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

Lei Xu,Donald Geman,Raimond L Winslow

doi:10.1186/1471-2105-8-275

Lei Xu, Donald Geman + Show 1 more

Open Access

https://doi.org/10.1186/1471-2105-8-275

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Abstract

BackgroundThere is a continuing need to develop molecular diagnostic tools which complement histopathologic examination to increase the accuracy of cancer diagnosis. DNA microarrays provide a means for measuring gene expression signatures which can then be used as components of genomic-based diagnostic tests to determine the presence of cancer.ResultsIn 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.ConclusionBy 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.

Highlights

There is a continuing need to develop molecular diagnostic tools which complement histopathologic examination to increase the accuracy of cancer diagnosis
We present a generalization of this method, termed top-scoring pair of groups (TSPG), which preserves the basic properties of the TSP classifier, namely invariance to normalization and comparison-based rules, but incorporates more genes into the decision-making
Data collection Microarray data sets were obtained from public gene expression data repositories, including Gene Expression Omnibus [20], Oncomine [21] and supporting web sites identified from the published literature

Summary

Introduction

There is a continuing need to develop molecular diagnostic tools which complement histopathologic examination to increase the accuracy of cancer diagnosis. 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 cancer from normal tissues [315]. The diversity of these signatures makes it difficult to distinguish the genes that play a crucial role in oncogenic processes from those that are spuriously differentially (page number not for citation purposes)

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