miRNA-target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancer

Thierry ChekouoKim-Anh Do


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 b...Continue Reading


Oct 31, 2015·Statistics in Medicine·Christine B PetersonMarina Vannucci
Aug 9, 2019·Interdisciplinary Sciences, Computational Life Sciences·Ashwinder Singh Yogita


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Related Concepts

Biological Markers
In Silico
Biochemical Pathway
MicroRNA Gene
RNA, Untranslated
Renal Carcinoma
Candidate Disease Gene

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