Establishment of diagnostic model of cerebrospinal protein fingerprint pattern for glioma and its clinical application.
- Author:
Jian LIU
1
;
Shu ZHENG
;
Jie-kai YU
;
Xue-bin YU
;
Wei-guo LIU
;
Jian-min ZHANG
;
Xun HU
Author Information
- Publication Type:Journal Article
- MeSH: Adult; Aged; Algorithms; Biomarkers, Tumor; Brain Neoplasms; cerebrospinal fluid; diagnosis; Cerebrospinal Fluid Proteins; genetics; Diagnosis, Differential; Female; Glioma; cerebrospinal fluid; diagnosis; Humans; Male; Meningioma; cerebrospinal fluid; diagnosis; Middle Aged; Neural Networks (Computer); Peptide Mapping; standards; Sensitivity and Specificity; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
- From: Journal of Zhejiang University. Medical sciences 2005;34(2):141-147
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVETo establish the diagnostic model of cerebrospinal protein profile for gliomas by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) and bioinformatics.
METHODSSeventy-five samples of cerebrospinal fluid from patients with gliomas, benign brain tumors and mild brain traumas were collected. A total of 50 samples from gliomas and non-brain-tumors were divided into training sets (33 cases including 17 gliomas and 16 non-brain-tumors) and testing sets (17 cases including 5 gliomas and 12 non-brain-tumors). The cerebrospinal proteins bound to H4 chip were detected by SELDI-TOF MS, the profiles of cerebrospinal protein were gained and then analyzed with artificial neural network algorithm (ANN); and the diagnostic model of cerebrospinal protein profiles for differentiating gliomas from non-brain-tumors was established. Forty-seven of cerebrospinal samples of gliomas and benign brain tumors were divided into training sets (31 cases including 13 gliomas and 18 benign brain tumors) and testing sets (16 cases including 9 gliomas and 7 benign brain tumors), the diagnostic model of cerebrospinal protein profiles for differentiating gliomas from benign brain tumors was established based on the same method. The support vector machine (SVM) algorithm was also used for evaluation, both results were very similar, but the result derived from ANN was more stable than that from SVM.
RESULTThe diagnostic model of cerebrospinal protein profiles for differentiating gliomas from non-brain-tumors was established and was challenged with the test set randomly, the sensitivity and specificity were 100% and 91.7%, respectively. The cerebrospinal protein profiling model for differentiating gliomas from benign brain tumors was also developed and was challenged with the test set randomly, the sensitivity and specificity were 88.9%, and 100%, respectively.
CONCLUSIONThe technology of SELDI-TOF MS which combined with analysis tools of bioinformatics is a novel effective method for screening and identifying tumor biomarkers of gliomas and it may provide a new approach for the clinical diagnosis of glioma.
