重庆医学2011，Vol.40Issue(7)：641-643,后插3,4.DOI:10.3969/j.issn.1671-8348.2011.07.007
食管癌血清差异蛋白质组测定结果分析
Analysis of detection results of different serum proteomes in esophageal cancer
谭小林 1任兴军 2王开正 1杨成虎 2冯峰 2胡琼英 1丁银环 1戴天阳1
作者信息
- 1. 泸州医学院附属医院检验科,四川泸州,646000
- 2. 四川省射洪县人民医院,629200
- 折叠
摘要
Abstract
Objective To find differential serum protomes in esophageal cancer by enhanced laser desorption ionization time of flight mass spectromctry(SELDI-TOF-MS) to establish diagnostic models for accumulating the data information for wide use in clinical diagnostic. Methods To detect the serum of the patients with esophageal cancer and related groups by SELDI chip, filter out different proteomes with the Biomarker Wizard Software. Then to develop the artificial neural network diagnostic models using significant different proteomes and use SPSS software to analyze diagnostic efficiency of significant difference in proteomes and the combined artificial neural network after verified by the blind. Finally, to identify related proteins. Results The selected different protcomes(P＜0. 01) were as follows.In esophagcal cancer, the intensity increasing proteomcs were 5 017. 6±4. 89, 7 458. 5± 6. 49,7 908. 1±7. 80,8 111±8. 45,8 577±7. 80 kD,lowerly expressed proteomes were 7 748. 3±9. 10,5 890. 9±7. 32,4 213. 8± 5. 93 kD. Built the screening and diagnosis models of artificial neural network by different proteomes of esophageal cancer. Sensitivi ty and specificity were over 90％. After blinding test,they were effective. The area under the ROC curve consisting by the output value of artificial neural network were 81. 6％ and 82. 1％ ,searching the related databases, two proteins were found. Conclusion The value of dioglositic models are useful by associating serum protein fingerprint spectrums with artificial neural network technology in proteomics data mining and are accord with the clinical diagnosis.关键词
食管肿瘤/质谱分析法/蛋白质组学/神经网络计算机/诊断模型引用本文复制引用
谭小林,任兴军,王开正,杨成虎,冯峰,胡琼英,丁银环,戴天阳..食管癌血清差异蛋白质组测定结果分析[J].重庆医学,2011,40(7):641-643,后插3,4.
