Large-scale integration of cancer microarray data identifies a robust common cancer signature

In this study, we collect and integrate ~ 1500 microarray gene expression profiles from 26 published cancer data sets across 21 major human cancer types. We then apply a statistical method, referred to as the Top-Scoring Pair of Groups (TSPG) classifier, and a repeated random sampling strategy to the integrated training data sets and identify a common cancer signature consisting of 46 genes. These 46 genes are naturally divided into two distinct groups; those in one group are typically expressed less than those in the other group for cancer tissues. Given a new expression profile, the classifier discriminates cancer from normal tissues by ranking the expression values of the 46 genes in the cancer signature and comparing the average ranks of the two groups. This signature is then validated by applying this decision rule to independent test data.By combining the TSPG method and repeated random sampling, a robust common cancer signature has been identified from large-scale microarray data integration. Upon further validation, this signature may be useful as a robust and objective diagnostic test for cancer.During the past century, the presence of cancer in tissues has been diagnosed on the basis of histopathology [1]. The major limitation of this approach is that it cannot achieve high accuracy of prediction in clinical practice. Therefore, there has been a persistent need to identify robust cancer signatures which could complement conventional histopathologic evaluation to increase the accuracy of cancer detection [2]. More recently, DNA microarrays have been developed as a means to simultaneously measure the transcript abundance (gene expression level) of mRNA for thousands of genes. This technology provides a potentially powerful tool for identifying molecular signatures capable of accurately detecting the presence of cancer.Many studies have used DNA microarrays to identify cancer type-specific gene expression signatures which can discriminate certain types of can