| abjective: With the development of the emerging technology of proteomics in rencent years,it provide a new research way for clinical diagnosis and treatment of diseases . The application of SELDI technology has been received success in a number of clinical areas, this study sought to explore the use of the technology and to find out the specific method for early colorectal cancer detection and diagnosis .Use proteinchip technology to select serum protein fingerprinting of colorectal cancer and discuss the value in colorectal cancer diagnosis preliminarily. Methods : Use surface-enhanced laser desorption ionization time of flight mass spectrometry(SELDI-TOF-MS) technology and Weak cation exchange surface (CM10) protein chip to monitor 70 cases of colorectal cancer patients and 48 control patients with benign lesions (including colorectal polyps and inflammatory bowel disease) and 25 cases of normal's serum protein spectrum. Colorectal cancer patients and the benign lesions and normal control will be divided into training set and the control (40 patients with 40 controls) and validation set (30 patients and 33control), The former is for the differences in colorectal cancer screening protein markers and establish the artificial neural network (ANN) model diagnosis, the latter is for the diagnosis of validity evidence of the blind erification. At first,we detected the mixed serum samples and the researched samples using SELDI mass spectrometry and CM10 protein chips.Ciphergen Proteinchip software analyzed the proteomic spectra from the mixed serum samples and computed the coefficient variation(CV).The coefficient variation was within 15% and so was satisfactory for the reproducibility of the protein profiling in the study. Biomarker Wizard Software 3.1 analyzed serum proteomic spectra.To compare the mass peaks of the colorectal cancer patients and controls,we used analysis of variance to exam the mass peaks which were collected initially,and p<0.01 was considered statistically significant. Using a multi-layer ANN with a back propagation algorithm,we identified a proteomic pattern that could discriminate cancer from control samples in the training set.The ANN used input and output data (samples for training set) to define (learn) the interrelationships among the data.Once the ANN has been trained, it could then predict outcomes from new sets of input data .The discovered patern was then used to determine the accuracy of the classification system in the test set. Results: 1)The proteomic spectra from the mixed serum samples were statistically analyzed,and the CV value was 11.50%,indicating good repeatability of the experiment,The matric peaks whose mass-to-charge ratio were less than 2000 were excluded to avoid possible interference.2)The 1063 mass peaks which were found by detecting the colorectal cancer patients and controls'serum samples were statistically analyzed.Among the 1063 mass peaks,148 mass peaks expressed obviously differentially. 3) In the 148 obviously differentially expressed protein peaks, screens 9 protein peak of strong ratio of the signal to noise.Using the training set samples, establish the artificial neural network diagnosis model. which 2974Da , 3282Da, 5948Da of the three peaks of protein charge-mass ratio (m/z) have obvious ratio of the signal to noise,they have the meaning of identity in colorectal cancer and normal comparison,and 3016Da, 6647Da of the two peaks of protein charge-mass ratio (m/z) have obvious ratio of the signal to noise, they have the meaning of identity in colorectal cancer and colorectal benign group. This model examination colon cancer's sensitivity is 94.2%, the peculiarity is 91.3%, the masculine predicted value is 91.2%, the negative predicted value is 94.5%. Conclution:1)Using the protein chip technology to discover 5 serum albumin designated object,they have the distinction significance to the colon cancer. 2) the preliminary establishment of colorectal cancer's serum albumin fingerprint mass spectrum, possibly has provided the new method for early diagnosis of the colorectal cancer.
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