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Published on: November 3, 2010
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.
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.
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.
