Abstract
Glycosylation is a common, complex, non-linear post-translational modification. The biosynthesis of these structures is regulated by a set of ‘glycogenes’. The role of transcription factors (TFs) in regulating the glycogenes and related glycosylation pathways is yet unknown. This manuscript presents a multi-OMICs data-mining framework to computationally predict tissue specific TF activities and cell signaling pathways regulating the biosynthesis of specific glycan structures. It combines existing ChIP-Seq (Chromatin ImmunoPrecipitation Sequencing) and RNA-Seq data to reveal 20,617 potentially significant TF-glycogene relationships. This includes interactions involving 524 unique TFs and 341 glycogenes that span 29 TCGA (The Cancer Genome Atlas) cancer types. Here, TF-glycogene interactions appeared in clusters or ‘communities’, suggesting that they may collectively drive changes in sets of carbohydrate structures rather than unique glycans as disease progresses. Upon applying the Fisher’s exact test along with glycogene pathway ontology, we identify TFs that may specifically regulate the biosynthesis of individual glycan types. Integration with knowledge from the Reactome database established the link between cell signaling pathways, transcription factors, glycogene expression, and glycosylation pathways. Whereas analysis results are presented for all 29 cancer types, specific focus is placed on human luminal and basal breast cancer disease progression. This implicates a key role for TGF-β and Wnt signaling in regulating TFs that control both tumorigenesis and cellular glycosylation. Overall, the computational predictions in this manuscript present a rich dataset that is ripe for experimental testing and hypotheses validation.
Competing Interest Statement
The authors have declared no competing interest.
