Integrative analysis of multiple gene expression profiles applied to liver cancer study

A statistical method for combining multiple microarray studies has been previously developed by the authors. Here, we present the application of the method to our hepatocellular carcinoma (HCC) data and report new findings on gene expression changes accompanying HCC. From the cross-verification result of our studies and that of published studies, we found that single microarray analysis might lead to false findings. To avoid those pitfalls of single-set analyses, we employed our effect size method to integrate multiple datasets. Of 9982 genes analyzed, 477 significant genes were identified with a false discovery rate of 10%. Gene ontology (GO) terms associated with these genes were explored to validate our method in the biological context with respect to HCC. Furthermore, it was demonstrated that the data integration process increases the sensitivity of analysis and allows small but consistent expression changes to be detected. These integration-driven discoveries contained meaningful and interesting genes not reported in previous expression profiling studies, such as growth hormone receptor, erythropoietin receptor, tissue factor pathway inhibitor-2, etc. Our findings support the use of meta-analysis for a variety of microarray data beyond the scope of this specific application.