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Artificial Neural Networks Analysis of Surface-Enhanced Laser Desorption/Ionization Mass Spectra of Serum Protein Pattern Distinguishes Colorectal Cancer from Healthy Population

¹ Department of Oncology, Second Affiliated Hospital, and ² Cancer Institute, College of Medicine, Zhejiang University, HangZhou, Zhejiang, Peoples Republic of China

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.

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