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A Pan-Cancer Network Analysis with Integration of miRNA-Gene Targeting for Multiomics Datasets

Large-scale genomics studies provide researchers with access to extensive datasets with extensive detail and unprecedented scope that encompasses not only genes, but also more experimental functional units, including non-coding microRNAs (miRNAs). In order to analyze these high-ﬁdelity data while remaining faithful to the underlying biology, statistical methods are neces-sary that can reﬂect the full range of understanding in contemporary molecular biology, while remaining ﬂexible enough to analyze a wide range of data and complex phenomena. Leveraging multiple omics datasets, miRNA-gene targets as well as signaling pathway topology, we present an integrative linear model to analyze signaling pathways. Speciﬁcally, we use a mixed linear model to characterize tumor and healthy tissue, and execute statistical signiﬁcance testing to identify pathway disturbances. In this paper, pan-cancer analysis is performed for a wide range of signaling pathways. We discuss speciﬁc ﬁndings from this analysis, as well as an interactive data visualization available for public consumption that contains the full range of our analytic ﬁndings.