Identification of Gastric Cancer Patients by Serum Protein Profiling

• Published in: Journal of proteome research Vol. 3; no. 6; pp. 1261 - 1266
• Main Authors: Ebert, Matthias P. A, Meuer, Jörn, Wiemer, Jan C, Schulz, Hans-Ulrich, Reymond, Marc A, Traugott, Ulrich, Malfertheiner, Peter, Röcken, Christoph
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 01.11.2004
• Subjects: Algorithms; Blood Proteins - analysis; Case-Control Studies; Humans; Mass Spectrometry; Neoplasm Proteins - blood; Protein Array Analysis; Sensitivity and Specificity; Stomach Neoplasms - blood; Stomach Neoplasms - diagnosis
• ISSN: 1535-3893; 1535-3907
• DOI: 10.1021/pr049865s

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. Keywords: Proteomics • diagnosis • stomach • SELDI