摘要
本论文采用快速高分辨液相色谱(RRLC)与正、负离子检测模式的电喷雾电离质谱(ESI-MS)的联用技术,针对血浆、尿液两种不同的体液基质,探索并建立了基于RRLC-MS/MS技术的整体轮廓与靶向代谢组学相结合的分析方法,通过这两种方法的优势互补,以及两种基质样本所含代谢物信息的互补,系统开展了食管癌的代谢组学研究,以寻找在疾病状态及治疗干预下食管癌患者体内发生显著性变化的小分子代谢物,即潜在的诊断及治疗标志物,并结合近期疗效评价结果,寻找可用于疗效评价的潜在标志物。
为了从获得的海量代谢组学数据中挖掘出可靠的差异代谢物,本研究在模式识别中引入组外验证的方法,对建立的统计模型进行了评价,并通过S-plot图,VIP list, Jack-knifed置信区间图,代谢轮廓图,独立样本t-test, CAMERA分析,pearson协相关分析,以及XICs分析等步骤,对潜在生物标志物进行了细致的筛选。由于疾病会引起机体一系列复杂的生化代谢紊乱,因此本研究中融入ROC曲线分析,分别对潜在的诊断标志物进行了表征,以筛选出诊断特异性及灵敏度较高的潜在标志物组。进一步通过高分辨MS谱及MS/MS谱分析获取精确质量数,并结合代谢物的质谱裂解规律,同位素丰度比法,数据库检索,以及标准品比较分析等不同手段与步骤,分析鉴定出一些潜在标志物的结构,在上述研究的基础上,对发现的潜在标志物的生物学意义进行了探讨,结合体内代谢途径,对食管癌患者机体中发生紊乱的代谢途径进行了初步分析,本研究结果有望为食管癌的临床早期诊断、预后、疗效评价及个体化治疗提供分子基础及重要依据。
首先,为了建立适用于血浆整体轮廓代谢组学的RRLC-MS分析方法,本研究对血浆样品前处理、液相色谱和质谱条件进行了细致的考察及优化,分析确定了采用三倍体积的冷藏乙腈沉淀蛋白法；进一步通过选择血浆中10种代表性化合物进行了方法学考察,结果表明该方法稳定、可靠,适用于后续的代谢组学研究。采用建立的RRLC-MS分析方法,对食管癌患者(包括治疗前、中、后)与健康对照组的血浆样本开展了整体轮廓代谢组学分析,通过分别对食管癌患者治疗前与健康者、患者治疗前与治疗后等不同组别进行系统、全面的多变量统计分析。通过采用建立的数据处理方法,在食管癌患者与健康者、食管癌患者治疗前与治疗后血浆中分别发现了52、48个有显著性差异的代谢物,即潜在的诊断、治疗标志物。结合疗效评价结果对潜在治疗标志物进行了治疗过程中的动态变化进分析,结果筛选出5个与食管癌疗效密切相关的潜在生物标志物。
此外,为了从食管癌尿液样本中寻找潜在生物标志物,本研究考察了尿液前处理方法,确定采用肌酐值校准稀释倍数法进行样品前处理,并建立了尿液整体轮廓代谢组学RRLC-MS分析方法。采用该方法开展了食管癌患者与健康者、患者治疗前与治疗后的尿液样本的系统分析。通过对差异变量的细致筛选,在食管癌患者与健康者、食管癌患者治疗前后尿液中分别发现了83个和43个代谢物的含量有显著性差异,并结合疗效评价结果对潜在治疗标志物进行分析,结果发现1个与食管癌疗效密切相关的潜在标志物。此外,结合疾病分期,发现有5个潜在诊断标志物在不同分期尿液样本中有着明显的统计学差异。
为了验证发现的潜在标志物的可靠性,本研究针对血浆、尿液两种体液基质建立了基于LC-MS/MS技术的MRM靶向代谢组学分析方法。运用该方法针对前期发现的潜在标志物的独立样本进行了靶向验证,结果在血浆中分别发现35个和20个更加可靠的潜在诊断标志物和治疗标志物,以及与疗效评价密切相关的3个标志物；另外从尿液中分别筛选出49个和26个更加可靠的潜在诊断标志物和治疗标志物,以及与疗效评价相关的1个标志物。
进一步对上述发现的潜在诊断及治疗标志物进行了结构鉴定。目前分析鉴定出28个潜在诊断标志物的结构,包括血浆中发现的18个代谢物,尿液中发现的15个代谢物,其中5个为两种样本共同发现的代谢物；另鉴定出16个潜在治疗标志物的结构,包括血浆中发现的11个代谢物,尿液中发现的7个代谢物,其中2个为两种样本共同发现的代谢物；并鉴定出4个与疗效评价相关标志物的结构：其中血浆中发现3个代谢物,尿液中发现1个代谢物。此外,通过对血浆、尿液中潜在诊断标志物进行ROC曲线分析,筛选出2个诊断特异性及灵敏度较高的潜在标志物组。
根据上述研究结果,结合体内代谢途径分析,发现食管癌患者体内的嘌呤代谢、TCA循环、β-氧化、糖酵解等多条代谢途径发生紊乱,为深入了解食管癌的发生、发展提供了重要依据。
A global and targeted metabolomics study, using plasma and urine from esophageal carcinoma (ESCC) patients before and after chemoradiotherapy (CRT) and healthy controls, was originally carried out by RRLC-(±)ESI-MS/MS to determine global alterations in the metabolic profiles and find biomarkers potentially applicable to diagnosis and monitoring treatment effects.
To explore more reliable potential biomarker candidates, an independent test set model was applied and the predictive ability of the established model was evaluated. Discriminating variables were selected according to variable importance in projection values (VIP), S-plot, jack-knifed-based confidence intervals, raw data plot, an independent t-test, R-package CAMERA, partial correlation analysis, and extracted ion chromatograms (XICs). Due to the cancers involve systematic deregulation of biochemical pathways, the single biomarker would be limited utilization for diagnosis. ROC analysis was conducted on single potential biomarkers and used for discovering more reliable biomarker panels. The exact mass, isotope pattern, the free databases search, high resolution MS/MS analyses were performed for the identification of the metabolites of interest. Together, biological significance of these metabolites was discussed. Understanding the biological significance of these potential biomarkers could provide further insight into the mechanisms underlying the pathophysiology of ESCC and treatment intervention, and facilitate the discovery of biomarkers for diagnosis, treatment monitoring, and response prediction in ESCC.
