METABOLITES PROFILE OF COLORECTAL CANCER CELLS AT DIFFERENT STAGES
DOI:
https://doi.org/10.22159/ijap.2019.v11s1.T0051Keywords:
Colorectal cancer, Metabolomics, Cancer stagesAbstract
Objective: The aim of this study is to characterize the metabolite profiles of colorectal cancer (CRC) cells of different stages of the disease to understand
the 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 SW
480 (stage B), HCT 15 and DLD-1 (stage C), and HCT 116 (stage D). Metabolites were extracted using methanol and water, and metabolic profiling was
performed 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 between
the CRC stages. Most notably, there were eight metabolites that were significantly upregulated in the more advanced stages (B, C, and D) compared
with 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 changes
occurring as CRC advances. The knowledge may help identify biomarkers for the staging of CRC, which could improve its prognosis as well as provide
a basis for the development of therapeutic interventions.
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Global cancer statistics 2018: GLOBOCAN estimates of incidence and
mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin
2018;68:394-424.
2. Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M,
et al. Cancer incidence and mortality worldwide: Sources, methods and
major patterns in GLOBOCAN 2012. Int J Cancer 2015;136:E359-86.
3. Compton CC, Greene FL. The staging of colorectal cancer: 2004 and
beyond. CA Cancer J Clin 2004;54:295-308.
4. Provenzale D, Jasperson K, Ahnen DJ, Aslanian H, Bray T, Cannon JA,
et al. Colorectal cancer screening, version 1.2015. J Natl Compr Canc
Netw 2015;13:959-68.
5. Bretthauer M. Evidence for colorectal cancer screening. Best Pract Res
Clin Gastroenterol 2010;24:417-25.
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American Cancer Society; 2017.
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Colorectal Cancer-Diagnosis, Screening and Management. United
Kingdom: IntechOpen; 2017.
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Metabolome Analysis: An Introduction. New Jersey: John Wiley and
Sons; 2007.
9. Nishiumi S, Kobayashi T, Ikeda A, Yoshie T, Kibi M, Izumi Y, et al.
A novel serum metabolomics-based diagnostic approach for colorectal
cancer. PLoS One 2012;7:e40459.
10. Qiu Y, Cai G, Su M, Chen T, Liu Y, Xu Y, et al. Urinary metabonomic
study on colorectal cancer. J Proteome Res 2010;9:1627-34.
11. Brown DG, Rao S, Weir TL, O’Malia J, Bazan M, Brown RJ, et al.
Metabolomics and metabolic pathway networks from human colorectal
cancers, adjacent mucosa, and stool. Cancer Metab 2016;4:11.
12. Williams MD, Zhang X, Park JJ, Siems WF, Gang DR, Resar LM, et al.
Characterizing metabolic changes in human colorectal cancer. Anal
Bioanal Chem 2015;407:4581-95.
13. Tian Y, Xu T, Huang J, Zhang L, Xu S, Xiong B, et al. Tissue
Metabonomic Phenotyping for Diagnosis and Prognosis of Human
Colorectal Cancer. Scientific Reports; 2016. p. 6.
14. Wang H, Wang L, Zhang H, Deng P, Chen J, Zhou B, et al. ¹H NMRbased
metabolic profiling of human rectal cancer tissue. Mol Cancer
2013;12:121.
15. Lin Y, Ma C, Liu C, Wang Z, Yang J, Liu X, et al. NMR-based fecal
metabolomics fingerprinting as predictors of earlier diagnosis in
patients with colorectal cancer. Oncotarget 2016;7:29454-64.
16. Yusof HM, Ab-Rahim S, Suddin LS, Saman MSA, Mazlan M.
Metabolomics profiling on different stages of colorectal cancer:
A systematic review. Malays J Med Sci 2018;25:16-34.
17. Cuperlovi?-Culf M, Barnett DA, Culf AS, Chute I. Cell culture
metabolomics: Applications and future directions. Drug Discov Today
2010;15:610-21.
18. Lauri I, Savorani F, Iaccarino N, Zizza P, Pavone LM, Novellino E,
et al. Development of an optimized protocol for NMR metabolomics
studies of human colon cancer cell lines and first insight from testing
of the protocol using DNA G-quadruplex ligands as novel anti-cancer
drugs. Metabolites 2016;6:E4.
19. Jiang W, Zhou L, Lin S, Li Y, Xiao S, Liu J, et al. Metabolic profiles of
gastric cancer cell lines with different degrees of differentiation. Int J
Clin Exp Pathol 2018;11:869-75.
