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

Authors

• Daniel F. Lyman, The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037, United States of America.
• Amanda Bell, The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037, United States of America.
• Alyson Black, The Department of Cell & Molecular Pharmacology, The Medical University of South Carolina, Charleston, SC, 29403, United States of America.
• Hayley Dingerdissen, The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037, United States of America.
• Edmund Cauley, The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037, United States of America.
• Nikhita Gogate, The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037, United States of America.
• David Liu, NASA Jet Propulsion Laboratory, Pasadena, CA 91109, United States of America.
• Ashia Joseph, The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037, United States of America.
• Robel Kahsay, The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037, United States of America.
• Daniel J. Crichton, NASA Jet Propulsion Laboratory, Pasadena, CA 91109, United States of America.
• Anand Mehta, The Department of Cell & Molecular Pharmacology, The Medical University of South Carolina, Charleston, SC, 29403, United States of America.
• Raja Mazumder, The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037, United States of America.

Document Type

Journal Article

Publication Date

8-4-2022

Journal

Glycobiology

DOI

10.1093/glycob/cwac046

Keywords

Cancer biomarker panel; Data integration; Glyco-informatics; Liver disease; N-linked glycans

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 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 hampers their use in research and clinical application. Mass spectrometry measures of fifty N-glycans, on seven 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 FDA-supported BioCompute Object. Using the biomarker data model allows 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, 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 1519.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/1519

Department

Biochemistry and Molecular Medicine