PURPOSE: Colorectal cancer is one of the most common malignancies. Substaging of the cancer is of importance not only to prognosis but also to treatment. Classification of substages based on DNA microarray technology is currently the most promising approach. We therefore investigated if gene expression microarrays could be used to classify colorectal tumors. METHODS: We used the Affymetrix oligonucleotide arrays to analyze the expression of more than 5,000 genes in samples from the sigmoid and upper rectum of the left colon. Five samples were from normal mucosa and five samples from each of the Dukes' stages A, B, C, and D. Expression data were filtered based on either covariance or a selection of the most significantly varying genes between tumor stages. RESULTS: A nearest neighbor classifier was used to classify normal, and Dukes' B and C samples with less than 20% error, whereas Dukes' A and D could not be classified correctly. A number of interesting gene clusters showed a discriminating difference between Dukes' B and C samples. These included mitochondrial genes, stromal remodeling genes, and genes related to cell adhesion. CONCLUSION: Molecular classification based on gene expression of one of the most common malignancies, colorectal cancer, now seems to be within reach. The data indicates that it is possible at least to classify Dukes' B and C colorectal tumors with microarrays.
PURPOSE:Colorectal cancer is one of the most common malignancies. Substaging of the cancer is of importance not only to prognosis but also to treatment. Classification of substages based on DNA microarray technology is currently the most promising approach. We therefore investigated if gene expression microarrays could be used to classify colorectal tumors. METHODS: We used the Affymetrix oligonucleotide arrays to analyze the expression of more than 5,000 genes in samples from the sigmoid and upper rectum of the left colon. Five samples were from normal mucosa and five samples from each of the Dukes' stages A, B, C, and D. Expression data were filtered based on either covariance or a selection of the most significantly varying genes between tumor stages. RESULTS: A nearest neighbor classifier was used to classify normal, and Dukes' B and C samples with less than 20% error, whereas Dukes' A and D could not be classified correctly. A number of interesting gene clusters showed a discriminating difference between Dukes' B and C samples. These included mitochondrial genes, stromal remodeling genes, and genes related to cell adhesion. CONCLUSION: Molecular classification based on gene expression of one of the most common malignancies, colorectal cancer, now seems to be within reach. The data indicates that it is possible at least to classify Dukes' B and C colorectal tumors with microarrays.
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