Abstract
New proteomic technologies have brought the hope of discovering novel early cancer-specific biomarkers in complex biological samples. Novel mass spectrometry (MS) based technologies in particular, such as surface-enhanced laser desorption/ionisation time of flight (SELDI-TOF-MS), have shown promising results in recent years. To find new potential biomarkers and establish the patterns for detection of cancers, we proposed a novel method to analysis SELDI-TOF-MS using binary cross section imaging and energy curve technology. The proposed method with advantage of visualization is to mining local information adequately so as to discriminate cancer samples from non-cancer ones. Applying the procedure to MS data of proteomic analysis of serum from ovarian cancer patients and serum from cancer-free individuals in the Food and Drug Administration/National Cancer Institute Clinical Proteomics Database, we find that there are outputs of the cancerous when the threshold is above 90 and M/Z is in the range 9362.3296-9747.2723, while outputs of the non-cancerous will appear when the threshold is 60 80 and M/Z is 243.4940-247.8824.
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References
Kozak, K.R., Su, F.: Characterization of Serum Biomarkers for Detection of Early Stage Ovarian Cancer. Proteomics 5(17), 4589–4596 (2005)
Ricolleau, G., Charbonnel, C.: Surface-enhanced Laser Desorption/Ionization Time of Flight Mass Spectrometry Protein Profiling Identifies Ubiquitin and Ferritin Light Chain as Prognostic Biomarkers in Node-Negative Breast Cancer Tumors. Proteomics 6(6), 1963–1975 (2006)
Donald, D., Hancock, T.: Bagged Super Wavelets Reduction for boosted Prostate Cancer Classification of Seldi-Tof Mass Spectral Serum Profiles. Chemometrics and Intelligent Laboratory Systems 82(1-2), 2–7 (2006)
Yu, J.K., Chen, Y.D.: An Integrated Approach to the Detection of Colorectal Cancer Utilizing Proteomics and Bioinformatics. World J Gastroenterol 10(21), 3127–3131 (2004)
Yang, S.Y., Xiao, X.Y.: Application of Serum SELDI Proteomic Patterns in Diagnosis of Lung Cancer. BMC Cancer 5(83) (2005)
Petricoin, E.F., Liotta, L.A.: SELDI-TOF-based serum Proteomic Pattern Diagnostics for Early Detection of Cancer. Current Opinion in Biotechnology 15, 24–30 (2004)
Petricoin, J.E., Ardekani, A.M., Hitt, B.A., Levine, P.J., Fusaro, V.A., Steinberg, S.M.: Use of Proteomic Patterns in Serum to Identify Ovarian Cancer. The Lancet 359, 572–577 (2002)
Petricoin, E.F., Ornstein, D.K., Paweletz, C.P., Ardekani, A., Hackett, P.S, Hitt, B.A.: Serum Proteomic Patterns for Detection of Prostate Cancer. J Ntl Cancer Inst 94(20), 1576–1578 (2002)
Poon, T.C.W., Johnson, P.J.: Proteome Analysis and its Impact on the Discovery of Serological Tumor Markers. Clin Chim Acta 313, 231–239 (2001)
Wulfkuhle, J.D, McLean, K.C, Paweletz, C.P., Sgroi, D.C., Trock, B.J., Steeg, P.S.: New Approaches to Proteomic Analysis of Breast Cancer. Proteomics 1(10), 1205–1215 (2001)
Wulfkuhle, J.D., Liotta, L.A., Petricoin, E.F.: Proteomic Applications for the Early Detection of Cancer. Nature Rev Cancer, 267-275 (2003)
Bakhtiar, R., Nelson, R.W.: Mass Spectrometry of the Proteome. Mol. Pharmacol. 60(3), 405–415 (2001)
Adam, B.L., Qu, Y., Davies, J.W., Ward, M.D., Clements, M.A., Cazares, L.H.: Serum Protein Fingerprinting Coupled with A Pattern-Matching Algorithm Distinguishes Prostate Cancer from Benign Prostate Hyperplasia and Healthy Men. Cancer Res. 62(3), 3609–3614 (2002)
Qu, Y., Adam, B.L., Yasui, Y., Ward, M.D., Cazares, L.H., Schellhammer, P.F.: Boosted Decision Tree Analysis of Surface-Enhanced Laser Desorption/Ionization Mass Spectral Serum Profiles Discriminated Prostate Cancer from Nonprostate Patients. Clin Chem 48, 1835–1843 (2002)
Ball, G., Main, S., Holding, F., Allibone, R., Lowe, J., Ali, S.: An Integrated Approach Utilizing Aritifiaial Neural Networks and Seldi Mass Spectrometry for the Classification of Human Tumours and Rapid Identification of Potential Biomarkers. Bioinformatics 18(3), 395–404 (2002)
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Hong, W., Meng, H., Wang, L., Song, J. (2007). Research on Patterns of Cancer Markers Based on Cross Section Imaging of Serum Proteomic Data. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_125
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DOI: https://doi.org/10.1007/978-3-540-74171-8_125
Publisher Name: Springer, Berlin, Heidelberg
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