Colorectal Cancer
Copyright ©The Author(s) 2004. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Nov 1, 2004; 10(21): 3127-3131
Published online Nov 1, 2004. doi: 10.3748/wjg.v10.i21.3127
An integrated approach to the detection of colorectal cancer utilizing proteomics and bioinformatics
Jie-Kai Yu, Yi-Ding Chen, Shu Zheng
Jie-Kai Yu, Cancer Institute, the Second Affiliated Hospital of Zhejiang University Medical College, Hangzhou 310009, Zhejiang Province, China
Jie-Kai Yu, College of Life Science of Zhejiang University, Hangzhou 310029, Zhejiang Province, China
Jie-Kai Yu, Hangzhou Genomics Institute, Hangzhou 310008, Zhejiang Province, China
Yi-Ding Chen, Department of Oncology, the Second Affiliated Hospital of Zhejiang University Medical College, Hangzhou 310009, Zhejiang Province, China
Shu Zheng, Cancer Institute, Zhejiang University, Hangzhou 310009, Zhejiang Province, China
Author contributions: All authors contributed equally to the work.
Supported by the Major State Basic Research Development Program of China 973 program, No. G1998051200
Correspondence to: Shu Zheng, Cancer Institute, Zhejiang University, Hangzhou 310009, Zhejiang Province, China. zhengshu@mail.hz.zj.cn
Telephone: +86-571-87783868 Fax: +86-571-87214404
Received: April 9, 2004
Revised: May 2, 2004
Accepted: May 9, 2004
Published online: November 1, 2004
Abstract

AIM: To find new potential biomarkers and to establish patterns for early detection of colorectal cancer.

METHODS: One hundred and eighty-two serum samples including 55 from colorectal cancer (CRC) patients, 35 from colorectal adenoma (CRA) patients and 92 from healthy persons (HP) were detected by surface-enhanced laser desorption/ionization mass spectrometry (SELDI-MS). The data of spectra were analyzed by bioinformatics tools like artificial neural network (ANN) and support vector machine (SVM).

RESULTS: The diagnostic pattern combined with 7 potential biomarkers could differentiate CRC patients from CRA patients with a specificity of 83%, sensitivity of 89% and positive predictive value of 89%. The diagnostic pattern combined with 4 potential biomarkers could differentiate CRC patients from HP with a specificity of 92%, sensitivity of 89% and positive predictive value of 86%.

CONCLUSION: The combination of SELDI with bioinformatics tools could help find new biomarkers and establish patterns with high sensitivity and specificity for the detection of CRC.

Keywords: $[Keywords]