Modeling and integration of N-glycan biomarkers in a comprehensive biomarker data model

Authors

Daniel F. Lyman, The Department of Biochemistry and Molecular Medicine, Ross Hall, School of Medicine & Health Sciences, The George Washington University, 2300 Eye Street N.W., Washington, DC 20037, USA.
Amanda Bell, The Department of Biochemistry and Molecular Medicine, Ross Hall, School of Medicine & Health Sciences, The George Washington University, 2300 Eye Street N.W., Washington, DC 20037, USA.
Alyson Black, The McCormick Genomic and Proteomic Center, The Department of Biochemistry and Molecular Medicine, Ross Hall, School of Medicine & Health Sciences, The George Washington University, 2300 Eye Street N.W., Washington, DC 20037, USA.
Hayley Dingerdissen, The Department of Biochemistry and Molecular Medicine, Ross Hall, School of Medicine & Health Sciences, The George Washington University, 2300 Eye Street N.W., Washington, DC 20037, USA.
Edmund Cauley, The Department of Biochemistry and Molecular Medicine, Ross Hall, School of Medicine & Health Sciences, The George Washington University, 2300 Eye Street N.W., Washington, DC 20037, USA.
Nikhita Gogate, The Department of Biochemistry and Molecular Medicine, Ross Hall, School of Medicine & Health Sciences, The George Washington University, 2300 Eye Street N.W., Washington, DC 20037, USA.
David Liu, NASA Jet Propulsion Laboratory, 4800 Oak Grove Dr., Pasadena, California 91109, USA.
Ashia Joseph, The Department of Biochemistry and Molecular Medicine, Ross Hall, School of Medicine & Health Sciences, The George Washington University, 2300 Eye Street N.W., Washington, DC 20037, USA.
Robel Kahsay, The Department of Biochemistry and Molecular Medicine, Ross Hall, School of Medicine & Health Sciences, The George Washington University, 2300 Eye Street N.W., Washington, DC 20037, USA.
Daniel J. Crichton, NASA Jet Propulsion Laboratory, 4800 Oak Grove Dr., Pasadena, California 91109, USA.
Anand Mehta, The Department of Cell & Molecular Pharmacology, Basic Science Building 358, 173 Ashley Avenue, The Medical University of South Carolina, Charleston, SC 29425, USA.
Raja Mazumder, The Department of Biochemistry and Molecular Medicine, Ross Hall, School of Medicine & Health Sciences, The George Washington University, 2300 Eye Street N.W., Washington, DC 20037, USA.

Document Type

Journal Article

Publication Date

9-19-2022

Journal

Glycobiology

Volume

32

Issue

10

DOI

10.1093/glycob/cwac046

Keywords

N-linked glycans; cancer biomarker panel; data integration; glyco-informatics; liver disease

Abstract

Molecular biomarkers measure discrete components of biological processes that can contribute to disorders when impaired. Great interest exists in discovering early cancer biomarkers to improve outcomes. Biomarkers represented in a standardized data model, integrated with multi-omics data, may improve the understanding and use of novel biomarkers such as glycans and glycoconjugates. Among altered components in tumorigenesis, N-glycans exhibit substantial biomarker potential, when analyzed with their protein carriers. However, such data are distributed across publications and databases of diverse formats, which hamper their use in research and clinical application. Mass spectrometry measures of 50 N-glycans on 7 serum proteins in liver disease were integrated (as a panel) into a cancer biomarker data model, providing a unique identifier, standard nomenclature, links to glycan resources, and accession and ontology annotations to standard protein, gene, disease, and biomarker information. Data provenance was documented with a standardized United States Food and Drug Administration-supported BioCompute Object. Using the biomarker data model allows the capture of granular information, such as glycans with different levels of abundance in cirrhosis, hepatocellular carcinoma, and transplant groups. Such representation in a standardized data model harmonizes glycomics data in a unified framework, making glycan-protein biomarker data exploration more available to investigators and to other data resources. The biomarker data model we describe can be used by researchers to describe their novel glycan and glycoconjugate biomarkers; it can integrate N-glycan biomarker data with multi-source biomedical data and can foster discovery and insight within a unified data framework for glycan biomarker representation, thereby making the data FAIR (Findable, Accessible, Interoperable, Reusable) (https://www.go-fair.org/fair-principles/).

APA Citation

Lyman, Daniel F.; Bell, Amanda; Black, Alyson; Dingerdissen, Hayley; Cauley, Edmund; Gogate, Nikhita; Liu, David; Joseph, Ashia; Kahsay, Robel; Crichton, Daniel J.; Mehta, Anand; and Mazumder, Raja, "Modeling and integration of N-glycan biomarkers in a comprehensive biomarker data model" (2022). GW Authored Works. Paper 1609.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/1609

Department

Biochemistry and Molecular Medicine