Establishment of a diagnostic model from plasma proteomic patterns of ovarian serous cystadenocarcinoma patients using SELDI-TOF-MS technology / 肿瘤

ABSTRACT

Objective: To screen differentiated expressed proteins in plasma of ovarian serous cystadenocarcinoma patients by using surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS) associated with bioinformatic support vector machines (SVM) and discuss how to establish algorithmic logical model for diagnosis of ovarian serous cys-tadenocarcinoma and its significance. Methods: SELDI-TOF-MS and CM10 chip were used to analyze the plasma samples from 26 ovarian serous cystadenocarcinoma women and 51 control women including 12 cases of ovarian cyst, 31 cases of uterous leiomyoma, 8 cases of ovarian benign cystadenoma. The data was analyzed by Biomarker Wizard software. The plasma proteomic diagnostic model for ovarian serous cys-tadenocarcinoma patients and control subjects were established by using SVM (a bioinformatic method). Results: Seventy-one differentiated protein peaks were screened by Biomarker Wizard software which were captured by SELDI-TOF-MS from CM10 chip (P <0.01). The proteomic profiling for ovarian serous cystadenocarcinoma was optimized by SVM re-screening. The key m/z value of these 7 proteins was 4 099, 4 477, 4 123, 4 081 and 3 938 (up-regulated), 8 785 and 13 783 (down-regulated). Three-fold cross validation followed by blinded determination demonstrated that the sensitivity and specificity of the established model were 84.62% and 96.08% separately, and the positive predictive value was 92.21% for differential diagnosis of ovarian serous cystadenocarcinoma patients. Conclusion: ProteinChip-mass spectrometry technology can rapidly and effectively screen differentiated proteins from the plasma of ovarian serous cystadenocarcinoma patients. Combined with SVM, a diagnostic model was generated from proteomic patterns of ovarian serous cystadenocarcinoma, which had potential significance for establishing diagnostic methods for ovarian cancer.