Xiao-Li Jin1, Bin Xu1, Yu-Lian Wu1. 1. Xiao-Li Jin, Bin Xu, Department of Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, Zhejiang Province, China.
Abstract
AIM: To develop a method to differentiate pancreatic cancer patients from healthy or benign individuals when carbohydrate antigen (CA) 19-9 is normal. METHODS: Forty-one serum samples from patients with pancreatic lesions and blood samples from 20 healthy individuals were collected at the first stage of the experiment according to the enrolment criteria. General characteristics and some clinical features were carefully compared to ensure that the results were reasonable. All the blood samples were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) combined with CM10 chips and a related bioinformatics analysis program to generate diagnostic models with different proteins. Forty-seven consecutive samples were tested at the next stage to verify the veracity and efficiency of the models. RESULTS: The sex, age, and serum CA19-9 levels among the three groups (malignant, benign, and healthy) were statistically matched (P values were 0.957, 0.145, and 0.382, respectively). Two patterns were generated. Pattern 1 with four proteins theoretically had a specificity and sensitivity of 100% in distinguishing pancreatic cancer from healthy individuals, while it was 86.7% and 86.4%, respectively, in the subsequent practical verification. The positive predictive value (PPV) of the model was 86.4%. One of the four proteins was expressed highly in pancreatic cancer while the other three were expressed weakly. Pattern 2 consisted of six proteins that showed a specificity of 70.0% and sensitivity of 77.3% for differentiating malignancy from benign tumors. Its PPV reached 85.0%. Only one of these six proteins showed high expression in the malignant group. CONCLUSION: SELDI-TOF-MS may facilitate diagnosis or differential diagnosis of pancreatic cancer when CA19-9 is normal. Pattern 1 may serve as a useful screening tool.
AIM: To develop a method to differentiate pancreatic cancerpatients from healthy or benign individuals when carbohydrate antigen (CA) 19-9 is normal. METHODS: Forty-one serum samples from patients with pancreatic lesions and blood samples from 20 healthy individuals were collected at the first stage of the experiment according to the enrolment criteria. General characteristics and some clinical features were carefully compared to ensure that the results were reasonable. All the blood samples were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) combined with CM10 chips and a related bioinformatics analysis program to generate diagnostic models with different proteins. Forty-seven consecutive samples were tested at the next stage to verify the veracity and efficiency of the models. RESULTS: The sex, age, and serum CA19-9 levels among the three groups (malignant, benign, and healthy) were statistically matched (P values were 0.957, 0.145, and 0.382, respectively). Two patterns were generated. Pattern 1 with four proteins theoretically had a specificity and sensitivity of 100% in distinguishing pancreatic cancer from healthy individuals, while it was 86.7% and 86.4%, respectively, in the subsequent practical verification. The positive predictive value (PPV) of the model was 86.4%. One of the four proteins was expressed highly in pancreatic cancer while the other three were expressed weakly. Pattern 2 consisted of six proteins that showed a specificity of 70.0% and sensitivity of 77.3% for differentiating malignancy from benign tumors. Its PPV reached 85.0%. Only one of these six proteins showed high expression in the malignant group. CONCLUSION: SELDI-TOF-MS may facilitate diagnosis or differential diagnosis of pancreatic cancer when CA19-9 is normal. Pattern 1 may serve as a useful screening tool.
Authors: H J Kim; M H Kim; S J Myung; B C Lim; E T Park; K S Yoo; D W Seo; S K Lee; Y I Min Journal: Am J Gastroenterol Date: 1999-07 Impact factor: 10.864
Authors: Alex J Rai; Zhen Zhang; Jason Rosenzweig; Ie-Ming Shih; Thang Pham; Eric T Fung; Lori J Sokoll; Daniel W Chan Journal: Arch Pathol Lab Med Date: 2002-12 Impact factor: 5.534
Authors: M J Duffy; C Sturgeon; R Lamerz; C Haglund; V L Holubec; R Klapdor; A Nicolini; O Topolcan; V Heinemann Journal: Ann Oncol Date: 2009-08-18 Impact factor: 32.976