In situ analysis of N-linked glycans as potential biomarkers of clinical course in human prostate cancer

Lindsey R. Conroy, Alexandra E. Stanback, Lyndsay E.A. Young, Harrison A. Clarke, Grant L. Austin, Jinze Liu, Derek B. Allison, Ramon C. Sun

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Prostate cancer is the most common cancer in men worldwide. Despite its prevalence, there is a critical knowledge gap in understanding factors driving disparities in survival among different cohorts of prostate cancer patients. Identifying molecular features separating disparate populations is an important first step in prostate cancer research that could lead fundamental hypotheses in prostate biology, predictive biomarker discovery, and personalized therapy. N-linked glycosylation is a co-translational event during protein folding that modulates a myriad of cellular processes. Recently, aberrant N-linked glycosylation has been reported in prostate cancers. However, the full clinical implications of dysregulated glycosylation in prostate cancer has yet to be explored. Herein, we performed direct on-tissue analysis of N-linked glycans using matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) from tissue microarrays of over 100 patient tumors with over 10 years of follow-up metadata. We successfully identified a panel of N-glycans that are unique between benign and prostate tumor tissue. Specifically, high-mannose as well as tri-and tetra-antennary N-glycans were more abundant in tumor tissue and increase proportionally with tumor grade. Further, we expanded our analyses to examine the N-glycan profiles of Black and Appalachian patients and have identified unique glycan signatures that correlate with recurrence in each population. Our study highlights the potential applications of MALDI-MSI for digital pathology and biomarker discovery for prostate cancer. Implications: MALDI-MSI identifies N-glycan perturbations in prostate tumors compared to benign tissue. This method can be utilized to predict prostate cancer recurrence and study prostate cancer disparities.

Original languageEnglish
JournalMolecular Cancer Research
Volume19
Issue number10
DOIs
StatePublished - Oct 2021

Bibliographical note

Funding Information:
Acknowledgements This study was supported by NIH grant R01 AG066653 (RSC), NINDS R21NS121966 (RSC), St Baldrick’s Career Development Award (RSC), Rally Foundation Independent Investigator Grant (RSC), V-Scholar Grant (RSC), and NIH Training Grant T32CA165990 (LRC). This research was also supported by funding from the University of Kentucky Markey Cancer Center and the NIH-funded Biospecimen Procurement & Translational Pathology Shared Resource Facility, as well as the Cancer Research Informatics Shared Resource Facility, of the University of Kentucky Markey Cancer Center P30CA177558.

Publisher Copyright:
© 2021 American Association for Cancer Research Inc.. All rights reserved.

ASJC Scopus subject areas

  • Molecular Biology
  • Oncology
  • Cancer Research

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