NIU Yan,ZHOU Yi,ZHENG Hong, et al. Metabonomics analysis of colorectal carcinoma cell lines SW480 and SW620 with different metastatic potentials[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2019, 49(4): 243-248.
Abstract：Objective: To study the metabolic profiles of human colorectal carcinoma cell lines with different metastatic potentials and analyze the relationship between metabolites and the metastasis of colorectal carcinomas. Methods: The characteristic alterations in metabolites of two isogenic colorectal cancer cell lines (highly metastatic SW620 and nonmetastatic SW480) were explored by the PLS-DA model based on the 1H NMR spectra. Moreover, Seahorse XF analyzer was used to assess ECAR and OCR. Results: The metabolic profile of SW620 and SW480 cell lines displayed obvious differences along in the PC1 direction. Compared with SW480, lactate, succinic, isoleucine, acetate, glycine and glutamine were increased, and phosphorylcholine, ATP, inositol and taurine were reduced in SW620 cells (P<0.05). SW620 cells with high metastatic potential have higher levels of glycolysis, TCA cycle activity, ECAR and OCR levels. Conclusion: The metabolic profile of SW620 cells with high metastatic potential shows clear differences from SW480 cells, and a variety of metabolites can be involved in the metastasis of colorectal carcinoma. Compared with SW480 cells, SW620 has a higher metabolic base potential, suggesting that it needs energy for high metastatic ability. Meanwhile, sw620 can adapt to its microenvironment more quickly, suggesting that the occurrence of metastasis is closely related to metabolism.
[1] TORRE L A, BRAY F, SIEGEL R L, et al. Global cancer statistics, 2012[J]. CA Cancer J Clin, 2015, 65(2): 87-108.
[2] CHEN W, ZHENG R, BAADE P D, et al. Cancer statistics in China, 2015[J]. CA Cancer J Clin, 2016, 66(2): 115-132.
[3] HANAHAN D, WEINBERG R A. Hallmarks of cancer: the next generation[J]. Cell, 2011, 144(5): 646-674.
[4] Cairns R A, Harris I S, Mak T W. Regulation of cancer cell metabolism[J]. Nat Rev Cancer, 2011, 11(2): 85-95.
[5] Yu L J, Wall B A, Wangari-Talbot J, et al. Metabotropic glutamate receptors in cancer[J]. Neuropharmacology, 2017, 115: 193-202.
[6] Lee J H, Cho H S, Lee J J, et al. Plasma glutamate carboxypeptidase is a negative regulator in liver cancer metas-tasis[J]. Oncotarget, 2016, 7(48): 79774-79786.
[7 ] ZHAO L, GAO H, LIAN F, et al. ¹H-NMR-based metabonomic analysis of metabolic profiling in diabetic nephropathy rats induced by streptozotocin[J]. Am J Physiol Renal Physiol, 2011, 300(4): F947-956.
[8] DONG B, JIA J, HU W, et al. Application of ¹H NMR metabonomics in predicting renal function recoverability after the relief of obstructive uropathy in adult patients[J]. Clin Biochem, 2013, 46(4-5): 346-353.
[9] Wang D, Zhao L, Zheng H, et al. Time-dependent lactate production and amino acids utilization in cultured astrocytes under high glucose exposure[J]. Mol Neurobiol, 2017, 55(1): 1-11.
[10] Zhang X, Lin Q, Chen J, et al. High glucose-induced cardiomyocyte death may be linked to unbalanced branched-chain amino acids and energy metabolism[J]. Molecules, 2018, 23(4): 807-821.
[11] 王丹, 郑涌泉, 赵良才, 等. 核磁共振氢谱检测糖尿病不同发病阶段额叶和枕叶的代谢变化[J]. 温州医科大学学报, 2017, 47(2): 90-95.
[12] 魏婷婷, 赵良才, 高红昌. 基于1H NMR的代谢组学分析糖尿病肾病小鼠血清的代谢模式[J]. 温州医科大学学报, 2018, 48(5): 313-318.
[13] ZHENG H, ZHAO L, XIA H, et al. NMR-Based metabolomics reveal a recovery from metabolic changes in the striatum of 6-OHDA-induced rats treated with basic fibroblast growth factor[J]. Mol Neurobiol, 2016, 53(10): 6690-6697.
[14] ZHAO L, DONG M, LIAO S, et al. Identification of key metabolic changes in renal interstitial fibrosis rats using metabonomics and pharmacology[J]. Sci Rep, 2016, 6: 27194.
[15] ZAYTSEVA Y Y, HARRIS J W, MITOV M I, et al. Increased expression of fatty acid synthase provides a survival advantage to colorectal cancer cells via upregulation of cellular respiration[J]. Oncotarget, 2015, 6(22): 18891-18904.
[16] SCHARPING N E, MENK A V, MORECI R S, et al. The tumor microenvironment represses T cell mitochondrial biogenesis to drive intratumoral T cell metabolic insufficiency and dysfunction[J]. Immunity, 2016, 45(3): 701-703.
[17] SCHELL J C, OLSON K A, JIANG L, et al. A role for the mitochondrial pyruvate carrier as a repressor of the Warburg effect and colon cancer cell growth[J]. Mol Cell, 2014, 56(3): 400-413.
[18] TIAN Y, XU T, HUANG J, et al. Tissue metabonomic phenotyping for diagnosis and prognosis of human colorectal cancer[J]. Sci Rep, 2016, 6: 20790.
[19] SIMÕES R V, SERGANOVA I S, KRUCHEVSKY N, et al.
Metabolic plasticity of metastatic breast cancer cells: adaptation to changes in the microenvironment[J]. Neoplasia, 2015, 17(8): 671-684.
[20] LU X, BENNET B, MU E, et al. Metabolomic changes accompanying transformation and acquisition of metastatic potential in a syngeneic mouse mammary tumor model[J]. J Biol Chem, 2010, 285(13): 9317-9321.
[21] MEADOWS A L, KONG B, BERDICHEVSKY M, et al. Metabolic and morphological differences between rapidly proliferating cancerous and normal breast epithelial cells[J]. Biotechnol Prog, 2008, 24(2): 334-341.
[22] CHEN E I, HEWEL J, KRUEGER J S, et al. Adaptation of energy metabolism in breast cancer brain metastases[J]. Cancer Res, 2007, 67(4): 1472-1486.
[23] CHEN J L, FAN J, YAN L S, et al. Urine metabolite profiling of human colorectal cancer by capillary electrophoresis mass spectrometry based on MRB[J]. Gastroenterol Res Pract, 2012, 2012: 125890.
[24] MONTROSE D C, ZHOU X K, KOPELOVICH L, et al. Metabolic profiling, a noninvasive approach for the detection of experimental colorectal neoplasia[J]. Cancer Prev Res (Phila), 2012, 5(12): 1358-1367.
[25] SAMANTA D, SEMENZA G L. Metabolic adaptation of cancer and immune cells mediated by hypoxia-inducible factors[J]. Biochim Biophys Acta Rev Cancer, 2018, 1870(1): 15-22.
[26] SINGH B, TAI K, MADAN S, et al. Selection of metastatic breast cancer cells based on adaptability of their metabolic state[J]. PLoS One, 2012, 7(5): e36510.
