Discrimination analysis of mass spectrometry proteomics for ovarian cancer detection

• 来源期刊: 中国药理学报(英文版)
• 关键词: discrimination analysis proteomics ovarian cancer mass spectrometry
• 年，卷（期）: 2008,（10）
• 页码范围: 1240-1246
• 页数: 7页
• 语种: 英文
• DOI: 10.1111/j.1745-7254.2008.00861.x

摘要

Aim:A discrimination analysis has been explored for the probabilistic classifica-tion of healthy versus ovarian cancer serum samples using proteomics data from mass spectrometry (MS).Methods:The method employs data normalization,clustering,and a linear discriminant analysis on surface-enhanced laser desorp-tion ionization (SELDI) time-of-flight MS data.The probabilistic classification method computes the optimal linear discriminant using the complex human blood serum SELDI spectra.Cross-validation and training/testing data-split experi-ments are conducted to verify the optimal discriminant and demonstrate the accu-racy and robustness of the method.Results:The cluster discrimination method achieves excellent performance.The sensitivity,specificity,and positive predic-tive values are above 97% on ovarian cancer.The protein fraction peaks,which significantly contribute to the classification,can be available from the analysis process.Conclusion:The discrimination analysis helps the molecular identities of differentially expressed proteins and peptides between the healthy and ovarian patients.