Abstract

BackgroundColorectal cancers (CRC) are associated with perturbations in cellular amino acids, nucleotides, pentose-phosphate pathway carbohydrates, and glycolytic, gluconeogenic, and tricarboxylic acid intermediates. A non-targeted global metabolome approach was utilized for exploring human CRC, adjacent mucosa, and stool. In this pilot study, we identified metabolite profile differences between CRC and adjacent mucosa from patients undergoing colonic resection. Metabolic pathway analyses further revealed relationships between complex networks of metabolites.MethodsSeventeen CRC patients participated in this pilot study and provided CRC, adjacent mucosa ~10 cm proximal to the tumor, and stool. Metabolomes were analyzed by gas chromatography-mass spectrometry (GC/MS) and ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS). All of the library standard identifications were confirmed and further analyzed via MetaboLyncTM for metabolic network interactions.ResultsThere were a total of 728 distinct metabolites identified from colonic tissue and stool matrices. Nineteen metabolites significantly distinguished CRC from adjacent mucosa in our patient-matched cohort. Glucose-6-phosphate and fructose-6-phosphate demonstrated 0.64-fold and 0.75-fold lower expression in CRC compared to mucosa, respectively, whereas isobar: betaine aldehyde, N-methyldiethanolamine, and adenylosuccinate had 2.68-fold and 1.88-fold higher relative abundance in CRC. Eleven of the 19 metabolites had not previously been reported for CRC relevance. Metabolic pathway analysis revealed significant perturbations of short-chain fatty acid metabolism, fructose, mannose, and galactose metabolism, and glycolytic, gluconeogenic, and pyruvate metabolism. In comparison to the 500 stool metabolites identified from human CRC patients, only 215 of those stool metabolites were also detected in tissue. This CRC and stool metabolome investigation identified novel metabolites that may serve as key small molecules in CRC pathogenesis, confirmed the results from previously reported CRC metabolome studies, and showed networks for metabolic pathway aberrations. In addition, we found differences between the CRC and stool metabolomes.ConclusionsStool metabolite profiles were limited for direct associations with CRC and adjacent mucosa, yet metabolic pathways were conserved across both matrices. Larger patient-matched CRC, adjacent non-cancerous colonic mucosa, and stool cohort studies for metabolite profiling are needed to validate these small molecule differences and metabolic pathway aberrations for clinical application to CRC control, treatment, and prevention.Electronic supplementary materialThe online version of this article (doi:10.1186/s40170-016-0151-y) contains supplementary material, which is available to authorized users.

Highlights

  • Colorectal cancers (CRC) are associated with perturbations in cellular amino acids, nucleotides, pentose-phosphate pathway carbohydrates, and glycolytic, gluconeogenic, and tricarboxylic acid intermediates

  • Data are presented as the mean fold difference in CRC metabolite abundance compared to adjacent mucosa for all patients

  • The information on the metabolic pathway, metabolite name, platform of detection, Kyoto Encyclopedia of Genes and Genomes (KEGG), Human Metabolome DataBase (HMDB), and PubChem identifiers for all 19 metabolites is listed in Additional file 1: Table S1

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Summary

Introduction

Colorectal cancers (CRC) are associated with perturbations in cellular amino acids, nucleotides, pentose-phosphate pathway carbohydrates, and glycolytic, gluconeogenic, and tricarboxylic acid intermediates. A non-targeted global metabolome approach was utilized for exploring human CRC, adjacent mucosa, and stool. In this pilot study, we identified metabolite profile differences between CRC and adjacent mucosa from patients undergoing colonic resection. In 2014, Qiu et al identified 15 significantly altered metabolites in matched surgical specimens by comparing CRC and adjacent mucosa from 3 different hospitals in China and 1 in the USA [9]. These metabolite signatures were used to predict the rate of recurrence and survival in patients after treatment [9]. Metabolic fingerprints that could distinguish adjacent mucosa from CRC may reveal stages of pathogenesis, inform the frequency of follow-up screening, and response to preventive measures needed for improved prognosis [13, 14]

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