a simple but highly effective approach to evaluate the prognostic performance of gene expression signatures一个简单但非常有效的方法来评估预后基因表达特征的性能.pdfVIP

A Simple but Highly Effective Approach to Evaluate the Prognostic Performance of Gene Expression Signatures 1 2 2 1,3 1 Maud H. W. Starmans *, Glenn Fung , Harald Steck , Bradly G. Wouters , Philippe Lambin 1 Department of Radiation Oncology (Maastro), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands, 2 Department of CAD and Knowledge Solutions, Siemens Medical Solutions, Malvern, Pennsylvania, United States of America, 3 Ontario Cancer Institute/ Princess Margaret Hospital, University Health Network, Toronto, Ontario, Canada Abstract Background: Highly parallel analysis of gene expression has recently been used to identify gene sets or ‘signatures’ to improve patient diagnosis and risk stratification. Once a signature is generated, traditional statistical testing is used to evaluate its prognostic performance. However, due to the dimensionality of microarrays, this can lead to false interpretation of these signatures. Principal Findings: A method was developed to test batches of a user-specified number of randomly chosen signatures in patient microarray datasets. The percentage of random generated signatures yielding prognostic value was assessed using ROC analysis by calculating the area under the curve (AUC) in six public available cancer patient microarray datasets. We found that a signature consisting of randomly selected genes has an average 10% chance of reaching significance when assessed in a single dataset, but can range from 1% to ,40% depending on the dataset in question. Increasing the number of validation datasets markedly reduces this number. Conclusions: We have s