Metabolomics for tumor marker discovery and identification based on chromatography-mass spectrometry

Expert Review of Molecular Diagnostics

Volumn 13, Issue 4, 2013, Pages 339-348

  Yin, Peiyuan ^a   Xu, Guowang ^a  

^a DALIAN INSTITUTE OF CHEMICAL PHYSICS   (China)

Author keywords

biomarker; cancer; chromatography; mass spectrometry; metabolomics; translational medicine

Indexed keywords

TUMOR MARKER;

CHEMICAL STRUCTURE; CHROMATOGRAPHY; CLINICAL PROTOCOL; DISEASE MARKER; HUMAN; HYDROPHILIC INTERACTION CHROMATOGRAPHY; LIQUID CHROMATOGRAPHY; LIVER CANCER; LIVER CELL CARCINOMA; MASS SPECTROMETRY; METABOLOMICS; NONHUMAN; OUTCOME ASSESSMENT; REVERSED PHASE LIQUID CHROMATOGRAPHY; REVIEW; SENSITIVITY AND SPECIFICITY;

CHROMATOGRAPHY; EARLY DIAGNOSIS; HUMANS; MASS SPECTROMETRY; METABOLIC NETWORKS AND PATHWAYS; METABOLOMICS; SENSITIVITY AND SPECIFICITY; TUMOR MARKERS, BIOLOGICAL;

EID: 84877306864     PISSN: 14737159     EISSN: 17448352     Source Type: Journal    
DOI: 10.1586/erm.13.23     Document Type: Review

References (107)

