Strenziok, R. and Hinz, S. and Wolf, C. and Conrad, T. O. F. and Krause, H. and Lingnau, A. and Lein, M. and Miller, K. and Schrader, M. (2008) Serum proteomic profiling by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry in testicular germ cell cancer patients. European Urology Supplements, 7 (3). p. 83.
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Official URL: http://dx.doi.org/10.1016/S1569-9056(08)60055-X
Abstract
Objectives: Surface-enhanced laser desorption ionization mass spectrometry (sELDI-TOF MS) incorporates time-of-flight protein mass spectrometry and chip-based chromatographic protein selection and allows rapid protein profiling of complex biological samples. In this study, serum protein profiles were examined in testicular germ cell cancer patients and healthy controls with the aim of predicting cancer patients by a molecular test combining on-chip chromatography and mass spectrometry. Methods: Serum samples were taken from 49 patients with histologically proven seminomas. Forty-nine healthy subjects served as controls. Mass-spectrometry-based SELDI-TOF ProteinChip technology was used for serum proteomic analysis. The samples were fractionated and bound to the IMAC-Cu+ and CM10 ProteinChip. Proteomic spectra were generated by the ProteinChip system and analyzed by the proteomic platform ?proteomic.net?. Serum samples were also examined for beta-human chorionic gonadotropin levels. An artificial intelligence learning algorithm was used to differentiate between cancer and control subjects. Results The classification algorithm correctly identified the pattern in 90.4% of the samples (95% confidence interval of 82.6%-95.5%). Decision trees predicted seminomas with 91.5% sensitivity and 89.4% specificity. Seminoma patients with normal beta-human chorionic gonadotropin serum levels (n=38 of 49) were correctly predicted with 80% sensitivity and 70% specificity. Conclusion Our study is the first to demonstrate that SELDI-TOF MS protein profiles of cancer and control subjects differ to a highly significant degree in testicular germ cell cancer patients. Validation of these findings may enable proteomic profiling to become a valuable tool, especially for aftercare.
Item Type: | Article |
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Subjects: | Mathematical and Computer Sciences > Statistics > Mathematical Statistics Mathematical and Computer Sciences > Artificial Intelligence > Machine Learning Medicine and Dentistry > Clinical Medicine |
Divisions: | Department of Mathematics and Computer Science > Institute of Mathematics > Comp. Proteomics Group Department of Mathematics and Computer Science > Institute of Mathematics Department of Mathematics and Computer Science > Institute of Mathematics > BioComputing Group |
ID Code: | 1018 |
Deposited By: | BioComp Admin |
Deposited On: | 18 Jan 2011 16:00 |
Last Modified: | 18 Jan 2011 16:20 |
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