a classification method based on principal components of seldi spectra to diagnose of lung adenocarcinoma基于主成分的分类方法的seldi光谱诊断肺腺癌.pdfVIP

A Classification Method Based on Principal Components of SELDI Spectra to Diagnose of Lung Adenocarcinoma 1. 2. 1. 1. 3 2 3 Qiang Lin *, Qianqian Peng , Feng Yao , Xu-Feng Pan , Li-Wen Xiong , Yi Wang , Jun-Feng Geng , 4 3 4 3 2 2 5 Jiu-Xian Feng , Bao-Hui Han , Guo-Liang Bao , Yu Yang , Xiaotian Wang , Li Jin , Wensheng Guo , Jiu- Cun Wang2* 1 Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China, 2 Ministry of Education Key Laboratory of Contemporary Anthropology and State Key Laboratory of Genetic Engineering, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China, 3 Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China, 4 Shanghai Chest Cancer Research Institute, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China, 5 Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America Abstract Purpose: Lung cancer is the leading cause of cancer death worldwide, but techniques for effective early diagnosis are still lacking. Proteomics technology has been applied extensively to the study of the proteins involved in carcinogenesis. In this paper, a classification method was developed based on principal components of surface-enhanced laser desorption/ ionization (SELDI) spectral data. This method was applied to SELDI spectral data from 71 lung adenocarcinoma patients and 24 healthy individuals. Unlike other peak-selection-based metho