Identification Of Serum Biomarkers For Nasopharyngeal Carcinama By Using Surface Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry And Establishment Of The Diagnostic Model

Purpose:To screen a suitable protein chip from four kinds of chips :Weak Cationic Exchanger CM10 chip,Immobilized Metal Affinity Capture IMAC-Cu chip,Reversed Phase or Hydrophobic H50 chip and Strong Anionic Exchanger Q10 chip .To provide a new method of finding biomakers of NPC,While establish the diagnostic model of NPC and provide a new and simple way for NPC early diagnosis.Methods:1) Serum samples from 4 cases of NPC and 4 cases of noncancer controls were collected respectively. Special protein or peptide spectras were detected by SELDI-TOF MS measurement after treating the samples onto four kinds of protein chips CM10,IMAC-Cu,H50 and Q10 for each case. According to the protein profiling results, screen the suitable protein chips.2) Serum samples were collected from 24 cases of NPC,24 cases of noncancer controls. The samples were profiled by SELDI-TOF MS measurement with CM10 chips. Biomarker Wizard software was used to analyze the proteomic profiles and screened NPC-related differences with proteins; Biomarker Pattern software was used to construct the diagnostic model. The validity of the diagnostic model was then challenged with a blind test set including another 32 serum samples.Results:1) Analysis of serum protein fingerprints showed CM10 chip combined more proteins which showed higher intensity in peaks than other three chips.So we chosed this chip to screen NPC-related differences with proteins and construct the diagnostic model.2) The Biomarker Wizard softerware get 94 protein pesks in CM10 chip,among them 26 show the difference obviously in the two groups. Compared with the normal people group, 5 proteins were up-regulated expressed and 21 down-regulated expressed. Useing M/Z 3159.83,5187.65,13738.6 protein pesks , a modle was built. In the training set of the modle that discriminated cancer from noncancer with sensitivity of 91.66% and specificity of 95.83%. In the learning set that discriminated cancer from noncancer with sensitivity of 87.5% and specificity of 95.83%. When the diagnostic model was challenged with a blind test set, it yielded a sensitivity of 95.00%, a specificity of 83.33%, and an accuracy rate of 90.63%. Conclusions:1) Each chip can combine with different species of serum proteins.CM10 chip can combine more differentiated proteins.2) There are differences among the group of NPC and noncancer controls in serum protein finger printing.3) The results suggest that SELDI-TOF-MS technology is a good platform for screening NPC-related differences with proteins.