This study aims to test the predictive power of gene expression data derived from NIH's database dbEST, which collects gene expression results from a large number and variety of DNA array experiments. The motivation of this study is to make comparable experimental studies, which are usually performed only for one or a few tissues or organs, with a wide variety of other tissues. Confirmation of a good predictive power of dbEST would put a number of interesting and partially surprising recent findings, solely based on data mining, on a more solid basis than available so far. The expression of nine genes (eIF4E, DDX6, HAT1, USP28, HSP90(beta, PKM2, PLK1, COX2 and OPN) plus two calibration genes in paired normal and cancer colon tissues of eight individual patients was investigated by quantitative RT-PCR and compared with the predictions made by the data-base. GUS and beta-actin reveal only little variation among different patients, making them good internal calibration standards. In normal colon tissue, data mining correctly predicts the expression of all nine genes, which covers two orders of magnitude. In cancer, dbEST is somewhat less precise, but still valuable for the comparison with clinical results.