For establishing the RRLC-MS method of the plasma metabolomics, the study was undertaken by comparing the different plasma preparation methods and the analytical condition in detail. The results indicated3-fold cold acetonitrile was optimal. Eventually, the method was evaluated by using ten representative compounds common in plasma. The results showed that the method was satisfactory and would be applied in plasma metabolomics. Then, a global metabolomics study using plasma from four groups including ESCC patients before, during and after CRT and healthy controls, was originally carried out by RRLC-MS to find biomarkers potentially applicable to diagnosis and monitoring treatment effects.52and48metabolites were found to be significantly altered in ESCC patients vs. healthy controls and in pre-vs. post-treatment patients based on the eatablished multivariate statistical data analysis (MVDA), respectively. Combined with the treatment effect,5metabolites were found to be closely related with the therapeutic response.
In additon, to explore the potential biomarkers in urine samples, the sample preparation method was investigated. Creatinine-normalized dilution method was applied. Then, a global metabolomics using urine from ESCC patients (before and after CRT treatment) and healthy controls, was originally carried out by RRLC-MS.83and43metabolites were found to be significantly altered in ESCC patients vs. healthy controls and in pre-vs. post-treatment patients, respectively. Combined with the treatment effect, I metabolites were found to be closely related with the therapeutic response. Furthermore,5metabolites were picked out to be related with the disease staging.
To further validate the reliability of the potential biomarkers, independent validation tests based on RRLC-MS/MS targeted metabolomics were performed for both urine and plasma samples. The resulted35and20metabolites were picked out after the validation test in comparision of ESCC patients vs. healthy controls and pre-vs. post-treatment patients in plasma.3metabolites were closely related with the treatment response. The resulted49and26metabolites were picked out after the validation test in comparision of ESCC patients vs. healthy controls and in pre-vs. post-treatment patients in urine.
Finally,28metabolites were identified as diagnostic biomarkers, including18metabolites in plasma,15metabolites in urine,5of which were identified in both plasma and urine samples.16metabolites were identified as therapeutic biomarkers, including I1metabolites in plasma,7metabolites in urine,2of which were identified in both plasma and urine samples. In additon,4metabolites were identified as closely related with the treatment reaponse. Furthermore, according to the ROC analysis, two biomarker panels were generated with the high sensitivity and specificity for ESCC diagnosis.
Together, the biological significance of these metabolites was discussed. Abnormal levels of these metabolites indicate that the purine metabolism, TCA cycle and β-oxidation, glycolysis metabolism are disturbed in ESCC patients. The result would be useful for the insight into occurrence and development of ESCC disease.
引文
[1]Collins, F. S., Green, E. D., Guttmacher, A. E., et al. A vision for the future of genomics research[J]. Nature,2003,422(6934):835-847.
[2]John, R. Metabolomics-an important emerging science. Business briefing:pharmatech[J]. Drug Discov Today,2004,1:51-54.
[3]Schmidt, C. W. Metabolomics:what's happening downstream of DNA[J]. Environ Health Perspect,2004,112(7):A410.
[4]Nicholson, J. K., Lindon, J. C., Holmes, E.'Metabonomics':understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data[J]. Xenobiotica,1999,29:1181-1189.
[5]Fiehn, O., Kopka, J., Dormann, P., et al. Metabohte profiling for plant functional genomicsfJ]. Nat Biotechnol,2000,18(11):1157-1161.
[6]Fiehn, O. Combining genomics, metabolome analysis, and biochemical modeling to understand metabolic networks[J]. Comp Funct Genomics,2001,2:155-168.
[7]Dettmer, K., Aronov, P. A., Hammock, B. D. Mass spectrometry-based metabolomics[J]. Mass Spectrom Rev,2007,26(1):51-78.
[8]杨军,宋硕林,Jose, C. P.,等.代谢组学及其应用[J].生物工程学报,2005,21(1)：1-5.
[9]Guo, B., Chen, B., Liu, A. M., et al. Liquid chromatography-mass spectrometric multiple reaction monitoring-based strategies for expanding targeted profiling towards quantitative metabolomics[J]. Current Drug Metabolism,2012,13(9):1226-1243.
[10]Taylor, J., King, R. D., Ahmann, T., et al. Application of metabolomics to plant genotype discrimination using statistics and machine learning[J]. Bioinformatics,2002,18(Suppl 2): S241-8.
[11]吴昊,薛如意,沈锡中.代谢组学在肿瘤诊断应用中的新进展[J].复旦学报,2008,3(4)：629-631.
[12]Griffiths, W. J., Therese, K., Wang, Y. Q., et al. Targeted metabolomics for biomarker discovery[J]. Angew Chem Int Ed,2010,49:5426-5445.
[13]Tang, H. R., Wang, Y. L. Metabolomics-a revolution in progress[J]. Progress in Biochemistry and Biophysics,2006,33:401-417.
[14]Holmes, E., Tang, H.R., Wang, Y. L., et al. The assessment of plant metabolite profiles by NMR-based methodologies[J]. Plant Med,2006,72(9):771-785.
[15]Plumb, R., Granger, J., Stumpf, C., et al. Metabonomic analysis of mouse urine by liquid-chromatography-time of flight mass spectrometry (LC-TOFMS):detection of strain, diurnal and gender differences[J]. Analyst,2003,128:819-823.
[16]Lenz, E. M., Bright, J., Knight, R., et al. A metabonomic investigation of the biochemical effects of mercuric chloride in the rat using 1H NMR and HPLC-TOF/MS:time dependent changes in the urinary profile of endogenous metabolites as a result of nephrotoxicity[J]. Analyst,2004,129: 535-541.
