a limitation to the scope of the claims large meta analysis of multiple cancers reveals a common compact and highly prognostic hypoxia metagene gene expression studies attempt to extrapolate biologically and clinically relevant hypotheses from gene expression patterns however many current studies make little use of existing knowledge such as gene function within specific pathways and prognostic signatures are often derived with no reference to the functional roles of their components one increasingly popular method that aims to make use of prior knowledge is gene set enrichment analysis gsea subramanian et al 2005 gsea first conducts a supervised analysis by ranking genes according to their ability to discriminate between different sample groups and then maps them onto previously defined gene sets typically formed according to common function using annotation sources the goal is to identify sets containing a statistically significant number of highly ranked genes and then to use this information to provide functional characterizations for the samples in question although powerful gsea relies on stratification of the experimental samples into distinct groups often making it unsuitable for use with heterogeneous clinical datasets another approach often applied to microarray data involves creation of a co expression network within which each u2018 node u2019 represents a gene and u2018 edges u2019 are created between genes when their expression patterns are significantly correlated co expression networks have been used to formulate functional and clinical hypotheses from in vivo data butte amp kohane 2003 hahn.