Classification Analysis of Surface-enhanced Laser Desorption/Ionization Mass Spectral Serum Profiles for Prostate Cancer

Classification analysis was performed on 322 SELDI-TOF-MS protein expression profiles for prostate cancer. Feature ranking was based on the F-test, information gain (entropy), and Gini diversity applied in a pairwise, one-against-all, and all-at-once modular form. Classifiers included 4NN, NBC, LDA, LVQ1, SVM, and ANN. 4-class bootstrap (0.632) accuracies were in the range 50-80%, with NBC resulting in the lowest average accuracy (50-66%) and SVM resulting in the greatest average accuracy (71-79%). A 12-peak model with 88% accuracy collapsed into 6 peaks with m/z values of 3460, 4172, 4581, 6890, 14281 and 14696. The peaks identified may be confirmed in the future to be markers of early detection and/or therapy.

[1]  Mustafa Ozen,et al.  Artificial Neural Network Analysis of DNA Microarray-based Prostate Cancer Recurrence , 2005, 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

[2]  M. Verma,et al.  Proteomics for cancer biomarker discovery. , 2002, Clinical chemistry.

[3]  B. Efron.  Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation , 1983 .

[4]  Gabriela Alexe,et al.  A robust meta‐classification strategy for cancer detection from MS data , 2006, Proteomics.

[5]  Jiebo Luo,et al.  Data Mining. Multimedia, Soft Computing, and Bioinformatics , 2005, IEEE Transactions on Neural Networks.

[6]  Jiekai Yu,et al.  SELDI-TOF-MS: the proteomics and bioinformatics approaches in the diagnosis of breast cancer. , 2005, Breast.

[7]  Sushmita Mitra,et al.  Data Mining , 2003 .

[8]  E. Petricoin,et al.  SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer. , 2004, Current opinion in biotechnology.

[9]  Jong-Sup Park,et al.  Role of proteomics in translational research in cervical cancer , 2006, Expert review of proteomics.

[10]  E. Petricoin,et al.  Serum proteomic patterns for detection of prostate cancer. , 2002, Journal of the National Cancer Institute.

[11]  Bruce Randall Donald,et al.  Probabilistic Disease Classification of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum , 2003, J. Comput. Biol..

[12]  E. Petricoin,et al.  Use of proteomic patterns in serum to identify ovarian cancer , 2002, The Lancet.

[13]  Timothy M Pawlik,et al.  The evolving role of proteomics in the early detection of breast cancer. , 2005, International journal of fertility and women's medicine.

[14]  Li Ning,et al.  [Research development of proteomics in pancreatic cancer]. , 2005, Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae.

[15]  Weida Tong,et al.  Using Decision Forest to Classify Prostate Cancer Samples on the Basis of SELDI-TOF MS Data: Assessing Chance Correlation and Prediction Confidence , 2004, Environmental health perspectives.

[16]  P. Schellhammer,et al.  Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. , 2002, Clinical chemistry.

[17]  Paolo Bechi,et al.  Differential expression proteomics of human colon cancer. , 2006, American journal of physiology. Gastrointestinal and liver physiology.

[18]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[19]  Alex Pothen,et al.  Computational protein biomarker prediction: a case study for prostate cancer , 2004, BMC Bioinformatics.

[20]  Sudhir Srivastava,et al.  Proteomics in the forefront of cancer biomarker discovery. , 2005, Journal of proteome research.

[21]  D. Chan,et al.  Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. , 2002, Clinical chemistry.

[22]  M. Ebert,et al.  Advances in clinical cancer proteomics: SELDI-ToF-mass spectrometry and biomarker discovery. , 2005, Briefings in functional genomics & proteomics.

[23]  L. Liotta,et al.  Proteomic Patterns of Nipple Aspirate Fluids Obtained by SELDI-TOF: Potential for New Biomarkers to Aid in the Diagnosis of Breast Cancer , 2002, Disease markers.