[17]Williams, R. E., Major, H., Lock, E. A., et al. D-Serine-induced nephrotoxicity:a HPLC-TOF/MS-based metabonomics approach[J]. Toxicology,2005,207:179-190.
[18]Boernsen, K. O., Gatzek, S., Imbert, G. Controlled protein precipitation in combination with chip-based nanospray infusion mass spectrometry. An approach for metabolomics profiling of plasma[J]. Anal Chem,2005,77(22):7255-7264.
[19]Plumb, R. S., Johnson, K. A., Rainville, P., et al. The detection of phenotypic differences in the metabolic plasma profile of three strains of Zucker rats at 20 weeks of age using ultra-performance liquid chromatography/orthogonal acceleration time-of-flight mass spectrometry[J]. Rapid Commun Mass Spectrom,2006,20(19):2800-2806.
[20]Want, E. J., O'Maille, G., Smith, C. A., et al. Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry[J]. Anal Chem, 2006,78(3):743-752.
[21]Yin, P., Zhao, X., Li, Q., et al. Metabonomics study of intestinal fistulas based on ultraperformance liquid chromatography coupled with Q-TOF mass spectrometry (UPLC/Q-TOFMS)[J]. J Proteome Res,2006,5(9):2135-2143.
[22]Jennifer, M. J., Fredrick, H. S., Matthew, R., et al. A rapid LC-FTMS method for the analysis of cysteine, cystine and cysteine/cystine steady-state redox potential in human plasma[J]. Clin Chim Acta,2008,396(1-2):43-48.
[23]Michopoulos, F., Lai, L., Gika, H., et al. UPLC-MS-based analysis of human plasma for metabonomics using solvent precipitation or solid phase extraction[J]. J Proteome Res,2009, 8(4):2114-2121.
[24]Idborg, H., Zamani, L., Edlund, P. O., et al. Metabolic fingerprinting of rat urine by LC/MS Part 2. Data pretreatment methods for handling of complex data[J]. J Chromatogr B,2005,828(1-2): 14-20.
[25]Idborg, H., Zamani, L., Edlund, P.O., et al. Metabolic fingerprinting of rat urine by LC/MS Part 1. Analysis by hydrophilic interaction liquid chromatography-electrospray ionization mass spectrometry[J]. J Chromatogr B,2005,828(1-2):9-13.
[26]Fiehn, O., Kopka, J., Trethewey, R. N., et al. Identification of uncommon plant metabolites basedon calculation of elemental compositions using gas chromatography and quadrupole mass spectrometry[J]. Anal Chem,2000,72(15):3573-3580.
[27]Colebatch, G., Desbrosses, G., Ott, T., et al. Global changes in transcription orchestrate metabolic differentiation during symbiotic nitrogen fixation in Lotus japonicus[J]. Plant J,2004, 39(4):487-512.
[28]Villas-Boas, S. G., Hojer-Pedersen, J., Akesson, M., et al. Global metabolite analysis of yeast: evaluation of sample preparation methods[J]. Yeast (Chichester, England),2005,22(14): 1155-1169.
[29]Xu, J., Tian, Y. P., Chen, Y. H., et al. Plasma preparation method for metabolomic analysisbased on rapid resolution liquid chromatography-mass spectrometry[J]. Chin J Anal Chem,2011, 39(12):1793-1797.
[30]Yang, J., Xu, G., Zheng, Y., et al. Diagnosis of liver cancer using HPLC-based metabonomics avoiding false-positive result from hepatitis and hepatocirrhosis diseases[J]. J Chromatogr B, 2004,813(1-2):59-65.
[31]Harrigan, G. G., LaPlante, R. H., Cosma, G. N., et al. Application of high-throughput Fourier-transform infrared spectroscopy in toxicology studies:contribution to a study on the development of an animal model for idiosyncratic toxicity[J]. Toxicol Lett,2004,146(3): 197-205.
[32]Gao, P., Lu, C., Zhang, F., et al. Integrated GC-MS and LC-MS plasma metabonomics analysis ofankylosing spondylitis[J]. Analyst,2008,133(9):1214-1220.
[33]Roessner, U., Wagner, C., Kopka, J., et al. Technical advance:simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry[J]. Plant J,2000,23(1): 131-142.
[34]Saito, N., Robert, M., Kitamura, S., et al. Metabolomics approach for enzyme discovery[J]. J Proteome Res,2006,5(8):1979-1987.
[35]Nordstrom, A., O'Maille, G., Qin, C., et al. Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics:quantitative analysis of endogenous and exogenous metabolites in human serum[J]. Anal Chem,2006,78(10):3289-3295.
[36]Plumb, R. S., Granger, J. H., Stumpf, C. L., et al. A rapid screening approach to metabonomics using UPLC and Q-TOF mass spectrometry:application to age, gender and diurnal variation in normal/Zucker obese rats and black, white and nude mice[J]. Analyst,2005,130(6):844-849.
[37]Wilson, I. D., Nicholson, J. K., Castro-Perez, J., et al. High resolution "ultra performance" liquid chromatography coupled to Q-TOF mass spectrometry as a tool for differential metabolic pathway profiling in functional genomic studies[J]. J Proteome Res,2005,4:591-598.
[38]Yoshida, T., Ronald, E., Kumagai, H. High-speed analyses using rapid resolution liquid chromatography on ZORBAX column packed 1.8μm particles[J]. Chromatography,2007,28(2): 81-87.
[39]Chen, Y. H., Zhang, R. P., Song, Y. M., et al. RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network:finding potential biomarkers for breast cancer[J]. Analyst,2009,134:2003-2011.
[40]An, Z. L., Chen, Y. H., Zhang, R. P., et al. Integrated ionization approach for RRLC-MS/MS-based metabonomics:finding potential biomarkers for lung cancer[J]. J Proteome Res,9:4071-4081.
[41]Yin, P., Zhao, X., Li, Q., et al. Metabonomics study of intestinal fistulas based on ultraperformance liquid chromatography coupled with Q-TOF mass spectrometry (UPLC/Q-TOF MS)[J]. J Proteome Res,2006,5(9):2135-2143.
