Establishment of serum protein pattern model for diagnosing nasopharyngeal carcinoma using surface enhanced laser desorption/ionization time-of-flight mass spectrometry
AIM: To find new biomarkers and to establish classification tree model for the detection and diagnosis of naso-pharyngeal carcinoma by surface enhanced laser desorption/ionization time-of-flight-mass spectrometry (SELDI-TOF-MS) and bioinformatics tools. METHODS: Serum samples from 30 naso-pharyngeal carcinoma patients and 24 non-cancer controls were analyzed using CM10 protein chip system by SELDI-TOF-MS technology. Protein peak identification and clustering were performed using the Biomarker Wizard software. The classification tree model was then constructed using Biomarker Patterns System. Double blind confirmation was applied to the classification tree model. The results from the models were compared with those from the EBVCA-IgA serology test in order to verify its application value. RESULTS: Five protein markers were identified with the relative molecular weights of 8559, 15 115, 15 836, 15 937 and16 089. The differences of these protein markers between nasepharyngeal carcinoma patients and non-cancer controls were statistically significant (P<0.05). The detective model could differentiate nasopharyngeal carcinoma from non-cancer controls with the accuracy rate of 98.1% (53/54), sensitivity of 96.7% (29/30) and specificity of 100% (24/24). The accuracy rate, sensitivity and specificity of double blind confirmation procedure were 86.4% (19/22), 80.0% (8/10) and 91.7% (11/12), respectively. The sensitivity was better compared with that from the EBVCA-IgA serology test. CONCLUSION: SELDI-TOF-MS technology can be used to find protein markers of nasopharyngeal carcinoma and construct detective models with high sensitivity and specificity.
NETL
NSTL
万方数据
维普
