A Bayesian Approach to Biomarker Selection Through MiRNA Regulatory Network with Application to Kidney Cancer

• Activity Number: 499
• Type: Contributed
• Date/Time: Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Thierry Chekouo*+ and Francesco Stingo and James Doecke and Kim-Ahn Do
• Companies: MD Anderson Cancer Center and MD Anderson Cancer Center and CSIRO Computational Informatics and MD Anderson Cancer Center
• Keywords: Bayesian variable selection ; genomic data ; miRNA regulatory network

Abstract

The availability of cross-platform, large-scale genomic data has enabled the investigation of complex biological relationships for many cancers. Identification of reliable cancer-related biomarkers requires the characterization of multiple interactions across complex genetic networks. MicroRNAs are small non-coding RNAs that regulate gene expression; however, the direct relationship between a microRNA and its target gene is difficult to measure. We propose a novel Bayesian model to identify microRNAs and their target genes that are associated with survival time by incorporating the microRNA regulatory network through prior distributions. We assume that biomarkers involved in regulatory networks are likely associated with survival time. Using simulation studies, we assess the performance of our method, and apply it to experimental data of kidney renal cell carcinoma (KIRC) obtained from The Cancer Genome Atlas.