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Journal of Experimental and Clinical Cancer Research

, 28:126

First Online: 09 September 2009Received: 06 May 2009Accepted: 09 September 2009


BackgroundAlthough gastric caner GC remains the second cause of cancer-related death, useful biomarkers for prognosis are still unavailable. We present here the attempt of mining novel biomarkers for GC prognosis by using serum proteomics.

MethodsSera from 43 GC patients and 41 controls with gastritis as Group 1 and 11 GC patients as Group 2 was successively detected by Surface Enhanced Laser Desorption-ionization Time of Flight Mass Spectrometry SELDI-TOF-MS with Q10 chip. Peaks were acquired by Ciphergen ProteinChip Software 3.2.0 and analyzed by Zhejiang University-ProteinChip Data Analysis System ZJU-PDAS. CEA level were evaluated by chemiluminescence immunoassay.

ResultsAfter median follow-up periods of 33 months, Group 1 with 4 GC patients lost was divided into 20 good-prognosis GC patients overall survival more than 24 months and 19 poor-prognosis GC patients no more than 24 months. The established prognosis pattern consisted of 5 novel prognosis biomarkers with 84.2% sensitivity and 85.0% specificity, which were significantly higher than those of carcinoembryonic antigen CEA and TNM stage. We also tested prognosis pattern blindly in Group 2 with 66.7% sensitivity and 80.0% specificity. Moreover, we found that 4474-Da peak elevated significantly in GC and was associated with advanced stage III+IV and short survival p < 0.03.

ConclusionWe have identified a number of novel biomarkers for prognosis prediction of GC by using SELDI-TOF-MS combined with sophisticated bioinformatics. Particularly, elevated expression of 4474-Da peak showed very promising to be developed into a novel biomarker associated with biologically aggressive features of GC.

Electronic supplementary materialThe online version of this article doi:10.1186-1756-9966-28-126 contains supplementary material, which is available to authorized users.

Fu-Ming Qiu, Jie-Kai Yu contributed equally to this work.

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Autor: Fu-Ming Qiu - Jie-Kai Yu - Yi-Ding Chen - Qi-Feng Jin - Mei-Hua Sui - Jian Huang


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