Identifying biological mechanisms for favorable cancer prognosis using non-hypothesis-driven iterative survival analysis.

ABSTRACT: Survival analyses based on the Kaplan-Meier estimate have been pervasively used to support or validate the relevance of biological mechanisms in cancer research. Recently, with the appearance of gene expression high-throughput technologies, this kind of analysis has been applied to tumor transcriptomics data. In a 'bottom-up' approach, gene-expression profiles that are associated with a deregulated pathway hypothetically involved in cancer progression are first identified and then subsequently correlated with a survival effect, which statistically supports or requires the rejection of such a hypothesis. In this work, we propose a 'top-down' approach, in which the clinical outcome (survival) is the starting point that guides the identification of deregulated biological mechanisms in cancer

SUBMITTER: Crespo I 

PROVIDER: S-EPMC5516921 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature