Objective: To set up a method for the detection of the serum protein fingerprint pattern by using the protein chip technology for exploration of serum protein fingerprint pattern models based on the artificial neural network in diagnosis of liver cancer.
Methods: One hundred and six serum samples form subjects with liver cancer, hepatocirrhosis, and healthy individuals were detected with protein biochip surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) for protein fingerprint pattern, and analyzed with the artificial neural network. The 106 samples were randomly put into a training group (n = 70, 35 patients with liver cancer, 14 patients with hepatocirrhosis, and 21 healthy individuals) and a blind test group (n = 36, 17 patients with liver cancer, 8 patients with hepatocirrhosis, and 11 healthy individuals).
Results: The serum protein fingerprint pattern model obtained by the artificial neural network from training group was used to detect the 36 unknown serum samples. The sensitivity and specificity of this method in detection of liver cancer were 88.2% (15/17) and 94.6% (18/19) respectively.
Conclusion: In comparison with the traditional methods, this new method has higher sensitivity and specificity in diagnosis of liver cancer and should be further studied.