1. Issaq HJ, Waybright TJ, Veenstra TD. Cancer biomarker discovery: opportunities and pitfalls in analytical methods. Electrophoresis 32(9), 967-975 (2011).
2. Marincola FM. Translational medicine: a two-way road. J. Transl. Med. 1(1), 1 (2003).
3. Nicholson JK, Lindon JC, Holmes E. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29(11), 1181-1189 (1999).
4. Ramautar R, Somsen GW, de Jong GJ. CE-MS in metabolomics. Electrophoresis 30(1), 276-291 (2009).
5. Han X, Gross RW. Shotgun lipidomics: multidimensional MS analysis of cellular lipidomes. Expert Rev. Proteomics 2(2), 253-264 (2005).
6. Kinross JM, Holmes E, Darzi AW, Nicholson JK. Metabolic phenotyping for monitoring surgical patients. Lancet 377(9780), 1817-1819 (2011).
7. Ganti S, Taylor SL, Abu Aboud O, et al. Kidney tumor biomarkers revealed by simultaneous multiple matrix metabolomics analysis. Cancer Res. 72(14), 3471-3479 (2012).
8. Minami Y, Kasukawa T, Kakazu Y, et al. Measurement of internal body time by blood metabolomics. Proc. Natl Acad. Sci. USA 106(24), 9890-9895 (2009).
9. Dettmer K, Nürnberger N, Kaspar H, Gruber MA, Almstetter MF, Oefner PJ. Metabolite extraction from adherently growing mammalian cells for metabolomics studies: optimization of harvesting and extraction protocols. Anal. Bioanal. Chem. 399(3), 1127-1139 (2011).
10. Rosenling T, Slim CL, Christin C, et al. The effect of preanalytical factors on stability of the proteome and selected metabolites in cerebrospinal fluid (CSF). J. Proteome Res. 8(12), 5511-5522 (2009).
11. Zivkovic AM, Wiest MM, Nguyen UT, Davis R, Watkins SM, German JB. Effects of sample handling and storage on quantitative lipid analysis in human serum. Metabolomics 5(4), 507-516 (2009).
12. Dunn WB, Broadhurst D, Begley P, et al.; Human Serum Metabolome (HUSERMET) Consortium. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 6(7), 1060-1083 (2011).
13. León Z, García-Cañaveras JC, Donato MT, Lahoz A. Mammalian cell metabolomics: experimental design and sample preparation. Electrophoresis doi:10.1002/elps.201200605 (2013) (Epub ahead of print).
14. A J, Trygg J, Gullberg J, et al. Extraction and GC/MS analysis of the human blood plasma metabolome. Anal. Chem. 77(24), 8086-8094 (2005).
15. Matyash V, Liebisch G, Kurzchalia TV, Shevchenko A, Schwudke D. Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics. J. Lipid Res. 49(5), 1137-1146 (2008).
16. Hofmann U, Maier K, Niebel A, Vacun G, Reuss M, Mauch K. Identification of metabolic fluxes in hepatic cells from transient 13C-labeling experiments: Part I. Experimental observations. Biotechnol. Bioeng. 100(2), 344-354 (2008).
17. Lin L, Yu Q, Yan X, et al. Direct infusion mass spectrometry or liquid chromatography mass spectrometry for human metabonomics? A serum metabonomic study of kidney cancer. Analyst 135(11), 2970-2978 (2010).
18. Theodoridis GA, Gika HG, Want EJ, Wilson ID. Liquid chromatography-mass spectrometry based global metabolite profiling: a review. Anal. Chim. Acta 711, 7-16 (2012).
19. Lu X, Cai J, Kong H, et al. Analysis of cigarette smoke condensates by comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry I acidic fraction. Anal. Chem. 75(17), 4441-4451 (2003).
20. Wilson ID, Plumb R, Granger J, Major H, Williams R, Lenz EM. HPLC-MS-based methods for the study of metabonomics. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 817(1), 67-76 (2005).
21. Lu X, Zhao X, Bai C, Zhao C, Lu G, Xu G. LC-MS-based metabonomics analysis. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 866(1-2), 64-76 (2008).
22. Soga T, Ohashi Y, Ueno Y, Naraoka H, Tomita M, Nishioka T. Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. J. Proteome Res. 2(5), 488-494 (2003).
23. Guo K, Li L. Differential 12C-/13Cisotope dansylation labeling and fast liquid chromatography/mass spectrometry for absolute and relative quantification of the metabolome. Anal. Chem. 81(10), 3919-3932 (2009).
