Literature DB >> 26207874

Lipidomic differentiation between human kidney tumors and surrounding normal tissues using HILIC-HPLC/ESI-MS and multivariate data analysis.

Eva Cífková1, Michal Holčapek2, Miroslav Lísa1, David Vrána3, Bohuslav Melichar3, Vladimír Študent4.   

Abstract

The characterization of differences among polar lipid classes in tumors and surrounding normal tissues of 20 kidney cancer patients is performed by hydrophilic interaction liquid chromatography (HILIC) coupled to electrospray ionization mass spectrometry (ESI-MS). The detailed analysis of identified lipid classes using relative abundances of characteristic ions in negative- and positive-ion modes is used for the determination of more than 120 individual lipid species containing attached fatty acyls of different chain length and double bond number. Lipid species are described using relative abundances, providing a better visualization of lipidomic differences between tumor and normal tissues. The multivariate data analysis methods using unsupervised principal component analysis (PCA) and supervised orthogonal partial least square (OPLS) are used for the characterization of statistically significant differences in identified lipid species. Ten most significant up- and down-regulated lipids in OPLS score plots are also displayed by box plots. A notable increase of relative abundances of lipids containing four and more double bonds is detected in tumor compared to normal tissues.
Copyright © 2015 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Glycerophospholipids; HILIC-HPLC/ESI–MS; Kidney cancer; Lipidomics; Multivariate data analysis

Mesh:

Substances:

Year:  2015        PMID: 26207874     DOI: 10.1016/j.jchromb.2015.07.011

Source DB:  PubMed          Journal:  J Chromatogr B Analyt Technol Biomed Life Sci        ISSN: 1570-0232            Impact factor:   3.205


  19 in total

1.  Off-line mixed-mode liquid chromatography coupled with reversed phase high performance liquid chromatography-high resolution mass spectrometry to improve coverage in lipidomics analysis.

Authors:  Mónica Narváez-Rivas; Ngoc Vu; Guan-Yuan Chen; Qibin Zhang
Journal:  Anal Chim Acta       Date:  2016-12-10       Impact factor: 6.558

2.  Lipid Profiling of In Vitro Cell Models of Adipogenic Differentiation: Relationships With Mouse Adipose Tissues.

Authors:  Lucy Liaw; Igor Prudovsky; Robert A Koza; Rea V Anunciado-Koza; Matthew E Siviski; Volkhard Lindner; Robert E Friesel; Clifford J Rosen; Paul R S Baker; Brigitte Simons; Calvin P H Vary
Journal:  J Cell Biochem       Date:  2016-03-16       Impact factor: 4.429

3.  Comprehensive untargeted lipidomic analysis using core-shell C30 particle column and high field orbitrap mass spectrometer.

Authors:  Mónica Narváez-Rivas; Qibin Zhang
Journal:  J Chromatogr A       Date:  2016-02-22       Impact factor: 4.759

4.  MALDI Orbitrap Mass Spectrometry Profiling of Dysregulated Sulfoglycosphingolipids in Renal Cell Carcinoma Tissues.

Authors:  Robert Jirásko; Michal Holčapek; Maria Khalikova; David Vrána; Vladimír Študent; Zuzana Prouzová; Bohuslav Melichar
Journal:  J Am Soc Mass Spectrom       Date:  2017-03-30       Impact factor: 3.109

5.  Non-targeted Lipidomics Using a Robust and Reproducible Lipid Separation Using UPLC with Charged Surface Hybrid Technology and High-Resolution Mass Spectrometry.

Authors:  Giorgis Isaac; Vladimir Shulaev; Robert S Plumb
Journal:  Methods Mol Biol       Date:  2022

Review 6.  Lipidomics: Techniques, Applications, and Outcomes Related to Biomedical Sciences.

Authors:  Kui Yang; Xianlin Han
Journal:  Trends Biochem Sci       Date:  2016-09-20       Impact factor: 13.807

7.  Plasma lipidomics profiling identified lipid biomarkers in distinguishing early-stage breast cancer from benign lesions.

Authors:  Xiaoli Chen; Hankui Chen; Meiyu Dai; Junmei Ai; Yan Li; Brett Mahon; Shengming Dai; Youping Deng
Journal:  Oncotarget       Date:  2016-06-14

Review 8.  Advances in Lipidomics for Cancer Biomarkers Discovery.

Authors:  Francesca Perrotti; Consuelo Rosa; Ilaria Cicalini; Paolo Sacchetta; Piero Del Boccio; Domenico Genovesi; Damiana Pieragostino
Journal:  Int J Mol Sci       Date:  2016-11-28       Impact factor: 5.923

9.  Metabolomic and Lipidomic Profiling Identifies The Role of the RNA Editing Pathway in Endometrial Carcinogenesis.

Authors:  Tatiana Altadill; Tyrone M Dowdy; Kirandeep Gill; Armando Reques; Smrithi S Menon; Cristian P Moiola; Carlos Lopez-Gil; Eva Coll; Xavier Matias-Guiu; Silvia Cabrera; Angel Garcia; Jaume Reventos; Stephen W Byers; Antonio Gil-Moreno; Amrita K Cheema; Eva Colas
Journal:  Sci Rep       Date:  2017-08-18       Impact factor: 4.379

10.  Altered lipid metabolism in the aging kidney identified by three layered omic analysis.

Authors:  Fabian Braun; Markus M Rinschen; Valerie Bartels; Peter Frommolt; Bianca Habermann; Jan H J Hoeijmakers; Björn Schumacher; Martijn E T Dollé; Roman-Ulrich Müller; Thomas Benzing; Bernhard Schermer; Christine E Kurschat
Journal:  Aging (Albany NY)       Date:  2016-03       Impact factor: 5.682

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