OBJECTIVE: The aims of this study were to detect serum proteomic patterns in gastric cancer serum samples using Surface-enhanced Laser Desorption/ionization-Time-of-flight-Mass Spectrometry ProteinChip array technology, to screen biomarker candidates, to build diagnostic models and to evaluate their clinical significance. METHODS: Serum samples from patients with gastric cancer and normal healthy control subjects (n = 125) were analysed using surface-enhanced laser desorption/ionization technology. The spectra were generated on weak cation exchange (WCX2) chips, and protein peak clustering and classification analyses were established using Ciphergen Biomarker Wizard and Biomarker Pattern software, respectively. The diagnostic models were developed and validated by discriminant analysis. In addition, the results of the surface-enhanced laser desorption/ionization model were compared with the biomarkers carcinoembryonic antigen and carbohydrate antigen 199 in a subset of samples using a microparticle enzyme immunoassay. RESULTS: Five protein peaks at 2046, 3179, 1817, 1725 and 1929 m/z were automatically chosen as components of the best biomarker pattern for diagnosis of gastric cancer. In addition, we identified a single protein peak at 4665 m/z, which could distinguish between stage I/II and stage III/IV gastric cancer with a specificity and sensitivity of 91.6% (11/12) and 95.4% (21/22), respectively. When this biomarker was validated in the second set of samples, the specificity and sensitivity were 91.7% (11/12) and 86.3% (19/22), respectively. CONCLUSIONS: The present results suggest that serum surface-enhanced laser desorption/ionization protein profiling can distinguish patients with gastric cancer, and in particular stage I/II patients, from normal subjects with a relatively high sensitivity and specificity. Surface-enhanced Laser Desorption/ionization-Time-of-flight-Mass Spectrometry is a potential new diagnostic tool for the screening of gastric cancer.
OBJECTIVE: The aims of this study were to detect serum proteomic patterns in gastric cancer serum samples using Surface-enhanced Laser Desorption/ionization-Time-of-flight-Mass Spectrometry ProteinChip array technology, to screen biomarker candidates, to build diagnostic models and to evaluate their clinical significance. METHODS: Serum samples from patients with gastric cancer and normal healthy control subjects (n = 125) were analysed using surface-enhanced laser desorption/ionization technology. The spectra were generated on weak cation exchange (WCX2) chips, and protein peak clustering and classification analyses were established using Ciphergen Biomarker Wizard and Biomarker Pattern software, respectively. The diagnostic models were developed and validated by discriminant analysis. In addition, the results of the surface-enhanced laser desorption/ionization model were compared with the biomarkers carcinoembryonic antigen and carbohydrate antigen 199 in a subset of samples using a microparticle enzyme immunoassay. RESULTS: Five protein peaks at 2046, 3179, 1817, 1725 and 1929 m/z were automatically chosen as components of the best biomarker pattern for diagnosis of gastric cancer. In addition, we identified a single protein peak at 4665 m/z, which could distinguish between stage I/II and stage III/IV gastric cancer with a specificity and sensitivity of 91.6% (11/12) and 95.4% (21/22), respectively. When this biomarker was validated in the second set of samples, the specificity and sensitivity were 91.7% (11/12) and 86.3% (19/22), respectively. CONCLUSIONS: The present results suggest that serum surface-enhanced laser desorption/ionization protein profiling can distinguish patients with gastric cancer, and in particular stage I/II patients, from normal subjects with a relatively high sensitivity and specificity. Surface-enhanced Laser Desorption/ionization-Time-of-flight-Mass Spectrometry is a potential new diagnostic tool for the screening of gastric cancer.
Authors: Lixia Diao; Charlotte H Clarke; Kevin R Coombes; Stanley R Hamilton; Jack Roth; Li Mao; Bogdan Czerniak; Keith A Baggerly; Jeffrey S Morris; Eric T Fung; Robert C Bast Journal: Cancer Inform Date: 2011-03-14
Authors: Yanlan Chai; Juan Wang; Ying Gao; Tao Wang; Fan Shi; Jin Su; Yunyi Yang; Xi Zhou; Liping Song; Zi Liu Journal: J Radiat Res Date: 2014-09-24 Impact factor: 2.724
Authors: Juan An; Chuanhao Tang; Na Wang; Yi Liu; Wanfeng Guo; Xiaoyan Li; Zihe Wang; Kun He; Xiaoqing Liu Journal: Zhongguo Fei Ai Za Zhi Date: 2013-05