title: Serum peptidome patterns of breast cancer based on magnetic bead separation and mass spectrometry analysis.
abstract: BACKGROUND
  Breast cancer is one of the most common cancers in the world, and the identification of biomarkers for the early detection of breast cancer is a relevant target. The present study aims to determine serum peptidome patterns for screening of breast cancer.
  
  
  METHODS
  The present work focused on the serum proteomic analysis of 36 healthy volunteers and 37 breast cancer patients using a ClinProt Kit combined with mass spectrometry (MS). This approach allows the determination of peptidome patterns that are able to differentiate the studied populations. An independent group of sera (36 healthy volunteers and 37 breast cancer patients) was used to verify the diagnostic capabilities of the peptidome patterns blindly. An immunoassay method was used to determine the serum mucin 1 (CA15-3) of validation group samples.
  
  
  RESULTS
  Support Vector Machine (SVM) Algorithm was used to construct the peptidome patterns for the identification of breast cancer from the healthy volunteers. Three of the identified peaks at m/z 698, 720 and 1866 were used to construct the peptidome patterns with 91.78% accuracy. Furthermore, the peptidome patterns could differentiate the validation group achieving a sensitivity of 91.89% (34/37) and a specitivity of 91.67% (33/36) (> CA 15-3, P < 0.05).
  
  
  CONCLUSIONS
  These results suggest that the ClinProt Kit combined with MS shows great potentiality for the diagnosis of breast cancer.
  The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1501556838687844.
authors.affiliation: Institute of Anal-colorectal Surgery, No, 150 Central Hospital of PLA, No, 2, Huaxiaxi Road, 471000, Luoyang, China.
authors.forename: Nai-Jun
authors.initials: NJ
authors.lastname: Fan
authors.forename: Chun-Fang
authors.initials: CF
authors.lastname: Gao
authors.forename: Guang
authors.initials: G
authors.lastname: Zhao
authors.forename: Xiu-Li
authors.initials: XL
authors.lastname: Wang
authors.forename: Qing-Yin
authors.initials: QY
authors.lastname: Liu
mesh_terms: D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000097:blood*; D001943:Breast Neoplasms / Q000097:blood*; D019295:Computational Biology / Q000379:methods; D005260:Female; D006801:Humans; D008280:Magnetics; D008875:Middle Aged; D020543:Proteome; D040901:Proteomics / Q000379:methods*; D012372:ROC Curve; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*; D060388:Support Vector Machine
chemical_list: D014408:Biomarkers, Tumor; D020543:Proteome