Prediction of pancreatic cancer by serum biomarkers using surface-enhanced laser desorption/ionization-based decision tree classification.
OBJECTIVE
In order to improve the prognosis of pancreatic cancer patients, it is crucial to explore novel tools for its early diagnosis. Here, we attempted to screen serum biomarkers to distinguish pancreatic cancer from non-cancer individuals.


METHODS
47 serum samples from pancreatic cancer patients, 39 of whom had small surgically resectable cancers, were collected before surgery, and an additional 53 serum samples from age- and sex-matched individuals without cancer were used as controls. The surface-enhanced laser desorption/ionization (SELDI) ProteinChip was applied to analyze serum protein profiling. 54 samples (27 with pancreatic cancer and 27 controls) were analyzed in the training set by a decision tree algorithm to be able to separate pancreatic cancer from controls. A double-blind test was used to determine the sensitivity and specificity of the classification model.


RESULTS
A panel of six biomarkers was selected to set up a decision tree as the classification model. The model separated effectively pancreatic cancer from control samples, achieving a sensitivity of 88.9% and a specificity of 74.1%. The double-blind test challenged the model with a sensitivity of 80% and a specificity of 84.6%.


CONCLUSIONS
The SELDI ProteinChip combined with an artificial intelligence classification algorithm shows great potential for the diagnosis of pancreatic cancer.
Yu
Yun
Y
Department of Pathophysiology, Shanghai Terry Fox Cancer Center and Institute of Hematology, Rui-Jin Hospital, Shanghai Second Medical University, Shanghai 200025, China.
Chen
Sheng
S
Wang
Li-Shun
LS
Chen
Wen-Li
WL
Guo
Wei-Jian
WJ
Yan
Hua
H
Zhang
Wei-Hua
WH
Peng
Cheng-Hong
CH
Zhang
Sheng-Dao
SD
Li
Hong-Wei
HW
Chen
Guo-Qiang
GQ
D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor / Q000097:blood*; D003663:Decision Trees; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010190:Pancreatic Neoplasms / Q000097:blood*; D011237:Predictive Value of Tests; D040081:Protein Array Analysis; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
D014408:Biomarkers, Tumor