The Role of Lipids in the Classification of Astrocytoma and Glioblastoma Using Mass Spectrometry Tumor Profiling

Eliferov, Vasiliy A.; Zhvansky, Evgeny; Sorokin, Anatoly; Shurkhay, Vsevolod; Bormotov, Denis S.; Pekov, Stanislav I.; Никитин, П.В.; Ryzhova, Marina; Kulikov, Eugene E.; Потапов, А. А.; Nikolaev, Eugene; Popov, Igor

doi:10.1134/s1990750821020025

Cited by 1 publication

(2 citation statements)

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“…The molecular profile analysis is challenged by the complexity of data, the matrix effect, and possible signal instability. To overcome such complications, it is usually suggested to implement special algorithms for data evaluation, preprocessing, and further analysis using machine learning [21][22][23][24][25][26][27][28].…”

Section: Discussionmentioning

confidence: 99%

“…The same data was further implemented to create classifiers for rapid identification of various tumors (glioblastoma, astrocytoma, meningioma) based on ambient mass spectrometry [34][35][36], which has become the basis for developing new ambient ionization techniques designed for clinical application [10]. On the other hand, the presented dataset, as it is an example of data representing actual data obtained in a clinic, was used as a model for developing an instrument for an interactive and automated tool for evaluating the stability and reproducibility of mass spectra [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37], and for the unification of representations of high-and low-resolution mass spectra for further clinical implementation [23].…”

Section: User Notesmentioning

confidence: 99%

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Lipid Profiles of Human Brain Tumors Obtained by High-Resolution Negative Mode Ambient Mass Spectrometry

Zavorotnyuk

Pekov

Sorokin

et al. 2021

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Alterations in cell metabolism, including changes in lipid composition occurring during malignancy, are well characterized for various tumor types. However, a significant part of studies that deal with brain tumors have been performed using cell cultures and animal models. Here, we present a dataset of 124 high-resolution negative ionization mode lipid profiles of human brain tumors resected during neurosurgery. The dataset is supplemented with 38 non-tumor pathological brain tissue samples resected during elective surgery. The change in lipid composition alterations of brain tumors enables the possibility of discriminating between malignant and healthy tissues with the implementation of ambient mass spectrometry. On the other hand, the collection of clinical samples allows the comparison of the metabolism alteration patterns in animal models or in vitro models with natural tumor samples ex vivo. The presented dataset is intended to be a data sample for bioinformaticians to test various data analysis techniques with ambient mass spectrometry profiles, or to be a source of clinically relevant data for lipidomic research in oncology.

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