Abstract
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.
Highlights
Alterations in cell metabolism, including changes in lipid composition occurring during malignancy, are well characterized for various tumor types
Energy metabolism alteration is a well-known hallmark of cancer that leads to substantial changes in cell lipid composition [1]
Numerous lipid species became dysregulated in various cancer types [2]. At this moment, only some generic trends in upregulation of mono and diunsaturated phosphatidylcholines are observed across various diagnoses, in particular, in glioblastoma multiform [2,3], which attracts interest in the investigation of lipid composition alterations occurring during malignancy
Summary
Energy metabolism alteration is a well-known hallmark of cancer that leads to substantial changes in cell lipid composition [1]. An inhibition of aerobic glycolysis, caused by cancer tissue hypovascularity, triggers the beta-oxidation pathway of long-chain fatty acids [9]. During this process, pairs of carbon atoms cleave from the aliphatic chain, yielding acetyl-CoA, which is utilized to produce ATP required for cell metabolism. The molecular profile analysis is challenged by the complexity of data, the matrix effect, and possible signal instability. 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]
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