OBJECTIVE: To explore the application of serum protein pattern models in diagnosis of colorectal cancer (CRC) by proteinchip technology. METHODS: One hundred and forty-seven serum samples (55 CRC patients and 92 healthy individuals) randomly divided into training set (n = 87, 32 CRC patients and 55 healthy individuals) and test set (n = 60), were subjected for analysis by surface enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS). Four top-scored peaks in 5910, 8930, 4476 and 8817 were detected by proteinchip software version 3.0. and were trained by a multi-layer artificial neural network (ANN) with a back propagation algorithm. An artificial neural network classifier had developed for separating CRC from the healthy group. The classifier was then challenged with the test set (60 samples including 23 CRC patients and 37 healthy individuals) to determine the validity and accuracy of the classification system. RESULTS: The artificial neural network classifier separated the CRC from the healthy samples, with sensitivity of 82.6% and specificity of 91.9%. CONCLUSION: Combination of SELDI-TOF-MS with the artificial neural network yields significant higher sensitivity and specificity than CEA in the diagnosis of CRC, which should be further studied.
OBJECTIVE: To explore the application of serum protein pattern models in diagnosis of colorectal cancer (CRC) by proteinchip technology. METHODS: One hundred and forty-seven serum samples (55 CRC patients and 92 healthy individuals) randomly divided into training set (n = 87, 32 CRC patients and 55 healthy individuals) and test set (n = 60), were subjected for analysis by surface enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS). Four top-scored peaks in 5910, 8930, 4476 and 8817 were detected by proteinchip software version 3.0. and were trained by a multi-layer artificial neural network (ANN) with a back propagation algorithm. An artificial neural network classifier had developed for separating CRC from the healthy group. The classifier was then challenged with the test set (60 samples including 23 CRC patients and 37 healthy individuals) to determine the validity and accuracy of the classification system. RESULTS: The artificial neural network classifier separated the CRC from the healthy samples, with sensitivity of 82.6% and specificity of 91.9%. CONCLUSION: Combination of SELDI-TOF-MS with the artificial neural network yields significant higher sensitivity and specificity than CEA in the diagnosis of CRC, which should be further studied.