LC−MS/MS Analysis of Ovarian Cancer Metastasis-Related Proteins Using a Nude Mouse Model: 14-3-3 Zeta as a Candidate Biomarker
datasetposted on 03.12.2010, 00:00 by Yifeng He, Xin Wu, Xiaohui Liu, Guoquan Yan, Congjian Xu
Peritoneal implantation is the most common metastatic pattern of epithelial ovarian cancer, and the five-year survival rate of patients is dramatically decreased when large-scale peritoneal metastasis occurs. This study aimed to determine serum proteins that could be used to detect early peritoneal metastasis of ovarian cancer. The secreted (or shed) proteins of the ovarian cancer cell line SKOV-3 were analyzed using LC−MS/MS, and 97 proteins were identified in the SKOV-3 culture supernatant. After the SKOV-3 cells were xenografted into the peritoneal cavities of nude mice, 3 of the 97 proteins were detected in animal sera. Following enzyme-linked immunosorbent assay (ELISA)-based screening of clinical blood samples, one of the three proteins, 14-3-3 zeta, was identified as a candidate biomarker. The average serum levels of 14-3-3 zeta in patients with epithelial ovarian cancer and benign gynecological diseases were significantly different. The expression of 14-3-3 zeta was associated with the degree of cancer peritoneal metastasis, the emergence of ascites, bilateral involvement, and the clinical stage and substage. Using 14-3-3 zeta, the overall diagnostic accuracy for ovarian cancer was greatly improved. Furthermore, siRNA-based experiments demonstrated that 14-3-3 zeta was responsible for approximately 62, 65, and 30% of the migratory, invasive, and implantation abilities of SKOV-3 cells, respectively. The present results demonstrated that the nude mouse xenograft model is an efficient system for performing function-oriented biomarker discovery, which can be used for a variety of research tasks in future molecular diagnoses, targeted therapies, and ovarian cancer vaccine development.