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DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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A kernel density estimation-based approach for quantifying O-GlcNAcylation dysregulation in cancer from gene

Rastko Stojšin¹, Jinlian Wang¹, Hongfang Liu¹

¹Center for Translational AI Excellence and Applications in Medicine, D. Bradley McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States.

Bioinformatics Advances

|March 16, 2026

Summary

O-GlcNAcylation dysregulation in cancer can now be quantified using O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA) expression. This new transcriptomics-based method accurately infers and classifies cancer status, offering a scalable approach for large-scale studies.

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Published on: August 16, 2017

Area of Science:

Biochemistry and Molecular Biology
Cancer Research
Bioinformatics

Background:

O-GlcNAcylation is a crucial post-translational modification impacting biological processes and cancer development.
Measuring O-GlcNAcylation directly is difficult due to its instability and low-throughput methods.
OGT and OGA expression levels offer a simpler way to infer O-GlcNAcylation dysregulation.

Purpose of the Study:

To develop a novel, scalable method for quantifying O-GlcNAcylation dysregulation using gene expression data.
To validate the method's accuracy in simulated and real-world cancer datasets.
To enable large-scale analysis of O-GlcNAcylation patterns in cancer.

Main Methods:

Developed a nonparametric kernel density estimation approach using joint OGT and OGA expression.
Utilized simulated datasets with controlled dysregulation levels for method validation.
Applied the method to The Cancer Genome Atlas (TCGA) data from six cancer types.

Main Results:

The proposed method accurately quantified O-GlcNAcylation dysregulation, outperforming existing metrics.
Inferred regulation scores were significantly lower in cancer samples compared to healthy controls.
The method achieved accurate cancer status classification (AUROC: 0.71-0.75) and generalized to external datasets.

Conclusions:

A transcriptomics-based framework provides a scalable and interpretable method for quantifying O-GlcNAcylation dysregulation in cancer.
This approach overcomes limitations of direct measurement, facilitating large-scale cancer research.
The freely available code and data promote further investigation and application of this method.