| Literature DB >> 15595736 |
Matthias P A Ebert1, Jörn Meuer, Jan C Wiemer, Hans-Ulrich Schulz, Marc A Reymond, Ulrich Traugott, Peter Malfertheiner, Christoph Röcken.
Abstract
Using surface-enhanced laser desorption ionization mass spectrometry (SELDI/TOF-MS) and ProteinChip technology, coupled with a pattern-matching algorithm and serum samples, we screened for protein patterns to differentiate gastric cancer patients from noncancer patients. A classifier ensemble, consisting of 50 decision trees, correctly classified all gastric cancers and all controls of a training set (100% sensitivity and 100% specificity). Eight of 9 stage I gastric cancers (88.9% sensitivity for stage I) were correctly classified. In addition, 28 sera from gastric cancer patients taken in different hospitals were correctly classified (100% sensitivity). Furthermore, all 11 control sera obtained from patients without gastric cancer (100% specificity) were classified correctly and 29 of 30 healthy blood-donors were classified as noncancerous. ProteinChip technology in conjunction with bioinformatics allows the highly sensitive and specific recognition of gastric cancer patients.Entities:
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Year: 2004 PMID： 15595736 DOI： 10.1021/pr049865s
Source DB: PubMed Journal: J Proteome Res ISSN： 1535-3893 Impact factor: 4.466