Prediction of Pancreatic Cancer by Serum Biomarkers Using Surface-Enhanced Laser Desorption/Ionization-Based Decision Tree Classification

Vol. 68, No. 1, 2005

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Laboratory/Clinical Translational Research

Prediction of Pancreatic Cancer by Serum Biomarkers Using Surface-Enhanced Laser Desorption/Ionization-Based Decision Tree Classification
Yun Yu^{a, 1}, Sheng Chen^{b, 1}, Li-Shun Wang^{a, 1}, Wen-Li Chen^a, Wei-Jian Guo^c, Hua Yan^a, Wei-Hua Zhang^e, Cheng-Hong Peng^b, Sheng-Dao Zhang^b, Hong-Wei Li^b, Guo-Qiang Chen^{a, d}

^aDepartment of Pathophysiology, Shanghai Terry Fox Cancer Center and Institute of Hematology, Rui-Jin Hospital, Shanghai Second Medical University,
^bDepartment of Surgery, Rui-Jin Hospital, and
^cDepartment of Digestive Disease, Xin-Hua Hospital, Shanghai Second Medical University, and
^dHealth Science Center, Shanghai Institutes for Biological Sciences, Shanghai Second Medical University, Chinese Academy of Sciences, Shanghai, China;
^eCiphergen Biosystems Ltd., Fremont, Calif., USA

Address of Corresponding Author

Oncology 2005;68:79-86 (DOI: 10.1159/000084824)

Key Words

Biomarkers
Mass spectrum
Pancreatic cancer
Surface-enhanced laser desorption/ionization
Serum biomarkers

Abstract

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%. Conclusion: The SELDI ProteinChip combined with an artificial intelligence classification algorithm shows great potential for the diagnosis of pancreatic cancer.

Author Contacts

Guo-Qiang Chen, MD, PhD
Department of Pathophysiology, Shanghai Second Medical University
No. 280, Chong-Qing South Road
Shanghai 200025 (China)
Tel./Fax +86 21 64154900, E-Mail chengq@shsmu.edu.cn

Article Information

Received: June 18, 2004
Accepted after revision: September 19, 2004
Published online: April 8, 2005
Number of Print Pages : 8
Number of Figures : 3, Number of Tables : 1, Number of References : 25

Medline Abstract (ID 15864000)

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