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

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.