24. Guo K, Li L. High-performance isotope labeling for profiling carboxylic acid- containing metabolites in biofluids by mass spectrometry. Anal. Chem. 82(21), 8789-8793 (2010).
25. Dai W, Huang Q, Yin P, et al. Comprehensive and highly sensitive urinary steroid hormone profiling method based on stable isotope-labeling liquid chromatographymass spectrometry. Anal. Chem. 84(23), 10245-10251 (2012).
26. Li Y, Ruan Q, Ye G, Lu X, Lin X, Xu G. A novel approach to transforming a non-targeted metabolic profiling method to a pseudo-targeted method using the retention time locking gas chromatography/mass spectrometry - selected ions monitoring. J. Chromatogr. A. 1255, 228-236 (2012).
27. Trygg J, Holmes E, Lundstedt T. Chemometrics in metabonomics. J. Proteome Res. 6(2), 469-479 (2007).
28. Bijlsma S, Bobeldijk I, Verheij ER, et al. Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation. Anal. Chem. 78(2), 567-574 (2006).
29. Lin X, Zhang Y, Ye G, et al. Classification and differential metabolite discovery of liver diseases based on plasma metabolic profiling and support vector machines. J. Sep. Sci. 34(21), 3029-3036 (2011).
30. Li X, Hansen J, Zhao X, et al. Independent component analysis in non-hypothesis driven metabolomics: improvement of pattern discovery and simplification of biological data interpretation demonstrated with plasma samples of exercising humans. J. Chromatogr. B. Analyt. Technol. Biomed. Life Sci. 910, 156-162 (2012).
31. Xia J, Mandal R, Sinelnikov IV, Broadhurst D, Wishart DS. MetaboAnalyst 2.0 - a comprehensive server for metabolomic data analysis. Nucleic Acids Res. 40(Web Server issue), W127-W133 (2012).
32. Lin XH, Wang QC, Yin PY, et al. A method for handling metabonomics data from liquid chromatography/mass spectrometry: combinational use of support vector machine recursive feature elimination, genetic algorithm and random forest for feature selection. Metabolomics 7(4), 549-558 (2011).
33. Chen J, Zhao X, Fritsche J, et al. Practical approach for the identification and isomer elucidation of biomarkers detected in a metabonomic study for the discovery of individuals at risk for diabetes by integrating the chromatographic and mass spectrometric information. Anal. Chem. 80(4), 1280-1289 (2008).
34. Clements M, Li L. Strategy of using microsome-based metabolite production to facilitate the identification of endogenous metabolites by liquid chromatography mass spectrometry. Anal. Chim. Acta 685(1), 36-44 (2011).
35. Kind T, Fiehn O. Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics 8, 105 (2007).
36. Kind T, Fiehn O. Advances in structure elucidation of small molecules using mass spectrometry. Bioanal. Rev. 2(1-4), 23-60 (2010).
37. Rojas-Chertó M, Kasper PT, Willighagen EL, Vreeken RJ, Hankemeier T, Reijmers TH. Elemental composition determination based on MS(n). Bioinformatics 27(17), 2376-2383 (2011).
38. Chen J, Zhang X, Cao R, et al. Serum 27-nor-5ß-cholestane-3,7,12, 24,25 pentol glucuronide discovered by metabolomics as potential diagnostic biomarker for epithelium ovarian cancer. J. Proteome Res. 10(5), 2625-2632 (2011).
39. Leichtle AB, Nuoffer JM, Ceglarek U, et al. Serum amino acid profiles and their alterations in colorectal cancer. Metabolomics 8(4), 643-653 (2012).
40. Nishiumi S, Kobayashi T, Ikeda A, et al. A novel serum metabolomics-based diagnostic approach for colorectal cancer. PLoS ONE 7(7), e40459 (2012).
41. Ma Y, Zhang P, Wang F, Liu W, Yang J, Qin H. An integrated proteomics and metabolomics approach for defining oncofetal biomarkers in the colorectal cancer. Ann. Surg. 255(4), 720-730 (2012).
42. Mal M, Koh PK, Cheah PY, Chan EC. Metabotyping of human colorectal cancer using two-dimensional gas chromatography mass spectrometry. Anal. Bioanal. Chem. 403(2), 483-493 (2012).
