title: Analysis of the raw serum peptidomic pattern in glioma patients.
abstract: BACKGROUND
  Glioma is a common and lethal type of brain tumor. Serum peptides reflected the pathological changes of the body. Here we studied the serum peptide profiles to distinguish glioma disease and measure glioma staging.
  
  
  METHODS
  Serum peptides were captured by WCX magnetic beads and were analyzed by MALDI-TOF mass spectrometer. Sera from 53 glioma patients and 69 age-matched healthy controls were analyzed. Clinpro Tools software was used to obtain a common peak m/z list from all measured samples. An optimal subset of peptides was selected to establish a predictive classification model with the newly developed competitive adaptive reweighted sampling (CARS) variable selection method. Serum peptide profiles were classified through a partial least-squares-linear discriminate analysis (PLS-LDA). We also searched for progressively different peptide peaks that correlated with an increasing malignancy of glioma.
  
  
  RESULTS
  The following pattern recognition equation was established with selected peptide signals: Y=-0.1113-0.113X1-0.2916X2+0.1128X3-0.2057X4-0.2047X5-0.3048X6+0.2835X7+0.3121X8-0.1458X9+0.0354X10-0.2022X11. Using this pattern, classification sensitivity and specificity achieved were 0.9057 and 0.9855, respectively. Additionally, we detected 3 peptide signals that correlated with glioma grade. Among these, the intensity of peak 2082.32 Da correlated positively with the glioma progressing, and peaks with sizes of 3316.08 Da and 6631.45 Da show a decreasing intensity with increasing glioma grade.
  
  
  CONCLUSIONS
  11 peptide recognition patterns and specific peak intensities might be useful for the early detection and tumor staging of glioma, but they need to be further validated and evaluated independently in clinical settings.
authors: Cancer Research Institute, Key Laboratory of Carcinogenesis of Ministry of Health, Central South University, 110 Xiangya Road, Changsha, Hunan, 410078, PR China.
authors: Zheng
authors: Z
authors: Li
authors: Hongmei
authors: H
authors: Lu
authors: Jing
authors: J
authors: Yang
authors: Xi
authors: X
authors: Zeng
authors: Lian
authors: L
authors: Zhao
authors: Hongdong
authors: H
authors: Li
authors: Qianjing
authors: Q
authors: Liao
authors: Shuping
authors: S
authors: Peng
authors: Ming
authors: M
authors: Zhou
authors: Minghua
authors: M
authors: Wu
authors: Juanjuan
authors: J
authors: Xiang
authors: Yanjin
authors: Y
authors: Wang
authors: Guiyuan
authors: G
authors: Li
mesh_terms: D000328:Adult; D001798:Blood Proteins / Q000032:analysis*; D001932:Brain Neoplasms / Q000097:blood* / Q000175:diagnosis; D016022:Case-Control Studies; D016002:Discriminant Analysis; D042241:Early Diagnosis; D005260:Female; D005910:Glioma / Q000097:blood* / Q000175:diagnosis; D006801:Humans; D059346:Magnets; D008297:Male; D008863:Microspheres; D008875:Middle Aged; D009363:Neoplasm Proteins / Q000032:analysis*; D009367:Neoplasm Staging; D010446:Peptide Fragments / Q000302:isolation & purification*; D040901:Proteomics; D012372:ROC Curve; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
chemical_list: D001798:Blood Proteins; D009363:Neoplasm Proteins; D010446:Peptide Fragments
keywords: CARS; Glioma; HCCA; MALDI-TOF MS; MB-WCX; MS; PLS–LDA; Pattern recognition; Peptide; SELDI; Variable selection; competitive adaptive reweighted sampling; mass spectrometry; matrix assisted laser desorption/ionization time-of-flight mass spectrometry; partial least-squares–linear discriminate analysis; surface-enhanced laser desorption/ionization; weak cation exchange chromatography magnetic bead; α-cyano-4-hydroxycinnamic acid