title: Artificial neural networks analysis of surface-enhanced laser desorption/ionization mass spectra of serum protein pattern distinguishes colorectal cancer from healthy population.
abstract: OBJECTIVE
  The low specificity and sensitivity of the carcinoembryonic antigen test makes it not an ideal biomarker for the detection of colorectal cancer. We developed and evaluated a proteomic approach for the simultaneous detection and analysis of multiple proteins for distinguishing individuals with colorectal cancer from healthy individuals.
  
  
  METHODS
  We subjected serum samples (including 55 colorectal cancer patients and 92 age- and sex-matched healthy individuals) from 147 individuals, for analysis by surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. Peaks were detected with Ciphergen SELDI software version 3.0. Using a multilayer artificial neural network with a back propagation algorithm, we developed a classifier for separating the colorectal cancer groups from the healthy groups.
  
  
  RESULTS
  The artificial neural network classifier separated the colorectal cancer from the healthy samples, with a sensitivity of 91% and specificity of 93%. Four top-scored peaks, at m/z of 5,911, 8,930, 8,817, and 4,476, were finally selected as the potential "fingerprints" for detection of colorectal cancer.
  
  
  CONCLUSIONS
  The combination of SELDI-TOF mass spectrometry with the artificial neural networks in the analysis of serum protein yields significantly higher sensitivity and specificity values for the detection and diagnosis of colorectal cancer.
authors.affiliation: Department of Oncology, Second Affiliated Hospital, Zhejiang University, HangZhou, Zhejiang, People's Republic of China.
authors.forename: Yi-ding
authors.initials: YD
authors.lastname: Chen
authors.forename: Shu
authors.initials: S
authors.lastname: Zheng
authors.forename: Jie-kai
authors.initials: JK
authors.lastname: Yu
authors.forename: Xun
authors.initials: X
authors.lastname: Hu
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000378:metabolism; D001798:Blood Proteins / Q000032:analysis*; D018395:CA-19-9 Antigen / Q000378:metabolism; D016022:Case-Control Studies; D015179:Colorectal Neoplasms / Q000097:blood*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D040081:Protein Array Analysis; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods
chemical_list: D014408:Biomarkers, Tumor; D001798:Blood Proteins; D018395:CA-19-9 Antigen