Circadian Gene Selection for Time-to-event Phenotype by Integrating CNV and RNAseq Data.

Background: The endogenous circadian clock, which controls daily rhythms in the expression of at least half of the mammalian genome, has a major influence on cell physiology. Consequently, disruption of the circadian system is associated with wide range of diseases including cancer. While several circadian clock genes have been associated with cancer progression, little is known about the survival when two or more platforms are considered together. Our goal was to determine if survival outcomes are associated with circadian clock function. To accomplish this goal, we developed a Bayesian hierarchical survival model coupled with the global local shrinkage prior and applied this model to available RNASeq and Copy Number Variation data to select significant circadian genes associates with cancer progression.

Results: Using a Bayesian shrinkage approach with the Bayesian accelerated failure time (AFT) model we showed the circadian clock associated gene DEC1 is positively correlated to survival outcome in breast cancer patients. The R package circgene implementing the methodology is available at https://github.com/MAITYA02/circgene.

Conclusions: The proposed Bayesian hierarchical model is the first shrinkage prior based model in its kind which integrates two omics platforms to identify the significant circadian gene for cancer survival.