[42]Chen, T. L., Xie, G. X., Wang, X. Y., et al. Serum and urine metabolite profiling reveals potential biomarkers of human hepatocellular carcinoma[J]. Mol Cell Proteomics,10(7): M110.004945.
[43]Weston, D. J. Ambient ionization mass spectrometry:current understanding of mechanistic theory; analytical performance and application areas[J]. Analyst,2010,135(4):661-668.
[44]Jackson, A. U., Werner, S. R., Talaty, N., et al. Targeted metabolomic analysis of Escherichia coli by desorption electrospray ionization and extractive electrospray ionization mass spectrometry[J]. Anal Biochem,2008,375(2):272-281.
[45]Cooks, R. G., Ouyang, Z., Takats, Z. et al. Detection technologies. Ambient mass spectrometry[J]. Science,311(5767):1566-1570.
[46]Nordstrom, A., Want, E., Northen, T., et al. Multiple ionization mass spectrometry strategy used to reveal the complexity of metabolomics[J]. Anal Chem,2008,80(2):421-429.
[47]Bullinger, D., Fux, R., Nicholson, G., et al. Identification of urinary modified nucleosides and ribosylated metabolites in humans via combined ESI-FTICR MS and ESI-IT MS analysis[J]. J Am Soc Mass Spectrom,2008,19(10):1500-1513.
[48]Fardet, A., Llorach, R., Martin, J. F., et al. A liquid chromatography-quadrupole time-of-flight (LC-QTOF)-based metabolomic approach reveals new metabolic effects of catechin in rats fed high-fat diets[J]. J Proteome Res,2008,7(6):2388-2398.
[49]De Vos, R. C., Moco, S., Lommen, A., et al. Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry[J]. Nat Protoc,2007,2(4):778-791.
[50]Williams, R. E., Major, H., Lock, E. A., et al. D-Serine-induced nephrotoxicity:a HPLC-TOF/MS-based metabonomics approach[J]. Toxicology,2005,207(2):179-190.
[51]Schmerler, D., Neugebauer, S., Ludewig, K., et al. Targeted metabolomics for discrimination of systemic inflammatory disorders in critically ill patients[J]. J Lipid Res,53(7):1369-1375.
[52]Stulten, D., Lamshoft, M., Zuhlke, S., et al. Isolation and characterization of a new human urinary metabolite of diclofenac applying LC-NMR-MS and high-resolution mass analyses[J]. J Pharm Biomed Anal,2008,47(2):371-376.
[53]Spraul, M., Freund, A. S., Nast, R. E., et al. Advancing NMR sensitivity for LC-NMR-MS using a cryoflow probe:application to the analysis of acetaminophen metabolites in urine[J]. Anal Chem,2003,75(6):1536-1541.
[54]卢红梅,梁逸曾.代谢组学分析技术及数据处理技术[J].分析测试学报,2008,27(3)：325-332.
[55]许国旺.代谢组学——方法与应用[M].北京：科学出版社,2008：142.
[56]Issaq, H. J., Nativ, O., Waybright, T., et al. Detection of bladder cancer in human urine by metabolomic profiling using high performance liquid chromatography/mass spectrometry[J]. J Urol,2008,179(6):2422-2426.
[57]Holmes, E., Antti, H. Chemometric contributions to the evolution of metabonomics: mathematical solutions to characterising and interpreting complex biological NMR spectrafJ]. Analyst,2002,127(12):1549-1557.
[58]Keun, H. C., Ebbels, T. M., Bollard, M. E., et al. Geometric trajectory analysis of metabolic responses to toxicity can define treatment specific profiles[J]. Chem Res Toxicol,2004, 17(5):579-587.
[59]Ciosek, P., Brzozka, Z., Wroblewski, W., et al. Direct and two-stage data analysis procedures based on PCA, PLS-DA and ANN for ISE-based electronic tongue-Effect of supervised feature extraction[J]. Talanta,2005,67,590-596.
[60]Wiklund, S., Johansson, E., Sjostrom, L., et al. Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models[J]. Anal Chem,2008,80:115-122.
[61]Numata, J., Ebenhoh,O., Knapp, E. W. Measuring correlations in metabolomic networks with mutual information[J]. Genome Inform,2008,20,112-122.
[62]Taylor, J., King, R.D., Altmann, T., et al. Application of metabolomics to plant genotype discrimination using statistics and machine learning[J]. Bioinformatics,2002,18 (Suppl 2): S241-8.
[63]Kind, T., Fiehn, O. Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry[J]. BMC Bioinformatics,2007,8:105.
[64]Hanahan, D., Weinberg, R. A. The hallmarks of cancer[J]. Cell,2000,100:57-70.
[65]Danial, N.N., Gramm, C.F., Scorrano, L., et al. BAD and glucokinase reside in a mitochondrial complex that integrates glycolysis and apoptosis[J]. Nature,2003,424:952-956.
[66]Cairns, R., Papandreou, I., Denko, N. Overcoming Physiologic barriers to cancer treatment by molecularly targeting the tumor microenvironment[J]. Mol Cancer Res,2006,4:61-70.
[67]Sutherland, R. M. Cell and environment interactions in tumor microregions:the multicell spheroid model[J]. Science,1988,240:177-184.
[68]Liotta, L. A., Kohn, E. C. The microenvironment of the tumour-host interface[J]. Nature,2001, 411:375-379.
[69]Warburg, O. On the origin of cancer cells[J]. Science,1956,123:309-314.
[70]Costello, L. C., Franklin, R. B. Why do tumour cells glycolyse? From glycolysis through citrate to lipogenesis[J]. Mol Cell Biochem,2005,280:1-8.
[71]Florian, C. L., Preece, N. E., Bhakoo, K. K., et al. Characteristic metabolic profiles revealed by 'H-NMR spectroscopy for three types of human brain and nervous system tumours[J]. NMR Biomed,1995,8:253-264.
