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Lipidomic Characterization of Tumor Tissues Using LC/MS, SFC/MS, MALDI-MS and Multivariate Data Analysis

Authors: Holčapek Michal | Cífková Eva | Lísa Miroslav | Chagovets Vitaliy Viktorovich | Vrána David | Gatěk Jiří | Melichar Bohuslav

Year: 2014

Type of publication: ostatní do riv

Page from-to: nestránkováno

Titles:

Language	Name	Abstract	Keywords
cze	Lipidomic Characterization of Tumor Tissues Using LC/MS, SFC/MS, MALDI-MS and Multivariate Data Analysis	Results Comprehensive lipidomic analyses of tumor tissues and surrounding normal tissues from several clinical trials (breast, kidney and lung cancer) were performed using optimized HILIC-HPLC/ESI-MS, SFC/MS, MALDI-Orbitrap MS methods. Individual lipid classes were quantified based on the addition of single IS and response factors for each class related to the IS. Statistically significant differences in average concentrations were observed several classes of polar lipids (PI, PE, LPE, SM, LPC, etc.). Detailed analysis of lipid species inside above mentioned classes was performed using relative abundances of deprotonated molecules in the negative-ion ESI mode or protonated molecules in the positive-ion ESI mode followed by MS/MS experiments. Multivariate data analysis using orthogonal 2 projections of latent structures (O2PLS) enables a clear differentiation of tumor and normal tissues based on changes of their lipidome. Conclusions The statistically significant lipidomic differences were described for different types of tumor tissues (e.g., breast, kidney, lung) in comparison with surrounding normal tissues of the same patient obtained after the surgery. This work was supported by ERC CZ project No. LL1302 (MSMT, Czech Republic). Novel Aspect Combination of UHPLC/MS, SFC/MS and MALDI-MS followed by the multivariate data analysis is used for detailed lipidomic characterization of cancer tissues.
eng	Lipidomic Characterization of Tumor Tissues Using LC/MS, SFC/MS, MALDI-MS and Multivariate Data Analysis	Results Comprehensive lipidomic analyses of tumor tissues and surrounding normal tissues from several clinical trials (breast, kidney and lung cancer) were performed using optimized HILIC-HPLC/ESI-MS, SFC/MS, MALDI-Orbitrap MS methods. Individual lipid classes were quantified based on the addition of single IS and response factors for each class related to the IS. Statistically significant differences in average concentrations were observed several classes of polar lipids (PI, PE, LPE, SM, LPC, etc.). Detailed analysis of lipid species inside above mentioned classes was performed using relative abundances of deprotonated molecules in the negative-ion ESI mode or protonated molecules in the positive-ion ESI mode followed by MS/MS experiments. Multivariate data analysis using orthogonal 2 projections of latent structures (O2PLS) enables a clear differentiation of tumor and normal tissues based on changes of their lipidome. Conclusions The statistically significant lipidomic differences were described for different types of tumor tissues (e.g., breast, kidney, lung) in comparison with surrounding normal tissues of the same patient obtained after the surgery. This work was supported by ERC CZ project No. LL1302 (MSMT, Czech Republic). Novel Aspect Combination of UHPLC/MS, SFC/MS and MALDI-MS followed by the multivariate data analysis is used for detailed lipidomic characterization of cancer tissues.	Lipidomic Characterization; Tumor; LC/MS; SFC/MS; MALDI-MS