Validation of Urinary Charged Metabolite Profiles in Colorectal Cancer Using Capillary Electrophoresis-Mass Spectrometry

This study aimed to validate and reanalyze urinary biomarkers for detecting colorectal cancers (CRCs). We previously conducted urinary metabolomic analyses using capillary electrophoresis-mass spectrometry and found a significant difference in various metabolites, especially polyamines, between patients with CRC and healthy controls (HC). We analyzed additional samples and confirmed consistency between the newly and previously analyzed data. In total, we included 36 HC, 34 adenoma (AD), and 214 CRC samples, which were used for subsequent analyses. Among the 132 quantified metabolites, 16 exhibited consistent differences in both datasets, which included polyamines, etc. Pathway analyses of the integrated data revealed significant differences in many metabolites, such as glutamine, and metabolites of the TCA and urea cycles. The discrimination ability of the combination of multiple metabolites among the three groups was evaluated, which yielded higher sensitivity than tumor markers. The Mann–Whitney test was employed to evaluate the prognosis predictivity of the assessed metabolites and the difference between the patients with or without recurrence, which yielded 16 significantly different metabolites. Among these 16 metabolites, 11 presented significant prognosis predictivity. These data indicated the potential of metabolite-based discrimination of patients with CRC and AD from HC and prognosis predictivity of the monitored metabolites.

High-throughput and high-sensitivity capillary electrophoresis–mass spectrometry method for sulfur-containing amino acids

Biological thiol amino acids have been suggested as biomarkers for pathological changes because they are reactive chemicals that participate in various physiological processes. In this study, multisegmented injection capillary electrophoresis–mass spectrometry with online sample preconcentration was used for analysis of thiol amino acids and intermediates of sulfur metabolism in human glioma cell line U-251 with high accuracy, throughput, and sensitivity. This was achieved using multiple, large-volume injections for online sample preconcentration. The 16 intermediates of sulfur metabolism had a good linear correlation coefficient range of 0.984–1 and the limit of detection range was 1.4–203.9 ng/mL. The recovery ranges of most amino acids were 88.1–114.5%, 89.0–104.3%, and 76.9–104.5% at 0.3, 0.75, and 1.5 μg/mL, respectively. The relative standard deviation ranges for the inter- and intra-day precision were 1.8–10.7% and 4.3–18.8%, respectively. Compared with the traditional injection method, the analytical time for compounds in sulfur metabolism was reduced to 4 min/sample, the method throughput was enhanced five times, and the sensitivity was increased 14.4–33.1 times. Customized injection sequences were applied in experimental optimization. The developed method simplified the experimental optimization to one injection and is suitable for the analysis of sulfur metabolites in biological samples and has high sensitivity, throughput, speed, and accuracy.