Artificial neural networks analysis of surface-enhanced laser desorption/ionization mass spectra of serum protein pattern distinguishes colorectal cancer from healthy population.

Abstract

PURPOSE:

The low specificity and sensitivity of the carcinoembryonic antigen test makes it not an ideal biomarker for the detection of colorectal cancer. We developed and evaluated a proteomic approach for the simultaneous detection and analysis of multiple proteins for distinguishing individuals with colorectal cancer from healthy individuals.

EXPERIMENTAL DESIGN:

We subjected serum samples (including 55 colorectal cancer patients and 92 age- and sex-matched healthy individuals) from 147 individuals, for analysis by surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. Peaks were detected with Ciphergen SELDI software version 3.0. Using a multilayer artificial neural network with a back propagation algorithm, we developed a classifier for separating the colorectal cancer groups from the healthy groups.

RESULTS:

The artificial neural network classifier separated the colorectal cancer from the healthy samples, with a sensitivity of 91% and specificity of 93%. Four top-scored peaks, at m/z of 5,911, 8,930, 8,817, and 4,476, were finally selected as the potential "fingerprints" for detection of colorectal cancer.

CONCLUSIONS:

The combination of SELDI-TOF mass spectrometry with the artificial neural networks in the analysis of serum protein yields significantly higher sensitivity and specificity values for the detection and diagnosis of colorectal cancer.

• Authors: Yi-ding Chen, Shu Zheng, Jie-kai Yu, Xun Hu
• Journal: Clinical cancer research : an official journal of the American Association for Cancer Research (Clin Cancer Res) Vol. 10 Issue 24 Pg. 8380-5 (Dec 15 2004) ISSN: 1078-0432 [Print] United States
• PMID: 15623616 (Publication Type: Comparative Study, Journal Article, Research Support, Non-U.S. Gov't)

Chemical References

• Biomarkers, Tumor
• Blood Proteins
• CA-19-9 Antigen

Topics

• Adult
• Aged
• Aged, 80 and over
• Biomarkers, Tumor (metabolism)
• Blood Proteins (analysis)
• CA-19-9 Antigen (metabolism)
• Case-Control Studies
• Colorectal Neoplasms (blood)
• Diagnosis, Differential
• Female
• Humans
• Male
• Middle Aged
• Neural Networks, Computer
• Protein Array Analysis
• Sensitivity and Specificity
• Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization (methods)