[72]马延磊,秦环龙.代谢组学及其在肿瘤生物学研究中的应用进展[J].世界华人消化杂志,2008,16(34)：3877-388.
[73]Sreekumar, A., Poisson, L. M., Rajendiran, T. M., et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression[J]. Nature,2009,457:910-915.
[74]Kim, Y. S., Maruvada, P., Milner, J. A. Metabolomics in biomarker discovery:future uses for cancer prevention[J]. Future Oncol,2008,4:93-102.
[75]Nishiumi, S., Shinohara, M., Ikeda, A., et al. Serum metabolomics as a novel diagnostic approach for pancreatic cancer[J]. Metabolomics,2010,6:518-528.
[76]Ressom, H. W., Xiao, J. F., Tuli, L. et al. Utilization of metabolomics to identify serum biomarkers for hepatocellular carcinoma in patients with liver cirrhosis[J]. Anal Chim Acta, 2012,743:90-100.
[77]Huang, Z. Z., Lin, L., Yao, G., et al. Bladder cancer determination via two urinary metabolites:a biomarker pattern approach[J]. Mol Cell Proteomics,2011,10, M111.007922.
[78]Lin, L., Huang, Z. Z., Gao, Y. et al. LC-MS based serum metabonomic analysis for renal cell carcinoma diagnosis, staging, and biomarker discovery[J]. J Proteome Res,2011,10:1396-1405.
[79]Zhang, Z., Qiu, Y., Hua, Y., et al. Serum and urinary metabonomic study of human osteosarcoma[J]. J Proteome Res,2010,9:4861-4868.
[80]Chen, J., Zhou, L., Zhang, X., et al. Urinary hydrophilic and hydrophobic metabolic profiling based on liquid chromatography-mass spectrometry methods:Differential metabolite discovery specific to ovarian cancer[J]. Electrophoresis,2012,33(22):3361-3369.
[81]Parkin, D. M., Bray, F., Ferlay, J., et al. Global cancer statistics 2002[J]. CA Cancer J. Clin., 2005,55:74-108.
[82]Jemal, A., Bray, F., Center, M. M., et al. Global cancer statistics[J]. CA Cancer J. Clin.,2011,61: 69-90.
[83]Vizcaino, A.P., Moreno, V., Lambert, R., et al. Time trends incidence of both major histologic types of esophageal carcinomas in selected countries 1973-1995[J]. Int J Cancer,2002,99: 860-868.
[84]Gholipour, C., Shalchi, R. A., Abbasi, M. A histopathological study of esophageal cancer on the western side of the Caspian littoral from 1994 to 2003[J]. Dis Esophagus,2008,21:322-327.
[85]Yang, C.S. Research on esophageal cancer in China[J]. Cancer Res,1980,40:2633-44.
[86]Blot, W. J., Li, J.Y. Some considerations in the design of a nutrition intervention trial in Linxian, People's Republic of China[J]. Natl Cancer Inst Monogr,1985,69:29-34.
[87]佟伟军,李静.120例食管癌、胃癌危险因素的病例对照研究[J].中国公共卫生,2001,17(12)：1093-1094.
[88]Islami, F., Boffetta, P., Ren, J. S., et al. High-temperature beverages and foods and esophageal cancer risk-a systematic review[J]. Int J Cancer,2009,125:491-524.
[89]Wu, M., Liu, A. M., Kampman, E., et al. Green tea drinking, high tea temperature and esophageal cancer in high-and low-risk areas of Jiangsu Province, China:a population-based case-control study[J]. Int J Cancer,2009,124:1907-1913.
[90]Engel, L. S., Chow, W. H., Vaughan, T. L., et al. Population attributable risks of esophageal and gastric cancers[J]. J Natl Cancer Inst,2003,95:1404-1413.
[91]朱俊宇,雷君,高尚兰.乌鲁木齐地区食管癌危险因素的病例对照研究[J].新疆医科大学学报,2009,32(10)：1434-1435.
[92]Kamangar, F., Chow, W. H., Abnet, C. C., et al. Environmental causes of esophageal cancer[J]. Gastroenterol Clin North Am,2009,38:27-57.
[93]Zhang, D. K., Su, X. D., Lin, P. Survival analysis of patients with stage Ⅱ squamous cell carcinoma of the thoracic esophagus after esophagectomy[J]. Cancer,2008,27(2):113-118.
[94]徐乐天,李泽坚,张志庸,等.现代胸外科学[M].北京：科学出版社,2004：596.
[95]孙旭芳,魏子白.食管癌的内镜诊断[J].中华腹部疾病杂志,2004,4(2)：107-108.
[96]靳玉兰,张伟,刘伯齐,等.食管癌前病变及原位癌组织中Ki67、P53、iNOS的异常表达[J].中华肿瘤杂志,2001,23：129-131.
[97]Hiyama, T., Yokozaki, H., Kitadai, Y., et al. Overexpression of human telomerase RNA is an early event in oesophageal carcinogenesis[J]. VirchOWS Arch,1999,434:483-487.
[98]张允清,谢立.P53、P21在食管癌和鼻咽癌中的表达及意义[J].河南肿瘤学杂志,2002,15：287-288.
[99]牛韵韵,高巍,张云汉,等.P53、P21与细胞周期调节蛋白在食管癌中的表达及意义[J].中国民政医学杂志,2000,12(6)：339-341.
[100]Ohashi, Y., Sasano, H., Yamaki, H., et al. Cell cycle inhibitory protein p27 in esophageal squamous cell carcinoma[J]. Anticancer Res,1999,19:1843-1848.
[101]Shimada, Y., Sato, F., Watanabe, G., et al. Loss of fragile histidine triad gene expression is associated with progression of esophageal squam ous cell carcinoma, but not with the patients prognosis and smoking history[J]. Cancer,2000,89:5-11.
[102]Mori, M., Mimori, K., Shiraishi, T., et al. Altered expression of Fhit in carcinoma and precarcinomatous lesions of the esophagus[J]. Cancer Res,2000,60:1177-1182.
