title: Mass spectrometric analysis of cerebrospinal fluid protein for glioma and its clinical application.
abstract: OBJECTIVE
  To establish and evaluate the fingerprint diagnostic models of cerebrospinal protein profile in glioma with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) and bioinformatics analysis, in order to seek new tumor markers.
  
  
  METHODS
  SELDI-TOF-MS was used to detect the cerebrospinal protein bond to ProteinChip H4. The cerebrospinal protein profiles were obtained and analyzed using the artificial neural network (ANN) method. Fingerprint diagnostic models of cerebrospinal protein profiles for distinguishing glioma from non-brain-tumor, and distinguishing glioma from benign brain tumor, were established. The support vector machine (SVM) algorithm was used for verification of established diagnostic models. The tumor markers were screened.
  
  
  RESULTS
  In a fingerprint diagnostic model of cerebrospinal protein profiles for distinguishing glioma from non-brain tumor, the sensitivity and specificity of glioma diagnosis were 100% and 91.7%, respectively. Seven candidate tumor markers were obtained. In a fingerprint diagnostic model for distinguishing glioma from benign brain tumor, the sensitivity and specificity of glioma diagnosis were 88.9% and 100%, respectively, and 8 candidate tumor markers were gained.
  
  
  CONCLUSIONS
  The combination of SELDI-TOF-MS and bioinformatics tools is a very effective method for screening and identifying new markers of glioma. The established diagnostic models have provided a new way for clinical diagnosis of glioma, especially for qualitative diagnosis.
authors: Department of Surgery, Second Affiliated Hospital, College of Medicine, Zhejiang Chinese Medical University, Hangzhou, China.
authors: Jian
authors: J
authors: Liu
authors: Cancer Institute, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
authors: Jiekai
authors: J
authors: Yu
authors: Cancer Institute, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
authors: Hong
authors: H
authors: Shen
authors: Cancer Institute, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
authors: Jianmin
authors: J
authors: Zhang
authors: Cancer Institute, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
authors: Weiguo
authors: W
authors: Liu
authors: Cancer Institute, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
authors: Zhe
authors: Z
authors: Chen
authors: Cancer Institute, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
authors: Shuda
authors: S
authors: He
authors: Cancer Institute, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
authors: Shu
authors: S
authors: Zheng
keywords: SELDI-TOF-MS; artificial neural network; cerebrospinal fluid; diagnostic model; glioma; support vector machine; tumor markers