[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126535-en":3,"doc-seo-126535-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126535,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Discovering Glioma Tissue through Its Biomarkers' Detection in Blood by Raman Spectroscopy and Machine Learning","Gliomas, including glioblastoma multiforme, represent highly malignant brain tumors where late diagnosis limits effective outcomes. This study estimates the dependence between glioma tissue and blood serum biomarkers using Raman spectroscopy combined with machine-learning analysis. Intracranial mouse experiments employ continuous U87 glioma cell lines to model tumor progression. Informative Raman bands separate experimental and control groups, showing increased lactate, tryptophan, fatty acids, and lipids during glioblastoma development. Non-linear correlations between specific Raman spectral lines and tumor size enable serum analysis to track brain-tissue state changes throughout glioma progression.","pharmaceutics  \nArticle  \nDiscovering Glioma Tissue through Its Biomarkers' Detection in Blood by Raman Spectroscopy and Machine Learning  \nDenis Vrazhnov 1,2, Anna Mankova 3, Evgeny Stupak 4, Yury Kistenev 1,2, Alexander Shkurinov 1,3 and Olga Cherkasova 5,6, *  \nCitation: Vrazhnov, D.; Mankova, A.; Stupak, E.; Kistenev, Y.; Shkurinov, A.; Cherkasova, O. Discovering Glioma Tissue through Its Biomarkers' Detection in Blood by Raman Spectroscopy and Machine Learning. Pharmaceutics 2023, 15, 203 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)pharmaceutics15010203  \nAcademic Editor: Maria Carafa  \nReceived: 8 December 2022  \nRevised: 27 December 2022  \nAccepted: 29 December 2022  \nPublished: 6 January 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 634050 Tomsk, Russia 2 V.E. Zuev Institute of Atmospheric Optics SB RAS, 634055 Tomsk, Russia  \n3 Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia  \n4 Novosibirsk Research Institute of Traumatology and Orthopedics n.a. Ya.L. Tsivyan,  \n630091 Novosibirsk, Russia  \n5 Institute on Laser and Information Technologies, Branch of the Federal Scientific Research Centre“Crystallography and Photonics” of RAS, 140700 Shatura, Russia  \n6 Faculty of Automation and Computer Engineering, Novosibirsk State Technical University,  \n630073 Novosibirsk, Russia  \n* Correspondence: [cherkasova@laser.nsc.ru or o.p.cherkasova@gmail.com](cherkasova@laser.nsc.ru or o.p.cherkasova@gmail.com)  \nAbstract: 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.  \nKeywords: optical methods of tissue study; glioma tissue; Raman spectroscopy; machine learning; U87 glioblastoma  \n1. Introduction  \nThe most commonly occurring malignant brain tumors are gliomas, and among themis glioblastoma multiforme (GBM), which accounts for 14.3% of all tumors and 49.1% of malignant tumors [1–3] . GBM is the most aggressive, invasive, and undifferentiated typeof tumor, and has been designated grade IV by the World Health Organization [4,5] . GBMis among the deadliest neoplasms.  \nOne reason for the poor outcome of glioblastoma is a late-stage diagnosis, since most of the existing methods of noninvasive diagnostics, such as magnetic resonance imaging (MRI) [6–8] and computer tomography [9], are ineffective for diagnosing small-size tumors. Optical methods, such as fluorescence imaging [10], multiphoton microscopy [11], photoacoustic imaging [12], optical absorption spectroscopy [13–15], Raman spectroscopy and imaging [16,17], and terahertz (THz) imaging [18–20] are widely used for direct detection of glio","cbCaicHAeKqu1bR3","https://ap.wps.com/l/cbCaicHAeKqu1bR3","pdf",5968487,5,1,19,"English","en",105,"# Introduction\n## Optical and noninvasive glioma diagnostics\n## Liquid biopsy and blood-based biomarkers\n## Study rationale and model\n# Materials and Methods\n## Raman spectroscopy and spectral analysis\n## Machine learning group separability\n# Results\n## Informative Raman spectral bands\n## Biomarker changes during glioblastoma development\n## Non-linear relationship with tumor size\n# Conclusion","[{\"question\":\"What is the main goal of this paper?\",\"answer\":\"To estimate the relationship between glioma tissue and blood-serum biomarkers using Raman spectroscopy with machine learning.\"},{\"question\":\"How were glioma progression and biomarkers modeled in the study?\",\"answer\":\"Glioblastoma development was modeled by intracranially transplanting U87 continuous cell lines into mouse brains, then analyzing Raman spectra from dried blood serum.\"},{\"question\":\"Which Raman spectral components changed during glioblastoma development?\",\"answer\":\"Lactate, tryptophan, fatty acids, and lipids showed increased contributions in dried blood-serum Raman spectra.\"}]","Discovering Glioma Tissue through Its Biomarkers' Detection in Blood by Raman Spectroscopy and Machine Learning | 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is the main goal of this paper?","Question",{"text":77,"@type":78},"To estimate the relationship between glioma tissue and blood-serum biomarkers using Raman spectroscopy with machine learning.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How were glioma progression and biomarkers modeled in the study?",{"text":82,"@type":78},"Glioblastoma development was modeled by intracranially transplanting U87 continuous cell lines into mouse brains, then analyzing Raman spectra from dried blood serum.",{"name":84,"@type":75,"acceptedAnswer":85},"Which Raman spectral components changed during glioblastoma development?",{"text":86,"@type":78},"Lactate, tryptophan, fatty acids, and lipids showed increased contributions in dried blood-serum Raman 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