An integrated approach to the detection of colorectal cancer utilizing proteomics and bioinformatics

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.

Figure 1

MSE values of the top 15 peaks. A: m/z of peaks. B: MSE values of peaks.

Figure 2

Accuracies of different combinations of peaks. A: Accuracies of different combinations of peaks in the pattern discriminating CRC patients from HP. B: Accuracies of differ-ent combinations of peaks in the pattern discriminating CRC patients from CRA patients.

Figure 3

Spectra and gel maps of potential biomarkers. A: Spectra and gel maps of the peak with the m/z of 5 911 Da. B: Spectra and gel maps of the peak with the m/z of 8 922, 8 944, 8 817 Da. C: Spectra and gel maps of the peak with the m/z of 17 247 Da.