Glycosylation spectral signatures for glioma grade discrimination using Raman spectroscopy

BMC Cancer

Research

Claudio Angione¹  Nathan Coles²  Meez Islam²  Panagiota S. Filippou²  Agathe Quesnel²  Priyanka Dey³  Ahmad A. Khundakar⁴  Tuomo M. Polvikoski⁵  Tiago F. Outeiro⁶ 

[1] National Horizons Centre, Teesside University, 38 John Dixon Ln, DL1 1HG, Darlington, UK;School of Computing, Engineering & Digital Technologies, Teesside University, Darlington, UK;Centre for Digital Innovation, Teesside University, Darlington, UK;School of Health & Life Sciences, Teesside University, TS1 3BX, Middlesbrough, UK;National Horizons Centre, Teesside University, 38 John Dixon Ln, DL1 1HG, Darlington, UK;School of Health & Life Sciences, Teesside University, TS1 3BX, Middlesbrough, UK;National Horizons Centre, Teesside University, 38 John Dixon Ln, DL1 1HG, Darlington, UK;School of Pharmacy and Biomedical Sciences, University of Portsmouth, PO1 2UP, Portsmouth, UK;School of Health & Life Sciences, Teesside University, TS1 3BX, Middlesbrough, UK;National Horizons Centre, Teesside University, 38 John Dixon Ln, DL1 1HG, Darlington, UK;Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK;Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK;Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK;Department of Experimental Neurodegeneration, Center for Biostructural Imaging of Neurodegeneration, University Medical Center, Göttingen, Germany;Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany;Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Göttingen, Germany;

关键词: Raman spectroscopy;    Gliomas;    Biomolecular signatures;    Diagnosis;    Glioblastoma;    Glycosylation;   

DOI  :  10.1186/s12885-023-10588-w

 received in 2022-11-09, accepted in 2023-01-27,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundGliomas 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.MethodsRS 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.ResultsGlioma 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.ConclusionRS 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.

【 授权许可】

CC BY   
© Crown 2023

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