In this study, we performed a proteomic analysis of sera from stage I gastric cancer patients using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) and established a diagnostic model for the early diagnosis of stage I gastric cancer. Serum samples from 169 gastric cancer patients and 83 age- and gender-matched healthy individuals were analyzed by SELDI-TOF-MS ProteinChip array technology. The SELDI-TOF-MS spectral data were analyzed using the Biomarker Wizard™ and Biomarker Patterns™ software to find differential proteins and develop a classification tree for gastric cancer. A total of 34 mass peaks were identified. Six peaks at a mass-to-charge ratio (m/z) of 2873, 3163, 4526, 5762, 6121 and 7778 were used to construct the diagnostic model. The model effectively distinguished gastric cancer samples from control samples, achieving a sensitivity and specificity of 93.49 and 91.57%, respectively. In addition, we identified 3 of the 6 protein peaks at 2873, 6121 and 7778 m/z, which distinguished between stage I and stage II/III/IV gastric cancer. The model had an accuracy of 88.89% for the identification of stage I gastric cancer. In conclusion, the diagnostic model for the detection of serum proteins by SELDI-TOF-MS ProteinChip array technology correctly distinguishes gastric cancer from healthy samples, and has the ability to screen and distinguish between early gastric cancer from advanced gastric cancer.
Keywords: gastric cancer; biomarkers; surface-enhanced laser desorption/ionization time-of-flight mass spectrometry; serum proteomic patterns.