20. Ser Z, Liu X, Tang NN, Locasale JW. Extraction parameters for
metabolomics from cultured cells. Anal Biochem 2015;475:22-8.
21. Bannur Z, Teh LK, Hennesy T, Rosli WR, Mohamad N, Nasir A, et al.
The differential metabolite profiles of acute lymphoblastic leukaemic
patients treated with 6-mercaptopurine using untargeted metabolomics
approach. Clin Biochem 2014;47:427-31.
22. Ehrig K, Kilinc MO, Chen NG, Stritzker J, Buckel L, Zhang Q, et al.
Growth inhibition of different human colorectal cancer xenografts after
a single intravenous injection of oncolytic vaccinia virus GLV-1h68.
J Transl Med 2013;11:79.
23. Gatzidou E, Mantzourani M, Giaginis C, Giagini A, Patsouris E,
Kouraklis G, et al. Augmenter of liver regeneration gene expression
in human colon cancer cell lines and clinical tissue samples. J BUON
2015;20:84-91.
24. Orgeron ML, Stone KP, Wanders D, Cortez CC, Van NT, Gettys TW,
et al. The impact of dietary methionine restriction on biomarkers of
metabolic health. Prog Mol Biol Transl Sci 2014;121:351-76.
25. Borrego SL, Fahrmann J, Datta R, Stringari C, Grapov D, Zeller M,
et al. Metabolic changes associated with methionine stress sensitivity
in MDA-MB-468 breast cancer cells. Cancer Metab 2016;4:9.
26. Cavuoto P, Fenech MF. A review of methionine dependency and the
role of methionine restriction in cancer growth control and life-span
extension. Cancer Treat Rev 2012;38:726-36.
27. Jeon H, Kim JH, Lee E, Jang YJ, Son JE, Kwon JY, et al. Methionine
deprivation suppresses triple-negative breast cancer metastasis in vitro
and in vivo. Oncotarget 2016;7:67223-34.
28. Giancaspero TA, Colella M, Brizio C, Difonzo G, Fiorino GM, Leone P,
et al. Remaining challenges in cellular flavin cofactor homeostasis and
flavoprotein biogenesis. Front Chem 2015;3:30.
29. Moat SJ, Ashfield-Watt PA, Powers HJ, Newcombe RG, McDowell IF.
Effect of riboflavin status on the homocysteine-lowering effect of
folate in relation to the MTHFR (C677T) genotype. Clin Chem
2003;49:295-302.
30. van den Donk M, Buijsse B, van den Berg SW, Ocké MC, Harryvan JL,
Nagengast FM, et al. Dietary intake of folate and riboflavin, MTHFR
C677T genotype, and colorectal adenoma risk: A dutch case-control
study. Cancer Epidemiol Biomarkers Prev 2005;14:1562-6.
31. Nakano E, Mushtaq S, Heath PR, Lee ES, Bury JP,
Riley SA, et al. Riboflavin depletion impairs cell proliferation in adult
human duodenum: Identification of potential effectors. Dig Dis Sci
2011;56:1007-19.
32. Fadaka A, Ajiboye B, Ojo O, Adewale O, Olayide I, Emuowhochere R.
Biology of glucose metabolization in cancer cells. J Oncol Sci
2017;3:45-51.
33. Rodríguez-Enríquez S, Juárez O, Rodríguez-Zavala JS, Moreno-
Sánchez R. Multisite control of the crabtree effect in ascites hepatoma
cells. Eur J Biochem 2001;268:2512-9.
34. Diaz-Ruiz R, Rigoulet M, Devin A. The warburg and crabtree effects:
On the origin of cancer cell energy metabolism and of yeast glucose
repression. Biochim Biophys Acta 2011;1807:568-76.
35. Vance JE, Vance DE. Biochemistry of Lipids, Lipoproteins and
Membranes. Amsterdam: Elsevier; 2008.
36. Santos CR, Schulze A. Lipid metabolism in cancer. FEBS J
2012;279:2610-23.
Published
15-09-2019
How to Cite
YUSOF, H. M., AB-RAHIM, S., WAN NGAH, W. Z., NATHAN, S., JAMAL A., R. A., & MAZLAN, M. (2019). METABOLITES PROFILE OF COLORECTAL CANCER CELLS AT DIFFERENT STAGES. International Journal of Applied Pharmaceutics, 11(5), 66–70. https://doi.org/10.22159/ijap.2019.v11s1.T0051
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