Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Early diagnosis and accurate staging of the disease is vital to improve the prognosis. Metabolomics has been used to identify changes in metabolite profiles in the different stages of cancer in order to introduce new non-invasive molecular tools for staging. In this systematic review, we aim to identify the common metabolite changes in human biological samples and the dominant metabolic pathways associated with CRC progression. A broad systematic search was carried out from selected databases. Four reviewers screened and reviewed the titles, abstracts, and full-text articles according to the inclusion and exclusion criteria. Quality assessment was conducted on the eight articles which met the criteria. Data showed that the metabolites involved with redox status, energy metabolism and intermediates of amino acids, choline and nucleotides metabolism were the most affected during CRC progression. However, there were differences in the levels of individual metabolites detected between the studies, and this might be due to the study population, sample preparation, analytical platforms used and statistical tools. In conclusion, this systematic review highlights the changes in metabolites from early to late stages of CRC. Moreover, biomarkers for prognosis are important to reduce CRC-related mortality.
Objective: The aim of this study is to characterize the metabolite profiles of colorectal cancer (CRC) cells of different stages of the disease to understandthe pathophysiological changes that may help to identify prevention strategies as well as the sites for potential therapeutic drug actions.Methods: Six CRC cell lines of different stages (classified using the Dukes classification) were used, and they are SW 1116 (stage A), HT 29 and SW480 (stage B), HCT 15 and DLD-1 (stage C), and HCT 116 (stage D). Metabolites were extracted using methanol and water, and metabolic profiling wasperformed using liquid chromatography-mass spectrometry. Mass profiler professional software was used for statistical analysis.Results: There were 111,096 compounds detected across the samples, and 24 metabolites were identified to be significantly different betweenthe CRC stages. Most notably, there were eight metabolites that were significantly upregulated in the more advanced stages (B, C, and D) comparedwith Stage A. These metabolites include flavin mononucleotide, l-methionine, muricatacin, amillaripin, 2-methylbutyroylcarnitine, lumichrome,hexadeconoic acid, and lysoPE (0:0/16:0).Conclusion: This study showed that the expressions of metabolites at different stages of CRC were different, which represent the metabolic changesoccurring as CRC advances. The knowledge may help identify biomarkers for the staging of CRC, which could improve its prognosis as well as providea basis for the development of therapeutic interventions.
Introduction: Metabolomic studies on various colorectal cancer (CRC) cell lines have improved our understanding of the biochemical events underlying the disease. However, the metabolic profile dynamics associated with different stages of CRC progression is still lacking. Such information can provide further insights into the pathophysiology and progression of the disease that will prove useful in identifying specific targets for drug designing and therapeutics. Thus, our study aims to characterize the metabolite profiles in the established cell lines corresponding to different stages of CRC. Methods: Metabolite profiling of normal colon cell lines (CCD 841 CoN) and CRC cell lines corresponding to different stages, i.e., SW 1116 (stage A), HT 29 and SW 480 (stage B), HCT 15 and DLD-1 (stage C), and HCT 116 (stage D), was carried out using liquid chromatography-mass spectrometry (LC-MS). Mass Profiler Professional and Metaboanalyst 4.0 software were used for statistical and pathway analysis. METLIN database was used for the identification of metabolites. Results: We identified 72 differential metabolites compared between CRC cell lines of all the stages and normal colon cells. Principle component analysis and partial least squares discriminant analysis score plot were used to segregate normal and CRC cells, as well as CRC cells in different stages of the disease. Variable importance in projection score identified unique differential metabolites in CRC cells of the different stages. We identified 7 differential metabolites unique to stage A, 3 in stage B, 5 in stage C, and 5 in stage D. Conclusion: This study highlights the differential metabolite profiling in CRC cell lines corresponding to different stages. The identification of the differential metabolites in CRC cells at individual stages will lead to a better understanding of the pathophysiology of CRC development and progression and, hence, its application in treatment strategies.
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