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AIM: To detect the new serum biomarkers for colorectal cancer (CRC) by serum protein profiling with surfaceenhanced laser desorption ionisation - time of flight mass spectrometry (SELDI-TOF MS). METHODS: Two independent serum sample sets were analysed separately with the ProteinChip technology (set A: 40 CRC + 49 healthy controls; set B: 37 CRC + 31 healthy controls), using chips with a weak cation exchange moiety and buffer pH 5, Discriminative power of differentially expressed proteins was assessed with a classification tree algorithm. Sensitivities and specificities of the generated classification trees were obtained by blindly applying data from set A to the generated trees from set B and vice versa. CRC serum protein profiles were also compared with those from breast, ovarian, prostate, and non-small cell lung cancer. RESULTS: Mass-to-charge ratios (m/z) 3.1×103, 3.3×103, 4.5×103, 6.6×103 and 28×103 were used as classifiers in the best-performing classification trees. Tree sensitivities and specificities were between 65% and 90%. Most of these discriminative m/z values were also different in the other tumour types investigated. M/z 3.3×103, main classifier in most trees, was a doubly charged form of the 6.6×l03-Da protein. The latter was identified as apolipoprotein C-I. M/z 3.1×103 was identified as an N-terminal fragment of albumin, and m/z 28×103 as apolipoprotein A-I. CONCLUSION: SELDI-TOF MS followed by classification tree pattern analysis is a suitable technique for finding new serum markers for CRC. Biomarkers can be identified and reproducibly detected in independent sample sets with high sensitivities and specificities. Although not specific for CRC, these biomarkers have a potential role in disease and treatment monitoring.
AIM: To detect the new serum biomarkers for colorectal cancer (CRC) by serum protein profiling with enhanced laser desorption ionisation - time of flight mass spectrometry (SELDI-TOF MS). METHODS: Two independent serum sample sets were were separately analyzed with the ProteinChip technology (Set A: 40 CRC + 49 healthy controls; set B: 37 CRC + 31 healthy controls), using chips with a weak cation exchange moiety and buffer pH 5, Discriminative power of differentially expressed proteins was assessed with a classification tree algorithm. Sensitivities and specificities of the generated classification trees were obtained by blindly applying data from set A to the generated trees from set B and vice versa. CRC serum protein profiles were also compared with those from breast, ovarian, prostate, and non-small cell lung cancer . RESULTS: Mass-to-charge ratios (m / z) 3.1 × 103, 3.3 × 103, 4.5 × 103, 6.6 × 103 and 28 × 103 were used as classifiers in the best- Most of these discriminative m / z values were also different in the other types of investigations. M / z 3.3 × 103, main classifier in most trees, was a doubly charged form of the 6.6 × 103-Da protein. The latter was identified as apolipoprotein CI. M / z 3.1 × 103 was identified as an N-terminal fragment of albumin, and m / z 28 × 103 as apolipoprotein AI. CONCLUSION: SELDI-TOF MS followed by classification tree pattern analysis is a suitable technique for finding new serum markers for CRC. Biomarkers can be identified and reproducibly detected in independent sample sets with high sensitivities and specificities. Although not specifically for CRC, these biomarkers have a potential role in disease and treatment monitoring .