ArticleBMC cancer2023
Glycosylation spectral signatures for glioma grade discrimination using Raman spectroscopy.
Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 23 citations in OpenAlex.
- Raman Spectroscopy for Differentiating High- and Low-Grade Canine Cutaneous Mast Cell Tumours.Veterinary and comparative oncology · 2026Article
- Challenges and opportunities for new intraoperative optical techniques in the surgical treatment of pituitary adenomas: a review.Journal of biomedical optics · 2025Review
- Fluorescence Guided Raman Spectroscopy enables the training of robust support vector machines for the detection of tumour marker proteins.Scientific reports · 2025Article
- Molecular Insights into α-Synuclein Fibrillation: A Raman Spectroscopy and Machine Learning Approach.ACS chemical neuroscience · 2025Article
- Review
- Alpha-synuclein aggregation induces prominent cellular lipid changes as revealed by Raman spectroscopy and machine learning analysis.Brain communications · 2025Article
- Raman and autofluorescence spectroscopy for in situ identification of neoplastic tissue during surgical treatment of brain tumors.Journal of neuro-oncology · 2024Article
- Precise Identification of Glioblastoma Micro-Infiltration at Cellular Resolution by Raman Spectroscopy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Machine Learning-Assisted Classification of Paraffin-Embedded Brain Tumors with Raman Spectroscopy.Brain sciences · 2024Article
- Distinguishing IDH mutation status in gliomas using FTIR-ATR spectra of peripheral blood plasma indicating clear traces of protein amyloid aggregation.BMC cancer · 2024Article
- Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS): Current Applications and Future Prospects.Biosensors · 2024Review
- Uncovering potential diagnostic and pathophysiological roles of α-synuclein and DJ-1 in melanoma.Cancer medicine · 2024Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
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.
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Registered trials
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