Focused metabolic profiling is a powerful tool for the determination of biomarkers. Here, a more global proton nuclear magnetic resonance ((1)H NMR)-based metabolomic approach coupled with a relative simple ultra high performance liquid chromatography (UHPLC)-based focused metabolomic approach was developed and compared to characterize the systemic metabolic disturbances underlying esophageal cancer (EC) and identify possible early biomarkers for clinical prognosis. Serum metabolic profiling of patients with EC (n=25) and healthy controls (n=25) was performed by using both (1)H NMR and UHPLC, and metabolite identification was achieved by multivariate statistical analysis. Using orthogonal projection to least squares discriminant analysis (OPLS-DA), we could distinguish EC patients from healthy controls. The predictive power of the model derived from the UHPLC-based focused metabolomics performed better in both sensitivity and specificity than the results from the NMR-based metabolomics, suggesting that the focused metabolomic technique may be of advantage in the future for the determination of biomarkers. Moreover, focused metabolic profiling is highly simple, accurate and specific, and should prove equally valuable in metabolomic research applications. A total of nineteen significantly altered metabolites were identified as the potential disease associated biomarkers. Significant changes in lipid metabolism, amino acid metabolism, glycolysis, ketogenesis, tricarboxylic acid (TCA) cycle and energy metabolism were observed in EC patients compared with the healthy controls. These results demonstrated that metabolic profiling of serum could be useful as a screening tool for early EC diagnosis and prognosis, and might enhance our understanding of the mechanisms involved in the tumor progression.

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Arthus reaction (AR), a type of unconventional immune complex-mediated inflammation, is likely accompanied by alterations in circulating metabolites. Here, a proton nuclear magnetic resonance ((1)H NMR) spectroscopy method coupled with a rapid resolution liquid chromatography (RRLC) method was developed to evaluate the systemic metabolic consequences of AR and characterize metabolic aberrations. Serum and urine samples from AR rats and normal controls were compared to determine whether there were significant alterations associated with AR. The partial least squares discriminant analysis (PLS-DA) models of metabolomic results demonstrated good intergroup separations between AR rats and normal controls. Multivariate statistical analysis revealed significant alterations in the levels of 34 metabolites, which were termed as the disease-associated biomarkers. Differential metabolites identified from the metabolomic analysis suggested that AR caused dysfunctions of kidney and liver accompanied with changes in widespread metabolic pathways including the tricarboxylic acid (TCA) cycle, gut microbiota metabolism, lipids and cell membranes metabolism, glucose metabolism, fatty acid β-oxidation, amino acids metabolism and ketogenesis. This study assessed and provided important metabolomic variations in serum and urine associated with AR and, therefore, demonstrated metabolomics as a powerful approach for the complete elucidation of the underlying pathophysiologic mechanisms of AR.

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