Carcinogenesis is a complex biological process that is affected by multiple genes, some of which can be used as biomarkers for specific tumor stages or types. An effective method for predicting such tumor markers, which are important for both diagnosis and prevention, is gene expression profiling. Here, we used a classification method and survival tests to predict cancer biomarker genes from individual cancer gene expression profiles. To validate the ability of classification in our samples, an area under the curve was calculated using support vector machine classification methods for selected genes. Twenty-three of the candidate biomarkers were correlated with patient survival. To confirm classification performance in other samples, we validated our results by comparison with breast and ovarian cancer samples. We conclude that these 23 genes might be used as cancer biomarkers.
Cancer biomarker Bioinformatics Classification Meta-analysis Gene expression profile