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Cancer Research UK Department of Medical Oncology, University of Glasgow, Cancer Research UK Beatson Laboratories, Glasgow G61 1BD, Scotland, United Kingdom [J. L. D., W. N. K., K. A. O.]; Beatson Institute for Cancer Research, Cancer Research UK Beatson Laboratories, Glasgow G61 1BD, Scotland, United Kingdom [J. K. V.]; Department of Statistics, University of Glasgow, Glasgow G12 8QW, Scotland, United Kingdom [E. C. W.]; and University Department of Pathology, Glasgow Royal Infirmary, Glasgow G4 0SF, Scotland, United Kingdom [K. A. O.]
Patients presenting with metastatic adenocarcinoma of unknown origin are a common clinical problem. Their optimal management and therapy are facilitated by identification of the primary site, yet histologically these tumors are almost identical. Better tumor markers are needed to enable the assignment of metastases to likely sites of origin. In this study, hierarchical clustering of public serial analysis of gene expression data showed that adenocarcinomas and their metastases cluster according to their site of origin. A novel bioinformatic approach was developed to exploit the differences between these clusters, using diverse sources: public expression data from serial analysis of gene expression and digital differential display; and the published literature, including microarray studies. Sixty-one candidate tumor markers with expression predicted to be characteristic of the site of origin were identified. Eleven genes were tested by reverse transcription-PCR in primary adenocarcinomas from a range of sites, and seven (64%) were site-restricted. Analysis of public gene expression data sets is a powerful method for the identification of clinically relevant tumor markers.
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