[103]孙海军,林乐岷,薛东波,等.上消化道恶性肿瘤中P16基因突变的研究[J].中国肿瘤临床与康复,2002,9：10-11.
[104]王柏春,王性发,王巨,等.食管癌P16基因变异分析[J].中国胸心血管外科临床杂志,2000,7：35-37.
[105]毛友生,赵晓航,张德超,等.食管癌肿瘤标志物研究进展[J].世界华人消化杂志,2002,10：1321-1323.
[106]Brockmann, J. G., StNottberg, H., Glodny, B., et al. CYFRA 21-1 serum analysis in patients with esophageal cancer[J]. Clin Cancer Res,2000,6:4249-4252.
[107]Ychou, M., Khemissa-Akouz, F., Kramar, A., et al. A comparison of serum Cyfra 21-1 and SCC AG in the diagnosis of squamous cell esophageal carcinoma[J]. Bull Cancer,2001,88: 1023-1027.
[108]王立峰,张伟,上吾如,等.中食管癌发生发展过程中环氧合酶-2蛋白表达的研究[J].中华肿瘤杂志,2001,23：14-16.
[109]Nemoto, T., Terashima, S., Kogure, M., et al. Overexpression of fatty acid synthase in oesophageal squamous cell dysplasia and carcinoma[J]. Pathobiology,2001,69:297-303.
[110]Wu, H., Xue, R.Y., Lu C. L., et al. Metabolomic study for diagnostic model of oesophageal cancer using gas chromatography/mass spectrometry[J]. J Chromatogr B,2009,877:3111-3117.
[111]Zhang, J., Liu, L.Y., Wei, S. W., et al. Metabolomics study of esophageal adenocarcinoma[J]. J Thorac Cardiovasc Surg,2011,141:469-475.
[112]Davis, V. W., Schiller, D. E., Eurich, D., et al. Urinary metabolomic signature of esophageal cancer and Barrett's esophagus[J]. World J Surg Oncol,2012,10(1):271-296.
[113]Hasim, A., Ma, H., Mamtimin B., et al. Revealing the metabonomic variation of EC using 1H-NMR spectroscopy and its association with the clinicopathological characteristics[J]. Mol Biol Rep,39(9):8955-8964.
[114]Alibakhshi, A, Aminian, A, Mirshari, W. R., et al. The effect of age on the outcome of esophageal cancer surgery[J]. Ann Thorac Med,2009,4(2):71-74.
[115]Besharat, S., Jabbari, A., Semnani, S., et al. Inoperable esophageal cancer and outcome of palliative care[J]. World J Gastroenterol,2008,14(23):3725-3728.
[116]Earlam, R., Cunha-Melo, J. R. Oesophageal squamous cell carcinoma:I. A critical review of surgery[J]. Br J Surg,1980,67(6):381-390.
[117]闵敏,钟国成.食管癌生物化疗研究进展[J].西南国防医药,2009,19(10)：1050-1052.
[118]WTO handbook for reporting results of cancer treatment[M]. Geneva (Switzerland):World Health Organization,1979.
[119]Duffaud, F., Therasse, P. New guidelines to evaluate the response to treatment in solid tumors [J]. Bull Cancer,2000,87(12):869-870.
[120]陈智伟,廖美琳.RECIST标准在肿癌治疗疗效评价中的应用[J].中国肿瘤,2004,13(10)：616-618.
[121]Xuan, J. K., Pan, G. H., Qiu, Y. P., et al. Metabolomic profiling to identify potential serum biomarkers for schizophrenia and risperidone action[J]. J Proteome Res,2011,10(12): 5433-5443.
[1]Boernsen, K. O., Gatzek, S., Imbert, G. Controlled protein precipitation in combination with chip-based nanospray infusion mass spectrometry. An approach for metabolomics profiling of plasma[J]. Anal Chem,2005,77(22):7255-7264.
[2]Gao, P., Lu, C., Zhang, F. X., et al. Integrated GC-MS and LC-MS plasma metabonomics analysis of ankylosing spondylitis[J]. Analyst,2008,133(9):1214-1220.
[3]Idborg, H., Zamani, L., Edlund, P. O., et al. Metabolic fingerprinting of rat urine by LC/MS Part 1. Analysis by hydrophilic interaction liquid chromatography-electrospray ionization mass spectrometry[J]. J Chromatogr B,2005,828(1-2):9-13.
[4]Idborg, H., Zamani, L., Edlund, P. O., et al. Metabolic fingerprinting of rat urine by LC/MS Part 2. Data pretreatment methods for handling of complex data[J]. J Chromatogr B,2005,828(1-2): 14-20.
[5]Johnson, J. M., Strobel, F. H., Reed, M., et al. A rapid LC-FTMS method for the analysis of cysteine, cystine and cysteine/cystine steady-state redox potential in human plasma[J]. Clin Chim Acta,2008,396(1-2):43-48.
[6]Michopoulos, F., Lai, L., Gika, H., et al. UPLC-MS-based analysis of human plasma for metabonomics using solvent precipitation or solid phase extraction[J]. J Proteome Res,2009, 8(4):2114-2121.
[7]Crews, B., Wikoff, W. R., Patti, G. J., et al. Variability analysis of human plasma and cerebral spinal fluid reveals statistical significance of changes in mass spectrometry-based metabolomics data[J]. Anal Chem,2009,81(20):8538-8544.
[1]Parkin, D. M., Bray, F., Ferlay, J., et al. Global cancer statistics 2002[J]. CA Cancer J Clin,2005, 55:74-108.
[2]Jemal, A., Bray, F., Center, M. M., et al. Global cancer statistics[J]. CA Cancer J Clin,2011,61: 69-90.
[3]Alibakhshi, A., Aminian, A., Mirshari, W. R., et al. The effect of age on the outcome of esophageal cancer surgery[J]. Ann Thorac Med,2009,4(2):71-74.
[4]Besharat, S., Jabbari, A., Semnani, S., et al. Inoperable esophageal cancer and outcome of palliative care[J]. World J Gastroenterol,2008,14(23):3725-3728.
