecular mechanism underlying the dynamic carcinogenic process of ovarian cancer. These proteins could be used as diagnostic biomarkers for early detection, disease monitoring and therapeutic targets.
OBJECTIVE:Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy, and early tumor detection is the most promising approach for improving the EOC survival rate. The goal of this study was to identify the biomarkers underlying ovarian carcinogenesis. STUDY DESIGN: To mimic the onset and progression of humanovarian cancer, we established a rat model of ovarian neoplasm by implanting 7,12-dimethylbenz(a)anthracene (DMBA)-coated silk cloth strips onto the ovaries. Sera collected from rats bearing serous ovarian carcinoma (SOC) at baseline, 12 and 24 weeks after DMBA treatment and from controls were analyzed using iTRAQ combined with two-dimensional liquid chromatography and tandem mass spectrometry. The data were analyzed with ProteinPilot software for peptide matching, protein identification, and protein quantitation. Ingenuity pathway analysis software was used to identify the canonical pathways and biological interaction networks of differentially expressed proteins. RESULTS: The cumulative ovarian tumor incidence rate reached 75% at 32 weeks after DMBA treatment. Out of all tumors, 94% were EOC, and 51% of the EOC cases were SOC. A total of 225 unique, non-redundant proteins were identified with 95% confidence. Twenty-seven differentially expressed proteins were significantly up- or down-regulated during the early or advanced carcinogenesis of SOC. Fifteen proteins were previously reported to be involved in ovarian cancer, and 12 proteins, including MMRN1, SERPINC1, TLN1, AHSG, PLG, APOA2, HPX, APOC1, APOC2, FERMT3, FETUB and HBB, were identified for the first time in our study. CONCLUSION: The discovery of these differentially expressed proteins provides valuable clues for understanding the molecular mechanism underlying the dynamic carcinogenic process of ovarian cancer. These proteins could be used as diagnostic biomarkers for early detection, disease monitoring and therapeutic targets.