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

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

Glycobiology

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.

Published: September 2022

AI Article Synopsis

  • Molecular biomarkers are critical for understanding biological processes and can indicate disorders; there's a growing focus on finding early cancer biomarkers to enhance treatment outcomes.* -
  • N-glycans show promise as cancer biomarkers when analyzed with associated proteins, but existing data is scattered and incompatible across various formats, limiting their research and clinical utility.* -
  • The development of a standardized cancer biomarker data model integrates 50 N-glycans from liver disease into a cohesive framework, improving the accessibility and usability of glycomics data for researchers and facilitating discoveries.*

Article 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/).

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9487899PMC
http://dx.doi.org/10.1093/glycob/cwac046DOI Listing

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