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Lookup NU author(s): Dr Tuomo Polvikoski, Professor Tiago OuteiroORCiD, Dr Ahmad Khundakar
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023, Crown.Background: Gliomas are the most common brain tumours with the high-grade glioblastoma representing the most aggressive and lethal form. Currently, there is a lack of specific glioma biomarkers that would aid tumour subtyping and minimally invasive early diagnosis. Aberrant glycosylation is an important post-translational modification in cancer and is implicated in glioma progression. Raman spectroscopy (RS), a vibrational spectroscopic label-free technique, has already shown promise in cancer diagnostics. Methods: RS was combined with machine learning to discriminate glioma grades. Raman spectral signatures of glycosylation patterns were used in serum samples and fixed tissue biopsy samples, as well as in single cells and spheroids. Results: Glioma grades in fixed tissue patient samples and serum were discriminated with high accuracy. Discrimination between higher malignant glioma grades (III and IV) was achieved with high accuracy in tissue, serum, and cellular models using single cells and spheroids. Biomolecular changes were assigned to alterations in glycosylation corroborated by analysing glycan standards and other changes such as carotenoid antioxidant content. Conclusion: RS combined with machine learning could pave the way for more objective and less invasive grading of glioma patients, serving as a useful tool to facilitate glioma diagnosis and delineate biomolecular glioma progression changes.
Author(s): Quesnel A, Coles N, Angione C, Dey P, Polvikoski TM, Outeiro TF, Islam M, Khundakar AA, Filippou PS
Publication type: Article
Publication status: Published
Journal: BMC Cancer
Year: 2023
Volume: 23
Issue: 1
Online publication date: 21/02/2023
Acceptance date: 27/01/2023
Date deposited: 10/03/2023
ISSN (electronic): 1471-2407
Publisher: BioMed Central Ltd
URL: https://doi.org/10.1186/s12885-023-10588-w
DOI: 10.1186/s12885-023-10588-w
PubMed id: 36809974
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