Nuclear magnetic resonance and surface-assisted laser desorption/ionization mass spectrometry-based serum metabolomics of kidney cancer

Kidney cancer is one of the most frequently diagnosed and the most lethal urinary cancer. Despite all the efforts made, no serum-specific biomarker is currently used in the clinical management of patients with this tumor. In this study, comprehensive high-resolution proton nuclear magnetic resonance spectroscopy ((1)H NMR) and silver-109 nanoparticle-enhanced steel target laser desorption/ionization mass spectrometry ((109)AgNPET LDI MS) approaches were conducted, in conjunction with multivariate data analysis, to discriminate the global serum metabolic profiles of kidney cancer (n = 50) and healthy volunteers (n = 49). Eight potential biomarkers have been identified using (1)H NMR metabolomics and nine mass spectral features which differed significantly (p < 0.05) between kidney cancer patients and healthy volunteers, as observed by LDI MS. A partial least squares discriminant analysis (OPLS-DA) model generated from metabolic profiles obtained by both analytical approaches could robustly discriminate normal from cancerous samples (Q(2) > 0.7), area under the receiver operative characteristic curve (ROC) AUC > 0.96. Compared with healthy human serum, kidney cancer serum had higher levels of glucose and lower levels of choline, glycerol, glycine, lactate, leucine, myo-inositol, and 1-methylhistidine. Analysis of differences between these metabolite levels in patients with different types and grades of kidney cancer was undertaken. Our results, derived from the combination of LDI MS and (1)H NMR methods, suggest that serum biomarkers identified herein appeared to have great potential for use in clinical prognosis and/or diagnosis of kidney cancer. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00216-020-02807-1) contains supplementary material, which is available to authorized users.