ArticlePharmaceutics2023
Discovering Glioma Tissue through Its Biomarkers' Detection in Blood by Raman Spectroscopy and Machine Learning.
Article in Pharmaceutics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Raman spectroscopy as a diagnostic tool for glioma: protocol for a systematic review.Diagnostic and prognostic research · 2026Review
- Overestimation of the classification model for Raman spectroscopy data of biological samples.mSystems · 2025Article
- Review
- Comment on "Raman spectroscopy combined with multivariate statistical algorithms for the simultaneous screening of cervical and breast cancers".Lasers in medical science · 2024Article
- Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms.Molecules (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
The most commonly occurring malignant brain tumors are gliomas, and among them is glioblastoma multiforme. The main idea of the paper is to estimate dependency between glioma tissue and blood serum biomarkers using Raman spectroscopy. We used the most common model of human glioma when continuous cell lines, such as U87, derived from primary human tumor cells, are transplanted intracranially into the mouse brain. We studied the separability of the experimental and control groups by machine learning methods and discovered the most informative Raman spectral bands. During the glioblastoma development, an increase in the contribution of lactate, tryptophan, fatty acids, and lipids in dried blood serum Raman spectra were observed. This overlaps with analogous results of glioma tissues from direct Raman spectroscopy studies. A non-linear relationship between specific Raman spectral lines and tumor size was discovered. Therefore, the analysis of blood serum can track the change in the state of brain tissues during the glioma development.
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Registered trials
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