Abstract
Omics data are now inexpensive to collect in vast quantities, across a wide variety of not only multiple data platform, but also distinct functional units. These bioinformatic datasets can enable scientific analysis of system-level cellular processes, including complex diseases such as cancers. Recent experimental research has found significant interactions between non-coding microRNAs (miRNAs) and genes. We propose an integrated, graphical regression model to endogenize the directed miRNA–gene target interactions and control for their effects in signaling pathway disturbance. We identify prominent miRNA–gene interactions and propose a graphical representation of the targeting. We merge this network with signaling pathway networks to obtain a cross-functional graph representation of regulatory relationships between genes and miRNAs. We integrate gene expression and miRNA expression, in tandem with graphical integration of epigenetic and transcriptomic data types, and estimate a statistical model. We find that our integration approach improves the statistical power, using a simulation study. We demonstrate our integrated model with an application to disturbance of the BRAF signaling pathway across 9 cancers. We find that integration of miRNA–gene targets clarifies the differential activity between healthy and tumor tissues, which in turn reflects different roles for the pathway across the different cancers.
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Linder, H., Zhang, Y. (2022). MiRNA–Gene Activity Interaction Networks (miGAIN): Integrated Joint Models of miRNA–Gene Targeting and Disturbance in Signaling Pathways. In: He, W., Wang, L., Chen, J., Lin, C.D. (eds) Advances and Innovations in Statistics and Data Science. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-08329-7_1
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DOI: https://doi.org/10.1007/978-3-031-08329-7_1
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