miRNA–target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancer
Thierry Chekouo
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 1400 Pressler Street, Unit 1411, Texas, 77030-3722 U.S.A.
Search for more papers by this authorCorresponding Author
Francesco C. Stingo
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 1400 Pressler Street, Unit 1411, Texas, 77030-3722 U.S.A.
email: [email protected]Search for more papers by this authorJames D. Doecke
CSIRO Computational Informatics/Australian e-Health Research Centre Level 5, UQ Health Sciences Building, 901/16 Royal Brisbane, Queensland, 4029 Australia
Search for more papers by this authorKim-Anh Do
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 1400 Pressler Street, Unit 1411, Texas, 77030-3722 U.S.A.
Search for more papers by this authorThierry Chekouo
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 1400 Pressler Street, Unit 1411, Texas, 77030-3722 U.S.A.
Search for more papers by this authorCorresponding Author
Francesco C. Stingo
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 1400 Pressler Street, Unit 1411, Texas, 77030-3722 U.S.A.
email: [email protected]Search for more papers by this authorJames D. Doecke
CSIRO Computational Informatics/Australian e-Health Research Centre Level 5, UQ Health Sciences Building, 901/16 Royal Brisbane, Queensland, 4029 Australia
Search for more papers by this authorKim-Anh Do
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 1400 Pressler Street, Unit 1411, Texas, 77030-3722 U.S.A.
Search for more papers by this authorSummary
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. We employ non-local prior distributions and a stochastic search method for the selection of biomarkers associated with the survival outcome. We use KEGG pathway information to incorporate correlated gene effects within regulatory networks. 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. Our novel method validates previously identified cancer biomarkers and identifies biomarkers specific to KIRC progression that were not previously discovered. Using the KIRC data, we confirm that biomarkers involved in regulatory networks are more likely to be associated with survival time, showing connections in one regulatory network for five out of six such genes we identified.
Supporting Information
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