Integrated Analysis of Mutation Data from Various Sources Identifies Key Genes and Signaling Pathways in Hepatocellular Carcinoma

Abstract

Our workflow illustrates the increased statistical power of integrating multiple studies of the same subject, which can provide biological insights that would otherwise be masked under individual sample sets. This type of bioinformatics approach is consistent with the necessity of making the best use of the ever increasing data provided in valuable databases, such as TCGA, to enhance the speed of deciphering human cancers.