In a recent article, published in PNAS, researchers have described a new method – Pheno-RNA – to identify candidate genes associated with specific phenotypes.
There are a variety of whole-genome approaches that can determine which genes are important for specific phenotypes. Transcriptional profiling, for example, can provide insight into differentially expressed genes. While some of these genes may be important for the process of interest, not all of them will be. Alternatively, systematic genome-scale mutational screens can also identify genes that affect a given phenotype. Nonetheless, these mutations may cause such phenotypes by indirect and/or artefactual effects. The integration of other data types is important in addressing the connection between genes and phenotypes.
Pheno-RNA is a phenotypic series. It is a set of quantitatively measured phenotypes ranging from null to severe. These phenotypes are generated by treating cells with a variety of experimental perturbations. The experimental profiles of the individual genes (under the specific conditions) are then correlated with a quantitative measurement of the phenotype. Therefore, the expression profiles of genes driving the phenotype should be highly correlated with the strength of the phenotype. Conversely, genes whose expression is regulated by only one or a few experimental conditions are likely passengers and not generally relevant for the phenotype. Unlike other approaches, Pheno-RNA correlates the transcriptional profile of a single gene to the strength of the phenotype.
In this study, the team applied Pheno-RNA to the process of cellular transformation. They used an inducible model in which transient activation of v-Src oncoprotein converts a non-transformed breast epithelial cell line into a stably transformed state. This transformation is mediated by an inflammatory feedback loop involving NF-κB, STAT3, and AP-1 factors and several genes that are directly coregulated by these factors.
Using this approach, the team identified ~200 genes whose expression profiles (over 17 conditions) showed remarkably high correlation to the level of transformation. These genes were enriched in biological categories important for transformation. Interestingly, 90 of these genes had not previously been associated with cancer. This suggests that they are potentially unknown genes with a role in cancer.
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