ArticleBioinformatics advances2026
A kernel density estimation-based approach for quantifying O-GlcNAcylation dysregulation in cancer from gene expression data.
Article in Bioinformatics advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Motivation: O-GlcNAcylation, a dynamic post-translational modification regulated by O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), influences critical biological processes and is dysregulated in cancers. Direct measurement of O-GlcNAcylation dysregulation is challenging due to its instability and low-throughput nature, limiting large-scale studies. However, the regulatory simplicity of this system and the availability of transcriptomic data enable inference of dysregulation from OGT and OGA expression. Results: We introduce a nonparametric kernel density estimation-based approach to quantify O-GlcNAcylation dysregulation using joint OGT and OGA expression. In simulated datasets with varied expression patterns and controlled dysregulation levels, our method consistently outperformed canonical metrics in quantifying dysregulation. In TCGA data from six cancer types, inferred regulation scores were significantly lower in cancer samples (0.25-0.30 vs. 0.49-0.51) and showed strong distributional differences (Kolmogorov-Smirnov Availability and implementation: The code and datasets used in this study are freely available at https://github.com/wonder-ai/O-GlcNAcylation_Project under an open-source license.
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