OBJECTIVE To establish a serum protein pattern model for screening pancreatic cancer. METHODS Twenty-nine serum samples from patients with pancreatic cancer were collected before surgery,and an additional 57 serum samples from age and sex-matched individuals without cancer were used as controls.WCX magnetic beans and a PBS Ⅱ-C protein chip reader (Ciphergen Biosystems Inc) were employed to detect the protein fingerprint expression of all serum samples. The resulting profiles comparing serum from cancer and normal patients were analyzed with the Biomarker Wizard system,to establish a model using the Biomarker Pattern system software. A double-blind test was used to determine the sensitivity and specificity of the model. RESULTS A group of 4 biomarkers(relative molecular weights were 5,705 Da,4,935 Da,5,318 Da,3,243 Da) were selected to set up a decision tree to produce the classification model to effectively screen pancreatic cancer patients.The results yielded a sensitivity of 100%(20/20),specificity of 97.4% (37/38).The ROC curve was 99.7%.A double-blind test used to challenge the model resulted in a sensitivity of 88.9% and a specificity of 89.5%. CONCLUSION New serum biomarkers of pancreatic cancer have been identified.The pa ern of combined markers provides a powerful and reliable diagnostic method for pancreatic cancer with high sensitivity and specificity. OBJECTIVE To establish a serum protein pattern model for screening pancreatic cancer. METHODS Twenty-nine serum samples from patients with pancreatic cancer were collected before surgery, and an additional 57 serum samples from age and sex-matched individuals without cancer were used as controls. WCX magnetic fields and a PBS Ⅱ-C protein chip reader (Ciphergen Biosystems Inc) were employed to detect the protein fingerprint expression of all serum samples. The resulting profiles comparing serum from cancer and normal patients were analyzed with the Biomarker Wizard system, to establish a model using the Biomarker Pattern system software. A double-blind test was used to determine the sensitivity and specificity of the model. RESULTS A group of 4 biomarkers (relative molecular weights were 5,705 Da, 4,935 Da, 5,318 Da, 3,243 Da) were selected to set up a decision tree to produce the classification model to effectively screen pancreatic cancer patients. The results yielded a sensitivity of 100% (2 Specificity was 97.4% (37/38). The ROC curve was 99.7%. A double-blind test used to challenge the model resulted in a sensitivity of 88.9% and a specificity of 89.5%. CONCLUSION New serum biomarkers of pancreatic cancer have been identified.The pa ern of combined markers provides a powerful and reliable diagnostic method for pancreatic cancer with high sensitivity and specificity.