[5]Earlam, R., Cunha-Melo, J. R. Oesophageal squamous cell carcinoma:I. A critical review of surgery[J]. Br J Surg,1980,67(6):381-390.
[6]Xuan, J. K., Pan, G. H., Qiu, Y. P., et al. Metabolomic profiling to identify potential serum biomarkers for schizophrenia and risperidone action[J]. J Proteome Res,2011,10(12): 5433-5443.
[7]Xu, J., Tian, Y. P., Chen, Y. H., et al. Plasma preparation method for metabolomic analysisbased on rapid resolution liquid chromatography-mass spectrometry[J]. Chin J Anal Chem,2011, 39(12):1793-1797.
[8]Gika, H. G., Theodoridis, G. A., Wingate, J. E., et al. Within-day reproducibility of an HPLC-MS-based method for metabonomic analysis:application to human urine[J]. J Proteome Res,2007,6:3291-3303.
[9]Smith, C. A., Want, E. J., O'Maille, G., et al. XCMS:processing mas spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification[J]. Anal Chem, 2006,78(3):779-787.
[10]Kose, F., Weckwerth, W., Linke, T., et al. Visualizing plant metabonomic correlation networks using clique-metabolite matrices[J]. Bioinformatics,2001,17(12):1198-1208.
[11]Clish, C. B., Davidov, E., Oresic, M., et al. Integrative biological analysis of the APOE*3-Leiden transgenic mouse[J]. OMICS,2004,8(1):3-13.
[12]Robertson, D. G., Reily, M. D., Baker, J. D. Metabonomics in pharmaceutical discovery and development[J]. J Proteome Res,2007,6:526-539.
[13]Bijlsma, S., Bobeldijk, L., Verheij, E. R., et al. Large-scale human metabolomics studies:A strategy for data (pre-) processing and validation[J]. Anal Chem,2006,78:567-574.
[14]Wiklund, S., Johansson, E., Sjostrom, L., et al. Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models[J]. Anal Chem,2008,80(1):115-122.
[15]van den Berg, R. A., Hoefsloot, H. C., Westerhuis, J. A., et al. Centering, scaling, and transformations:improving the biological information content of metabolomics data[J]. BMC Genomics,2006,7:142.
[16]Bylesjo, M. R., Cloarec, O., Nicholson, J. K., et al. OPLS discriminant analysis:combining the strengths of PLS-DA and SIMCA classification[J]. J Chemom,2006,20(8-10):341-351.
[17]Eriksson, L., Johansson, E., Kettaneh-Wold, N., et al. Multivariate and megavariate data analysis part I:Basic principles and applications[M]. Sweden:Umetrics Academy,2006:35.
[18]Wiklund, S., Johansson, E., Sjostrom, L., et al. Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Anal Chem,2008,80:115-122.
[19]Kind, T., Fiehn, O. Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry[J]. BMC Bioinformatics,2007,8:105.
[20]Chen, Y. H., Zhang, R. P., Song, Y. M., et al. RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network:finding potential biomarkers for breast cancer[J]. Analyst,2009,134:2003-2011.
[21]An, Z. L., Chen, Y. H., Zhang, R. P., et al. Integrated ionization approach for RRLC-MS/MS-based metabonomics:finding potential biomarkers for lung cancer[J]. J Proteome Res,2010,9:4071-4081.
[22]Chen, T. L., Xie, G. X., Wang, X. Y., et al. Serum and urine metabolite profiling reveals potential biomarkers of human hepatocellular carcinoma[J]. Mol Cell Proteomics,2011,10: M110.004945.
[23]Borum, P. R. Changing perspective of carnitine function and the need for exogenous cartnitine of patients treated with hemodialysis[J]. Am J Clin Nutr,1996,64:976-977.
[24]Bremer, J. Carnitine-metabolism and functions[J]. Physiol Rev,1983,63:1420-1480.
[25]Qiu, Y. P., Cai, G. X., Su, M. M., et al. Serum metabolite profiling of human colorectal cancer using GC-TOFMS and UPLC-QTOFMS[J]. J Proteome Res,2009,8:4844-4850.
[26]Chen, J., Wang, W. Z., Lv, S., et al. Metabonomics study of liver cancer based on ultra performance liquid chromatography coupled to mass spectrometry with HILIC and RPLC separations[J]. Anal Chim Acta,2009,650:3-9.
[27]Adlouni, H. A., Katrib, K., Ferard, G. Changes in carnitine in polymorphonuclear leukocytes, mononuclear cells, and plasma from patients with inflammatory disorders[J]. Clin Chem,1988, 34:40-43.
[28]Zhao, Z. W., Xiao, Y. J., Elson, P., et al. Plasma lysophosphatidylcholine levels:potential biomarkers for colorectal cancer[J]. J Clin Oncol,2007,25:2696-2701.
[29]Kishi, Y., Okudaira, S., Tanaka, M., et al. Autotaxin is overexpressed in glioblastoma multiforme and contributes to cell motility of glioblastoma by converting lysophosphatidylcholine to lysophosphatidic acid[J]. J Biol Chem,2006,281:17492-17500.
[30]Hoelzinger, D. B., Mariani, L., Weis, J., et al. Gene expression profile of glioblastoma multiforme invasive phenotype points to new therapeutic targets[J]. Neoplasia,2005,7:7-16.
[31]Yang, S. Y., Lee, J., Park, C. G., et al. Expression of autotaxin (NPP-2) is closely linked to invasiveness of breast cancer cells[J]. Clin Exp Metastasis,2002,19:603-608.
[32]Watanabe, M., Houten, S. M., Mataki, C., et al. Bile acids induce energy expenditure by promoting intracellular thyroid hormone activation[J]. Nature,2006,439:484-489.
[33]Heuberger, W., Berardi, S., Jacky, E., et al. Increased urinary excretion of carnitine in patients treated with cisplatin[J]. Eur J Clin Pharmacol,1998,54:503-508.
[34]Nakajima, Y., Ito, T., Maeda, Y., et al. Evaluation of valproate effects on acylcarnitine in epileptic children by LC-MS/MS[J]. Brain Dev,2011,33:816-823.
