Diagnosis of pancreatic adenocarcinoma using protein chip technology.
BACKGROUND
To develop a serum-specific protein fingerprint which is capable of differentiating samples from patients with pancreatic cancer and those with other pancreatic conditions.


METHODS
We used SELDI-TOF-MS coupled with CM10 chips and bioinformatics tools to analyze a total of 118 serum samples in this study; 78 serum samples were analyzed to establish the diagnostic models and the other 40 samples were analyzed on the second day as an independent test set.


RESULTS
The analysis of this independent test set yielded a specificity of 91.6% and a sensitivity of 91.6% for pattern 1, which distinguished pancreatic adenocarcinoma (PC) from healthy individuals and a specificity of 80.0% and a sensitivity of 90.9% for pattern 2, which distinguished PC from chronic pancreatitis.


CONCLUSIONS
This study indicated that the SELDI-TOF-MS technique can facilitate the discovery of better serum tumor biomarkers and a combination of specific models is more accurate than a single model in diagnosis of PC.
Liu
Daren
D
Department of Surgery, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, PR China.
Cao
Liping
L
Yu
Jiekai
J
Que
Risheng
R
Jiang
Wenzhi
W
Zhou
Yiming
Y
Zhu
Linhua
L
D000230:Adenocarcinoma / Q000175:diagnosis*; D014408:Biomarkers, Tumor / Q000097:blood*; D018395:CA-19-9 Antigen / Q000097:blood; D005260:Female; D006801:Humans; D008297:Male; D009362:Neoplasm Metastasis / Q000175:diagnosis; D010190:Pancreatic Neoplasms / Q000175:diagnosis* / Q000473:pathology; D050500:Pancreatitis, Chronic / Q000175:diagnosis; D040081:Protein Array Analysis; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
D014408:Biomarkers, Tumor; D018395:CA-19-9 Antigen