• colorectal cancer (CRC)
• heat shock proteins (HSPs)
• metastatic colorectal cancer

Colorectal cancer (CRC) is the second most common cause of cancer-related deaths in Europe and other Western countries, mainly due to the lack of well-validated clinically useful biomarkers with enough sensitivity and specificity to detect this disease at early stages. Although it is well known that the pathogenesis of CRC is a progressive accumulation of mutations in multiple genes, much less is known at the proteome level. Therefore, in the last years many proteomic studies have been conducted to find new candidate protein biomarkers for diagnosis, prognosis and as therapeutic targets for this malignancy, as well as to elucidate the molecular mechanisms of colorectal carcinogenesis. An important advantage of the proteomic approaches is the capacity to look for multiple differentially expressed proteins in a single study. This review provides an overview of the recent reports describing the different proteomic tools used for the discovery of new protein markers for CRC such as two-dimensional electrophoresis methods, quantitative mass spectrometry-based techniques or protein microarrays. Additionally, we will also focus on the diverse biological samples used for CRC biomarker discovery such as tissue, serum and faeces, besides cell lines and murine models, discussing their advantages and disadvantages, and summarize the most frequently identified candidate CRC markers.

• Colorectal cancer
• Serum
• Tissue
• Proteins
• Biomarkers
• Proteomics
• Protein identification
• Mass spectrometry

• Animals
• Biomarkers, Tumor/metabolism
• Cell Line, Tumor
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/metabolism
• Electrophoresis, Gel, Two-Dimensional
• Feces
• Humans
• Mass Spectrometry
• Mice
• Mutation
• Neoplasm Proteins/metabolism
• Protein Array Analysis/methods
• Proteome
• Proteomics/methods
• Subcellular Fractions

Obesity and associated reduced consumption of plant derived foods are linked to increased risk of colon cancer as well as a number of other organ specific cancers. Inflammatory processes are a contributing factor but the precise mechanisms remain elusive. Obesity and cancer incidence are increasing worldwide, presenting bleak prospects for reducing, or preventing, obesity related cancers. The incidence of these preventable cancers can be achieved with greater understanding of the molecular mechanisms linking diet and carcinogenesis. Janice Drew has developed a research program over recent years to investigate molecular mechanisms related to consumption of anti-inflammatory metabolites generated from consumption of plant based diets, the impact of high fat diets and associated altered metabolism and obesity on regulation of colon inflammatory responses and processes regulating the colon epithelium. Comprehensive strategies have been developed incorporating transcriptomics, including the novel gene expression technology, the GenomeLab System and proteomics, together with biochemical analyses of plasma and tissue samples to assess correlated changes in oxidative stress, inflammation and pathology. The approaches developed have achieved success in establishing antioxidant and anti-inflammatory activity of dietary antioxidants and associated genes and pathways that interact to modulate redox status in the colon. Cellular processes and genes altered in response to obesity and high fat diets have provided evidence of molecular mechanisms that are implicated in obesity related cancer.

• Diet and cancer
• Obesity-related cancer
• Adipokines
• Leptin
• Phenolic acids
• Mitochondria
• Proteomics
• Multiplex gene expression

Studies of several tumour types have shown that expression profiling of cellular protein extracted from surgical tissue specimens by direct mass spectrometry analysis can accurately discriminate tumour from normal tissue and in some cases can sub-classify disease. We have evaluated the potential value of this approach to classify various clinico-pathological features in colorectal cancer by employing matrix-assisted laser desorption ionisation time of-flight-mass spectrometry (MALDI-TOF MS).

Protein extracts from 31 tumour and 33 normal mucosa specimens were purified, subjected to MALDI-Tof MS and then analysed using the 'GenePattern' suite of computational tools (Broad Institute, MIT, USA). Comparative Gene Marker Selection with either a t-test or a signal-to-noise ratio (SNR) test statistic was used to identify and rank differentially expressed marker peaks. The k-nearest neighbours algorithm was used to build classification models either using separate training and test datasets or else by using an iterative, 'leave-one-out' cross-validation method.

73 protein peaks in the mass range 1800-16000Da were differentially expressed in tumour verses adjacent normal mucosa tissue (P ≤ 0.01, false discovery rate ≤ 0.05). Unsupervised hierarchical cluster analysis classified most tumour and normal mucosa into distinct cluster groups. Supervised prediction correctly classified the tumour/normal mucosa status of specimens in an independent test spectra dataset with 100% sensitivity and specificity (95% confidence interval: 67.9-99.2%). Supervised prediction using 'leave-one-out' cross validation algorithms for tumour spectra correctly classified 10/13 poorly differentiated and 16/18 well/moderately differentiated tumours (P = < 0.001; receiver-operator characteristics - ROC - error, 0.171); disease recurrence was correctly predicted in 5/6 cases and disease-free survival (median follow-up time, 25 months) was correctly predicted in 22/23 cases (P = < 0.001; ROC error, 0.105). A similar analysis of normal mucosa spectra correctly predicted 11/14 patients with, and 15/19 patients without lymph node involvement (P = 0.001; ROC error, 0.212).

Protein expression profiling of surgically resected CRC tissue extracts by MALDI-TOF MS has potential value in studies aimed at improved molecular classification of this disease. Further studies, with longer follow-up times and larger patient cohorts, that would permit independent validation of supervised classification models, would be required to confirm the predictive value of tumour spectra for disease recurrence/patient survival.