An integrated approach utilizing proteomics and bioinformatics to detect ovarian cancer  

作　　者：余捷凯 郑树 唐勇 李力 

机构地区：[1]Cancer Institute, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China School of Life Science, Zhejiang University, Hangzhou 310029, China [2]Cancer Institute, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China [3]Affiliated Tumor Hospital of Guangxi Medical University, Nanning 530021, China

出　　处：《Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)》2005年第4期227-231,共5页浙江大学学报（英文版）B辑（生物医学与生物技术）

基　　金：Project (No. G1998051200) supported by the National Basic Research Program (973) of China

摘　　要：Objective: To find new potential biomarkers and establish the patterns for the detection of ovarian cancer. Methods: Sixty one serum samples including 32 ovarian cancer patients and 29 healthy people were detected by surface-enhanced laser desorption/ionization mass spectrometry (SELDI-MS). The protein fingerprint data were analyzed by bioinformatics tools. Ten folds cross-validation support vector machine (SVM) was used to establish the diagnostic pattern. Results: Five potential bio- markers were found (2085 Da, 5881 Da, 7564 Da, 9422 Da, 6044 Da), combined with which the diagnostic pattern separated the ovarian cancer from the healthy samples with a sensitivity of 96.7%, a specificity of 96.7% and a positive predictive value of 96.7%. Conclusions: The combination of SELDI with bioinformatics tools could find new biomarkers and establish patterns with high sensitivity and specificity for the detection of ovarian cancer.Objective: To find new potential biomarkers and establish the patterns for the detection of ovarian cancer. Methods: Sixty one serum samples including 32 ovarian cancer patients and 29 healthy people were detected by surface-enhanced laser desorption/ionization mass spectrometry (SELDI-MS). The protein fingerprint data were analyzed by bioinformatics tools. Ten folds cross-validation support vector machine (SVM) was used to establish the diagnostic pattern. Results: Five potential bio- markers were found (2085 Da, 5881 Da, 7564 Da, 9422 Da, 6044 Da), combined with which the diagnostic pattern separated the ovarian cancer from the healthy samples with a sensitivity of 96.7%, a specificity of 96.7% and a positive predictive value of 96.7%. Conclusions: The combination of SELDI with bioinformatics tools could find new biomarkers and establish patterns with high sensitivity and specificity for the detection of ovarian cancer.

关 键 词：Ovarian cancer SVM DIAGNOSIS SELDI-TOF PROTEOMICS 

分 类 号：R737.31[医药卫生—肿瘤]