Nuclear magnetic resonance-based metabolomics and metabolic pathway networks from patient-matched esophageal carcinoma, adjacent noncancerous tissues and urine

Abstract

BACKGROUND Several studies have demonstrated a correlation between esophageal cancer (EC) and perturbed urinary metabolomic profiles, but none has described the correlation between urine metabolite profiles and those of the tumor and adjacent esophageal mucosa in the same patient. AIM To investigate how urinary metabolic phenotypes were linked to the changes in the biochemical landscape of esophageal tumors. METHODS Nuclear magnetic resonance-based metabolomics were applied to esophageal tumor tissues and adjacent normal mucosal tissues alongside patient-matched urine samples. RESULTS Analysis revealed that specific metabolite changes overlapped across both metrics, including glucose, glutamate, citrate, glycine, creatinine and taurine, indicating that the networks for metabolic pathway perturbations in EC, potentially involved in but not limited to disruption of fatty acid metabolism, glucose and glycolytic metabolism, tricarboxylic acid cycle and glutaminolysis. Additionally, changes in most urinary biomarkers correlated with changes in biomarker candidates in EC tissues, implying enhanced energy production for rapid cell proliferation. CONCLUSION Overall, these associations provide evidence for distinct metabolic signatures and pathway disturbances between the tumor tissues and urine of EC patients, and changes in urinary metabolic signature could reflect reprogramming of the aforementioned metabolic pathways in EC tissues. Further investigation is needed to validate these initial findings using larger samples and to establish the underlying mechanism of EC progression.

1H NMR‐based metabolomics of paired esophageal tumor tissues and serum samples identifies specific serum biomarkers for esophageal cancer

“…Esophageal tissue was prepared as previously described by us. 17 Briefly, frozen tissue specimens (approximately 300 mg) were thawed at 25 C, cut into small pieces, and homogenized in a solution containing distilled water (0.6 mL) and methanol (1.2 mL) at 16,000 rpm for 80 s. After homogenization, 1.2 mL chloroform and 1.2 mL distilled water were added, and the sample was vortex-mixed for 60 s. After 15-min incubation on ice, the sample was centrifuged at 2000 rpm for 5 min. Subsequently, water-soluble metabolites were extracted in the upper methanol phase while lipid-soluble metabolites were extracted in the lower chloroform phase.…”

“…To distinguish the serum samples of EC patients from those of HCs-and the tissues of cancer patients from their corresponding DNT samples-the 1 H NMR spectral data were postprocessed and subjected to multivariate analysis based on previously published research methods. [9][10][11]17 First, the normalized NMR spectral datasets were amplified as unit variances to emphasize the variation in metabolites with low abundance. Then, principal component analysis (PCA) was applied using the SIMCA-P + program (version 14.1;…”

“…The relative concentrations of the selected peaks with VIP values greater than 1 were evaluated by the integral of the area under the peak. The major metabolites in the spectra were identified based on the available literature 17,22,23 and data hosted on the Human Metabolome Database (http://www.hmdb.ca/).…”

“…Therefore, increased glutamate levels, together with depleted glutamine and elevated alanine levels, indicate that glutaminase activity was enhanced in the tumor tissue for EC development. 9,17 Elevated glucose levels, together with depleted lactate and alanine levels observed in the EC serum, suggest an acceleration of extracellular gluconeogenesis to replenish glucose consumption by tumor cells. 29,30 Glutamate elevation observed here in the EC serum could also indicate that tumor cell proliferation requires increased energy input, generated by augmented glutaminolysis.…”

“…Indeed, we have recently identified that metabolic changes reflect deregulation in metabolic pathways in the urine and tumor tissue of EC patients with high specificity; based on this, we developed a urinary biomarker panel comprising six metabolites that has excellent diagnostic value for EC. 17 However, urine metabolism primarily reflects the metabolic characteristics of the urinary system. Serum is another biofluid that can provide substantial information about the conditions within the body, and several NMR-based studies have identified perturbed serum metabolic signatures in EC patients.…”