43. Cheng Y, Xie G, Chen T, et al. Distinct urinary metabolic profile of human colorectal cancer. J. Proteome Res. 11(2), 1354-1363 (2012).
44. Ritchie SA, Ahiahonu PW, Jayasinghe D, et al. Reduced levels of hydroxylated, polyunsaturated ultra long-chain fatty acids in the serum of colorectal cancer patients: implications for early screening and detection. BMC Med. 8, 13 (2010).
45. Ma YL, Qin HL, Liu WJ, et al. Ultra-high performance liquid chromatography-mass spectrometry for the metabolomic analysis of urine in colorectal cancer. Dig. Dis. Sci. 54(12), 2655-2662 (2009).
46. Denkert C, Budczies J, Weichert W, et al. Metabolite profiling of human colon carcinoma - deregulation of TCA cycle and amino acid turnover. Mol. Cancer 7, 72 (2008).
47. Wedge DC, Allwood JW, Dunn W, et al. Is serum or plasma more appropriate for intersubject comparisons in metabolomic studies? An assessment in patients with small-cell lung cancer. Anal. Chem. 83(17), 6689-6697 (2011).
48. Hori S, Nishiumi S, Kobayashi K, et al. A metabolomic approach to lung cancer. Lung Cancer 74(2), 284-292 (2011).
49. An Z, Chen Y, Zhang R, et al. Integrated ionization approach for RRLC-MS/MS-based metabonomics: finding potential biomarkers for lung cancer. J. Proteome Res. 9(8), 4071-4081 (2010).
50. Yang Q, Shi X, Wang Y, et al. Urinary metabonomic study of lung cancer by a fully automatic hyphenated hydrophilic interaction/RPLC-MS system. J. Sep. Sci. 33(10), 1495-1503 (2010).
51. Maeda J, Higashiyama M, Imaizumi A, et al. Possibility of multivariate function composed of plasma amino acid profiles as a novel screening index for non-small cell lung cancer: a case control study. BMC Cancer 10, 690 (2010).
52. Hanai Y, Shimono K, Matsumura K, et al. Urinary volatile compounds as biomarkers for lung cancer. Biosci. Biotechnol. Biochem. 76(4), 679-684 (2012).
53. Matsumura K, Opiekun M, Oka H, et al. Urinary volatile compounds as biomarkers for lung cancer: a proof of principle study using odor signatures in mouse models of lung cancer. PLoS ONE 5(1), e8819 (2010).
54. Yao Z, Yin P, Su D, et al. Serum metabolic profiling and features of papillary thyroid carcinoma and nodular goiter. Mol. Biosyst. 7(9), 2608-2614 (2011).
55. Wei J, Xie G, Zhou Z, et al. Salivary metabolite signatures of oral cancer and leukoplakia. Int. J. Cancer 129(9), 2207-2217 (2011).
56. Sugimoto M, Wong DT, Hirayama A, Soga T, Tomita M. Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. Metabolomics 6(1), 78-95 (2010).
57. Urayama S, Zou W, Brooks K, Tolstikov V. Comprehensive mass spectrometry based metabolic profiling of blood plasma reveals potent discriminatory classifiers of pancreatic cancer. Rapid Commun. Mass Spectrom. 24(5), 613-620 (2010).
58. Yin P, Wan D, Zhao C, et al. A metabonomic study of hepatitis B-induced liver cirrhosis and hepatocellular carcinoma by using RP-LC and HILIC coupled with mass spectrometry. Mol. Biosyst. 5(8), 868-876 (2009).
59. Tan Y, Yin P, Tang L, et al. Metabolomics study of stepwise hepatocarcinogenesis from the model rats to patients: potential biomarkers effective for small hepatocellular carcinoma diagnosis. Mol. Cell Proteomics 11(2), M111.010694 (2012).
60. Chen T, Xie G, Wang X, et al. Serum and urine metabolite profiling reveals potential biomarkers of human hepatocellular carcinoma. Mol. Cell Proteomics 10(7), M110.004945 (2011).
61. Patterson AD, Maurhofer O, Beyoglu D, et al. Aberrant lipid metabolism in hepatocellular carcinoma revealed by plasma metabolomics and lipid profiling. Cancer Res. 71(21), 6590-6600 (2011).
62. Xiao JF, Varghese RS, Zhou B, et al. LC-MS based serum metabolomics for identification of hepatocellular carcinoma biomarkers in Egyptian cohort. J. Proteome Res. 11(12), 5914-5923 (2012).
