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000255496 037__ $$aDZNE-2023-00297
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000255496 1001_ $$aQuesnel, Agathe$$b0
000255496 245__ $$aGlycosylation spectral signatures for glioma grade discrimination using Raman spectroscopy.
000255496 260__ $$aHeidelberg$$bSpringer$$c2023
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000255496 520__ $$aGliomas 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.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.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.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.
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000255496 650_7 $$2Other$$aBiomolecular signatures
000255496 650_7 $$2Other$$aDiagnosis
000255496 650_7 $$2Other$$aGlioblastoma
000255496 650_7 $$2Other$$aGliomas
000255496 650_7 $$2Other$$aGlycosylation
000255496 650_7 $$2Other$$aRaman spectroscopy
000255496 650_2 $$2MeSH$$aHumans
000255496 650_2 $$2MeSH$$aSpectrum Analysis, Raman: methods
000255496 650_2 $$2MeSH$$aGlycosylation
000255496 650_2 $$2MeSH$$aGlioma: pathology
000255496 650_2 $$2MeSH$$aBrain Neoplasms: pathology
000255496 650_2 $$2MeSH$$aGlioblastoma: pathology
000255496 650_2 $$2MeSH$$aNeoplasm Grading
000255496 7001_ $$aColes, Nathan$$b1
000255496 7001_ $$aAngione, Claudio$$b2
000255496 7001_ $$aDey, Priyanka$$b3
000255496 7001_ $$aPolvikoski, Tuomo M$$b4
000255496 7001_ $$0P:(DE-2719)2814138$$aOuteiro, Tiago F$$b5$$udzne
000255496 7001_ $$aIslam, Meez$$b6
000255496 7001_ $$aKhundakar, Ahmad A$$b7
000255496 7001_ $$00000-0003-3974-988X$$aFilippou, Panagiota S$$b8
000255496 773__ $$0PERI:(DE-600)2041352-X$$a10.1186/s12885-023-10588-w$$gVol. 23, no. 1, p. 174$$n1$$p174$$tBMC cancer$$v23$$x1471-2407$$y2023
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