ORIGINAL RESEARCH

Comparison of lipid alterations in astrocytomas with increasing grade

About authors

1 Skolkovo Institute of Science and Technology, Moscow, Russia

2 Moscow Institute of Physics and Technology, Dolgoprudny, Russia

3 Siberian State Medical University, Tomsk, Russia

4 Semenov Federal Research Center for Chemical Physics, Moscow, Russia

Correspondence should be addressed: Igor A. Popov
Institutskiy per., 9, str. 7, Dolgoprudny, 141701, Russia; ur.tpim@ai.vopop

About paper

Funding: the study was performed within the framework of the state assignment of the Ministry of Science and Higher Education of the Russian Federation (agreement № 075-03-2022-107, project № 0714-2020-0006). The study involved the use of equipment of the Semenov Federal Research Center for Chemical Physics RAS.

Author contribution: Pekov SI — concept, data analysis, manuscript writing; Bocharov KV — experimental procedure, resource provision; Bormotov DS — experimental procedure, data analysis; Eliferov VA — experimental procedure, communication with surgeons; Parochkina EV — experimental procedure; Sorokin AA — data analysis, concept; Nikolaev EN — resource provision; Popov IA — search of funding sources, research management.

Compliance with ethical standards: the study was approved by the Ethics Committee of the Burdenko Research Institute of Neurosurgery (protocols № 40 dated 12 April 2016 and № 131 dated 17 July 2018) and conducted in accordance with the principles of the Declaration of Helsinki (2000) and its subsequent revisions. All patients submitted the informed consent to study participation and the use of biomaterial for scientific purposes.

Received: 2024-01-11 Accepted: 2024-02-07 Published online: 2024-02-24
|
  1. Kreatsoulas D, Damante M, Gruber M, et al. Supratotal surgical resection for low-grade glioma: a systematic review. Cancers (Basel). 2023; 15 (9): 2493.
  2. Karschnia P, Vogelbaum MA, van den Bent M, et al. Evidencebased recommendations on categories for extent of resection in diffuse glioma. Eur J Cancer. 2021; 149: 23–33.
  3. Chanbour H, Chotai S. Review of intraoperative adjuncts for maximal safe resection of gliomas and its impact on outcomes. Cancers (Basel). 2022; 14: 22.
  4. Bogusiewicz J, Bojko B. Insight into new opportunities in intrasurgical diagnostics of brain tumors. TrAC — Trends Anal Chem. Elsevier B.V. 2023; 162: 117043.
  5. Cordova JS, Gurbani SS, Olson JJ, et al. A systematic pipeline for the objective comparison of whole-brain spectroscopic MRI with histology in biopsy specimens from grade 3 glioma. Tomography. 2016; 2 (2): 106–16.
  6. Schupper AJ, Rao M, Mohammadi N, et al. Fluorescence-guided surgery: a review on timing and use in brain tumor surgery. Front Neurol. 2021; 12.
  7. Behbahaninia M, Martirosyan NL, Georges J, et al. Intraoperative fluorescent imaging of intracranial tumors: a review. Clin Neurol Neurosurg. Elsevier B.V. 2013; 115 (5): 517–28.
  8. Pekov SI, Bormotov DS, Nikitin PV, et al. Rapid estimation of tumor cell percentage in brain tissue biopsy samples using inline cartridge extraction mass spectrometry. Anal Bioanal Chem. Analytical and Bioanalytical Chemistry. 2021; 413 (11): 2913–22.
  9. Brown HM, Alfaro CM, Pirro V, et al. Intraoperative mass spectrometry platform for IDH mutation status prediction, glioma diagnosis, and estimation of tumor cell infiltration. J Appl Lab Med. 2021; 6 (4): 902–16.
  10. Eberlin LS, Dill AL, Golby AJ, et al. Discrimination of human astrocytoma subtypes by lipid analysis using desorption electrospray ionization imaging mass spectrometry. Angew Chemie - Int Ed. 2010; 49 (34): 5953–6.
  11. Bormotov D, Shamraeva M, Kuzin A, et al. Ambient ms profiling of meningiomas: intraoperative oncometabolite-based monitoring. Bull Russ State Med Univ. 2022; 2022 (6): 74–81.
  12. King ME, Lin M, Spradlin M, et al. Advances and emerging medical applications of direct mass spectrometry technologies for tissue analysis. Annu Rev Anal Chem. 2023; 16: 1–25.
  13. Eberlin LS, Margulis K, Planell-Mendez I, et al. Pancreatic cancer surgical resection margins: molecular assessment by mass spectrometry imaging. PLOS Med. 2016; 13 (8): e1002108.
  14. Sorokin A, Shurkhay V, Pekov S, et al. Untangling the metabolic reprogramming in brain cancer: discovering key molecular players using mass spectrometry. Curr Top Med Chem. 2019; 19 (17): 1521–34.
  15. Duraj T, García-Romero N, Carrión-Navarro J, et al. Beyond the Warburge effect: oxidative and glycolytic phenotypes coexist within the metabolic heterogeneity of glioblastoma. Cells. 2021; 10 (2): 202.
  16. Pekov SI, Sorokin AA, Kuzin AA, et al. Analysis of phosphatidylcholines alterations in human glioblastomas ex vivo. Biochem Suppl Ser B Biomed Chem 2021; 15 (3): 241–7.
  17. McNeill RS, Vitucci M, Wu J, et al. Contemporary murine models in preclinical astrocytoma drug development. Neuro Oncol. 2015; 17 (1): 12–28.
  18. Mason SE, Manoli E, Alexander JL, et al. Lipidomic profiling of colorectal lesions for real-time tissue recognition and riskstratification using rapid evaporative ionization mass spectrometry. Ann Surg. 2023; 277 (3): e569–e577.
  19. Bormotov DS, Eliferov VA, Peregudova OV, et al. Incorporation of a disposable ESI emitter into inline cartridge extraction mass spectrometry improves throughput and spectra stability. J Am Soc Mass Spectrom. 2023; 34 (1): 119–22.
  20. Ahdesmäki M, Strimmer K. Feature selection in omics prediction problems using cat scores and false nondiscovery rate control. Ann Appl Stat. 2010; 4 (1): 503–19.
  21. Wu X, Geng F, Cheng X, et al. Lipid droplets maintain energy homeostasis and glioblastoma growth via autophagic release of stored fatty acids. Science. Elsevier Inc., 2020; 23 (10): 101569.
  22. Jaraíz-Rodríguez M, del Prado L, Balsa E. Metabolic remodeling in astrocytes: paving the path to brain tumor development. Neurobiol Dis. 2023; 188: 106327.
  23. Panov A, Orynbayeva Z, Vavilin V, et al. Fatty acids in energy metabolism of the central nervous system. Biomed Res Int. 2014; 2014.