63. Ressom HW, Xiao JF, Tuli L, et al. Utilization of metabolomics to identify serum biomarkers for hepatocellular carcinoma in patients with liver cirrhosis. Anal. Chim. Acta 743, 90-100 (2012).
64. El-Mir MY, Badia MD, Luengo N, Monte MJ, Marin JJ. Increased levels of typically fetal bile acid species in patients with hepatocellular carcinoma. Clin. Sci. 100(5), 499-508 (2001).
65. Zhou L, Wang Q, Yin P, et al. Serum metabolomics reveals the deregulation of fatty acids metabolism in hepatocellular carcinoma and chronic liver diseases. Anal. Bioanal. Chem. 403(1), 203-213 (2012).
66. Li S, Liu H, Jin Y, Lin S, Cai Z, Jiang Y. Metabolomics study of alcohol-induced liver injury and hepatocellular carcinoma xenografts in mice. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 879(24), 2369-2375 (2011).
67. Soga TSM, Honma M, Mori M, et al. Serum metabolomics reveals γ-glutamyl dipeptides as biomarkers for discrimination among different forms of liver disease. J. Hepatol. 55(4), 896-905 (2011).
68. Chen J, Zhou L, Zhang X, et al. Urinary hydrophilic and hydrophobic metabolic profiling based on liquid chromatographymass spectrometry methods: differential metabolite discovery specific to ovarian cancer. Electrophoresis 33(22), 3361-3369 (2012).
69. Zhang T, Wu X, Ke C, et al. Identification of potential biomarkers for ovarian cancer by urinary metabolomic profiling. J. Proteome. Res. 12(1), 505-512 (2012).
70. Zhang T, Wu XY, Yin MZ, et al. Discrimination between malignant and benign ovarian tumors by plasma metabolomic profiling using ultra performance liquid chromatography/mass spectrometry. Clin. Chim. Acta. 413(9-10), 861-868 (2012).
71. Woo HM, Kim KM, Choi MH, et al. Mass spectrometry based metabolomic approaches in urinary biomarker study of women's cancers. Clin. Chim. Acta 400(1-2), 63-69 (2009).
72. Chen Y, Zhang R, Song Y, et al. RRLCMS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network: finding potential biomarkers for breast cancer. Analyst 134(10), 2003-2011 (2009).
73. Budczies J, Denkert C, Müller BM, et al. Remodeling of central metabolism in invasive breast cancer compared to normal breast tissue: a GC-TOFMS based metabolomics study. BMC Genomics 13
74. Nam H, Chung BC, Kim Y, Lee K, Lee D. Combining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification. Bioinformatics 25(23), 3151-3157 (2009).
75. Frickenschmidt A, Frohlich H, Bullinger D, et al. Metabonomics in cancer diagnosis: mass spectrometry-based profiling of urinary nucleosides from breast cancer patients. Biomarkers 13(4), 435-449 (2008).
76. Kim Y, Koo I, Jung BH, Chung BC, Lee D. Multivariate classification of urine metabolome profiles for breast cancer diagnosis. BMC Bioinformatics 11(Suppl. 2), S4 (2010).
77. Sreekumar A, Poisson LM, Rajendiran TM, et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457(7231), 910-914 (2009).
78. Lucarelli G, Fanelli M, Larocca AM, et al. Serum sarcosine increases the accuracy of prostate cancer detection in patients with total serum PSA less than 4.0 ng/ml. Prostate 72(15), 1611-1621 (2012).
79. Jentzmik F, Stephan C, Miller K, et al. Sarcosine in urine after digital rectal examination fails as a marker in prostate cancer detection and identification of aggressive tumours. Eur. Urol. 58(1), 12-18; discussion 20 (2010).
80. Miyagi Y, Higashiyama M, Gochi A, et al. Plasma free amino acid profiling of five types of cancer patients and its application for early detection. PLoS ONE 6(9), e24143 (2011).
81. Kosti O, Xu X, Veenstra TD, et al. Urinary estrogen metabolites and prostate cancer risk: a pilot study. Prostate 71(5), 507-516 (2011).
82. Yang L, Gaikwad NW, Meza J, et al. Novel biomarkers for risk of prostate cancer: results from a case-control study. Prostate 69(1), 41-48 (2009).
83. Prehn C, Möller G, Adamski J. Recent advances in 17-hydroxysteroid dehydrogenases. J. Steroid Biochem. Mol. Biol. 114(1-2), 72-77 (2009).
