The Proteomics of Colorectal Cancer: Identification of a Protein Signature Associated with Prognosis

Colorectal cancer is one of the commonest types of cancer and there is requirement for the identification of prognostic biomarkers. In this study protein expression profiles have been established for colorectal cancer and normal colonic mucosa by proteomics using a combination of two dimensional gel electrophoresis with fresh frozen sections of paired Dukes B colorectal cancer and normal colorectal mucosa (n = 28), gel image analysis and high performance liquid chromatography-tandem mass spectrometry. Hierarchical cluster analysis and principal components analysis showed that the protein expression profiles of colorectal cancer and normal colonic mucosa clustered into distinct patterns of protein expression. Forty-five proteins were identified as showing at least 1.5 times increased expression in colorectal cancer and the identity of these proteins was confirmed by liquid chromatography-tandem mass spectrometry. Fifteen proteins that showed increased expression were validated by immunohistochemistry using a well characterised colorectal cancer tissue microarray containing 515 primary colorectal cancer, 224 lymph node metastasis and 50 normal colonic mucosal samples. The proteins that showed the greatest degree of overexpression in primary colorectal cancer compared with normal colonic mucosa were heat shock protein 60 (p < 0.001), S100A9 (p < 0.001) and translationally controlled tumour protein (p, 0.001). Analysis of proteins individually identified 14-3-3 beta as a prognostic biomarker (chi(2) = 6.218, p = 0.013, HR = 0.639, 95% CL 0.448-0.913). Hierarchical cluster analysis identified distinct phenotypes associated with survival and a two-protein signature consisting of 14-3-3b and aldehyde dehydrogenase 1 was identified as showing prognostic significance (chi(2) = 7.306, p = 0.007, HR = 0.504, 95% CL 0.303-0.838) and that remained independently prognostic (p = 0.01, HR = 0.416, 95% CL 0.208-0.829) in a multivariate model.