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Cancer Epidemiol Biomarkers Prev. 2008 Oct;17(10):2835-45. doi: 10.1158/1055-9965.EPI-08-0231.

Diagnostic mRNA expression patterns of inflamed, benign, and malignant colorectal biopsy specimen and their correlation with peripheral blood results.

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2nd Department of Medicine, Semmelweis University, Szentkirályi str. 46, 1088 Budapest, Hungary.



Gene expression profile (GEP)-based classification of colonic diseases is a new method for diagnostic purposes. Our aim was to develop diagnostic mRNA expression patterns that may establish the basis of a new molecular biological diagnostic method.


Total RNA was extracted, amplified, and biotinylated from frozen colonic biopsies of patients with colorectal cancer (n=22), adenoma (n=20), hyperplastic polyp (n=11), inflammatory bowel disease (n=21), and healthy normal controls (n=11), as well as peripheral blood samples of 19 colorectal cancer and 11 healthy patients. Genome-wide gene expression profile was evaluated by HGU133plus2 microarrays. To identify the differentially expressed features, the significance analysis of microarrays and, for classification, the prediction analysis of microarrays were used. Expression patterns were validated by real-time PCR. Tissue microarray immunohistochemistries were done on tissue samples of 121 patients.


Adenoma samples could be distinguished from hyperplastic polyps by the expression levels of nine genes including ATP-binding cassette family A, member 8, insulin-like growth factor 1 and glucagon (sensitivity, 100%; specificity, 90.91%). Between low-grade and high-grade dysplastic adenomas, 65 classifier probesets such as aquaporin 1, CXCL10, and APOD (90.91/100) were identified; between colorectal cancer and adenoma, 61 classifier probesets including axin 2, von Willebrand factor, tensin 1, and gremlin 1 (90.91/100) were identified. Early- and advanced-stage colorectal carcinomas could be distinguished using 34 discriminatory transcripts (100/66.67).


Whole genomic microarray analysis using routine biopsy samples is suitable for the identification of discriminative signatures for differential diagnostic purposes. Our results may be the basis for new GEP-based diagnostic methods.

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