84. Clyne M. Prostate cancer: the androgen receptor-a novel target for vaccines. Nat. Rev. Urol. 9(12), 671 (2012).
85. Weiss RH, Kim K. Metabolomics in the study of kidney diseases. Nat. Rev. Nephrol. 8(1), 22-33 (2012).
86. Kim K, Aronov P, Zakharkin SO, et al. Urine metabolomics analysis for kidney cancer detection and biomarker discovery. Mol. Cell Proteomics 8(3), 558-570 (2009).
87. Kim K, Taylor SL, Ganti S, Guo L, Osier MV, Weiss RH. Urine metabolomic analysis identifies potential biomarkers and pathogenic pathways in kidney cancer. OMICS 15(5), 293-303 (2011).
88. Ganti S, Taylor SL, Kim K, et al. Urinary acylcarnitines are altered in human kidney cancer. Int. J. Cancer 130(12), 2791-2800 (2012).
89. Huang Z, Lin L, Gao Y, et al. Bladder cancer determination via two urinary metabolites: a biomarker pattern approach. Mol. Cell Proteomics 10(10), M111.007922 (2011).
90. Issaq HJ, Nativ O, Waybright T, et al. Detection of bladder cancer in human urine by metabolomic profiling using high performance liquid chromatography/mass spectrometry. J. Urol. 179(6), 2422-2426 (2008).
91. Pasikanti KK, Esuvaranathan K, Ho PC, et al. Noninvasive urinary metabonomic diagnosis of human bladder cancer. J. Proteome Res. 9(6), 2988-2995 (2010).
92. Putluri N, Shojaie A, Vasu VT, et al. Metabolomic profiling reveals potential markers and bioprocesses altered in bladder cancer progression. Cancer Res. 71(24), 7376-7386 (2011).
93. Chan EC, Koh PK, Mal M, et al. Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). J. Proteome Res. 8(1), 352-361 (2009).
94. Miura D, Fujimura Y, Yamato M, et al. Ultrahighly sensitive in situ metabolomic imaging for visualizing spatiotemporal metabolic behaviors. Anal. Chem. 82(23), 9789-9796 (2010).
95. Kinross JM, Alkhamesi N, Barton RH, et al. Global metabolic phenotyping in an experimental laparotomy model of surgical trauma. J. Proteome Res. 10(1), 277-287 (2011).
96. Ye G, Zhu B, Yao Z, et al. Analysis of urinary metabolic signatures of early hepatocellular carcinoma recurrence after surgical removal using gas chromatography- mass spectrometry. J. Proteome Res. 11(8), 4361-4372 (2012).
97. Clayton TA, Lindon JC, Cloarec O, et al. Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature 440(7087), 1073-1077 (2006).
98. Wei J, Xie G, Ge S, et al. Metabolic transformation of DMBA-induced carcinogenesis and inhibitory effect of salvianolic acid b and breviscapine treatment. J. Proteome Res. 11(2), 1302-1316 (2012).
99. Jones RL, Smith IE. Neoadjuvant treatment for early-stage breast cancer: opportunities to assess tumour response. Lancet Oncol. 7(10), 869-874 (2006).
100. Wei S, Liu L, Zhang J, et al. Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer. Mol. Oncol. doi:10.1016/j. molonc.2012.10.003 (2012) (Epub ahead of print).
101. Wang B, Chen D, Chen Y, et al. Metabonomic profiles discriminate hepatocellular carcinoma from liver cirrhosis by ultraperformance liquid chromatographymass spectrometry. J. Proteome Res. 11(2), 1217-1227 (2012).
102. Chen F, Xue J, Zhou L, Wu S, Chen Z. Identification of serum biomarkers of hepatocarcinoma through liquid chromatography/mass spectrometry-based metabonomic method. Anal. Bioanal. Chem. 401(6), 1899-1904 (2011).
103. Kang X, Sun L, Guo K, et al. Serum protein biomarkers screening in HCC patients with liver cirrhosis by ICAT-LC-MS/MS. J. Cancer Res. Clin. Oncol. 136(8), 1151-1159 (2010).
104. Becker S, Kortz L, Helmschrodt C, Thiery J, Ceglarek U. LC-MS-based metabolomics in the clinical laboratory. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 883-884, 68-75 (2012).
105. Gatenby RA, Gillies RJ. Why do cancers have high aerobic glycolysis? Nat. Rev. Cancer 4(11), 891-899 (2004).
106. METLIN. http://metlin.scripps.edu
107. Human Metabolome Database. www.hmdb.ca