湖南中医药大学学报2016，Vol.36Issue(4)：19-24,6.DOI:10.3969/j.issn.1674-070X.2016.04.005
大肠癌唾液蛋白指纹图谱分子诊断模型研究
Establishment of Saliva Protein Fingerprint Molecular Diagnostic Models for Screening Colorectal Cancer
摘要
Abstract
〔Abstract〕 Objective To establish a novel molecular diagnostic model of saliva protein fingerprint in colorectal cancer (CRC) patients. Methods Saliva samples from 34 patients with CRC, and 45 healthy people were analyzed by weak cationic-exchange magnetic beads (MB-WCX) and matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) methods. Subsequently, we compared the saliva peptide signatures of the two groups and obtained differently expressed peptides by using of Biomarker Wizard, then establish a diagnostic model to diagnose gastric carcinoma by using of Biomarker Patterns 5.0.2. Results 312 differentially expressed protein peaks were detected, the ratio of two groups>3.0 (CRC/control>3.0, or control/ CRC>3.0), including 37 protein peaks were statistically significant (P<0.05); 7 peaks were up-regulated, 28 peaks were down-regulated, there were 35 different protein peaks have significant difference (P<0.01). Further more, we screened and built a saliva proteomic models with 3 protein molecules m/z 2501.26, 4779.95, 3140.39to distinguish CRC groups and normal groups. The sensitivity of this model was 88% (30/34), and the specificity was 98 %(44/45). The reliability of this model was further verified with a sensitivity of 85% (29/34) and a specificity of 88% (37/45) by cross validation method. Conclusion Saliva proteomic profiling by using MALDI-TOF-MS combined with WCX technique is a novel potential tool for the clinical diagnosis of CRC.关键词
大肠癌/唾液/蛋白质组/分子诊断模型/蛋白指纹图谱/基质辅助激光解析电离飞行时间质谱Key words
colorectal cancer/saliva/proteome/molecular diagnostic model/protein fingerprint/matrix -assisted laser desorption ionization time-of-flight mass spectrometry分类
医药卫生引用本文复制引用
贺佐梅,黄飞娟,周小青,吴正治,谢梦洲..大肠癌唾液蛋白指纹图谱分子诊断模型研究[J].湖南中医药大学学报,2016,36(4):19-24,6.基金项目
国家自然科学基金项目资助（81273665）；湖南中医药大学中医诊断学国家重点学科开放基金项目（201401）。 （）
