| Literature DB >> 27992229 |
Patrick Leuthold1,2, Elke Schaeffeler1,2, Stefan Winter1,2, Florian Büttner1,2, Ute Hofmann1,2, Thomas E Mürdter1,2, Steffen Rausch1,2,3, Denise Sonntag4, Judith Wahrheit4, Falko Fend5, Jörg Hennenlotter3, Jens Bedke3, Matthias Schwab1,2,6,7, Mathias Haag1,2.
Abstract
Metabolite profiling of tissue samples is a promising approach for the characterization of cancer pathways and tumor classification based on metabolic features. Here, we present an analytical method for nontargeted metabolomics of kidney tissue. Capitalizing on different chemical properties of metabolites allowed us to extract a broad range of molecules covering small polar molecules and less polar lipid classes that were analyzed by LC-QTOF-MS after HILIC and RP chromatographic separation, respectively. More than 1000 features could be reproducibly extracted and analyzed (CV < 30%) in porcine and human kidney tissue, which were used as surrogate matrices for method development. To further assess assay performance, cross-validation of the nontargeted metabolomics platform to a targeted metabolomics approach was carried out. Strikingly, from 102 metabolites that could be detected on both platforms, the majority (>90%) revealed Spearman's correlation coefficients ≥0.3, indicating that quantitative results from the nontargeted assay are largely comparable to data derived from classical targeted assays. Finally, as proof of concept, the method was applied to human kidney tissue where a clear differentiation between kidney cancer and nontumorous material could be demonstrated on the basis of unsupervised statistical analysis.Entities:
Keywords: LC−MS; Q-TOF; clear cell renal cell carcinoma (ccRCC); kidney cancer; lipidomics; metabolomics; nontargeted metabolomics; platform comparison; targeted metabolomics; tissue metabolomics
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Year: 2017 PMID: 27992229 DOI: 10.1021/acs.jproteome.6b00875
Source DB: PubMed Journal: J Proteome Res ISSN: 1535-3893 Impact factor: 4.466