[1]Smith, C. A., Want, E. J., O'Maille, G., et al. XCMS:processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification[J]. Anal Chem, 2006,78(3):779-787.
[2]Griffiths, W. J., Therese, K., Wang, Y. Q., et al. Targeted metabolomics for biomarker discovery [J]. Angew Chem Int Ed,2010,49:5426-5445.
[3]Guo, B., Chen, B., Liu, A., et al. Liquid chromatography-mass spectrometric multiple reaction monitoring-based strategies for expanding targeted profiling towards quantitative metabolomics[J]. Curr Drug Metab,2012,13(9):1226-1243.
[4]Schmerler, D., Neugebauer, S., Ludewig, K., et al. Targeted metabolomics for discrimination of systemic inflammatory disorders in critically ill patients[J]. J Lipid Res,2012,53(7):1369-1375.
[5]Vuk, M., Curk, T. ROC curve, lift chart and calibration plot[J]. Metodoloski zvezki,2006,3(1): 89-108.
[6]Fan, Y., Murphy, T. B., Byrne, J. C., et al. Applying random forests to identify biomarker panels in serum 2D-DIGE data for the detection and staging of prostate cancer[J]. J Proteome Res,2011, 10:1361-1373.
[7]Weinstein, J. N. A postgenomic visual icon[J]. Science,2008,319:1772-1773.
[8]Xuan, J. K., Pan, G. H., Qiu, Y. P., et al. Metabolomic profiling to identify potential serum biomarkers for schizophrenia and risperidone action[J]. J Proteome Res,2011,10:5433-5443.
[1]Pasikanti, K. K., Esuvaranathan, K., Ho, P. C. et al. Noninvasive urinary metabonomic diagnosis of human bladder cancer[J]. J Proteome Res,2010,9(6):2988-95.
[2]Ma, Y., Zhang, P., Yang, Y., et al. Metabolomics in the fields of oncology:a review of recent research[J]. Mol Biol Rep,2012,39(7):7505-11.
[3]Clyne, M. Kidney cancer:Metabolomics for targeted therapy[J]. Nat Rev Urol,2012,9(7):355.
[4]Ganti, S., Taylor, S. L., Abu, A. O., et al. Kidney tumor biomarkers revealed by simultaneous multiple matrix metabolomics analysis[J]. Cancer Res,2012,1572(14):3471-3479.
[5]Zhang, A. H., Sun, H., Wu, X. H., et al. Urine metabolomics [J]. Clinica Chimica Acta,2012,414: 65-69.
[6]Chen, Y. H., Zhang, R. P., Song, Y. M., et al. RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network:finding potential biomarkers for breast cancer[J]. Analyst,2009,134:2003-2011.
[7]An, Z. L., Chen, Y. H., Zhang, R. P., et al. Integrated ionization approach for RRLC-MS/MS-based metabonomics:finding potential biomarkers for lung cancer[J]. J Proteome Res,9:4071-4081.
[8]Tsuchiya, Y., Takahashi, Y., Jindo, T., et al. Comprehensive evaluation of canine renal papillary necrosis induced by nefiracetam, a neurotransmission enhancer[J]. European journal of pharmacology,2003,475, (1-3):119-128.
[9]陈艳华,张瑞萍,宋咏梅,等.基于快速高分辨液相色谱串联质谱技术的代谢组学尿液分析方法的建立[J].分析化学,2011,39(2)：173-177.
[10]Smith, C. A., Want, E. J., O'Maille, G., et al. XCMS:processing mas spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification[J]. Anal Chem, 2006,78(3):779-787.
[11]Bijlsma, S., Bobeldijk, L., Verheij, E. R., et al. Large-scale human metabolomics studies:A strategy for data (pre-) processing and validation[J]. Anal Chem,2006,78:567-574.
[12]van den Berg, R. A., Hoefsloot, H. C., Westerhuis, J. A., et al. Centering, scaling, and transformations:improving the biological information content of metabolomics data[J]. BMC Genomics,2006,7:142.
[13]Wiklund, S., Johansson, E., Sjostrom, L., et al. Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models[J]. Anal Chem,2008,80:115-122.
[14]Vuk, M., Curk, T. ROC curve, lift chart and calibration plot[J]. Metodoloski zvezki,2006, 3(1):89-108.
[15]Weinstein, J. N. A postgenomic visual icon[J]. Science,2008,319:1772-1773.
[16]Eriksson, L., Johansson, E., Kettaneh-Wold, N., et al. Multivariate and megavariate data analysis part I:Basic principles and applications[M]. Sweden:Umetrics Academy,2006:35.
[17]Webster, L.T., Siddiqui, U.A., Lucas, S.V., et al. Identification of separate acyl-CoA:glycine and acyl-CoA:L-glutamine N-acyltransferase activities in mitochondrial fractions from liver of rhesusmonkey and man[J]. J Biol Chem,1976,251(11):3352-3358.
[18]Yang, D., Beylot, M., Agarwal, K.C., et al. Assay of the human liver citric acid cycle probe phenylacetylglutamine and of phenylacetate in plasma by gas chromatography-mass spectrometry[J]. Anal Biochem,1993,212(1):277-282.
[19]Koizumi, H., Shimizu, T., Nishino, H., et al. Cis-urocanic acid attenuates histamine receptor-mediated activation of adenylate cyclase and increase in intracellular Ca2+[J]. Arch Dermatol Res,1998,290, (5):264-269.
[20]Scott, I. R., Harding, C. R., Barrett, J. G. Histidine-rich protein of the keratohyalin granules. Source of the free amino acids, urocanic acid and pyrrolidone carboxylic acid in the stratum corneum[J].Biochim Biophys Acta,1982,719(1):110-117.
[21]Kim, J. W., Dang, C. V. Cancer's molecular sweet tooth and the Warburg effect[J]. Cancer Res, 2006,66(18):8927-8930.
[22]Warburg, O. On the origin of cancer cells[J]. Science,1956,123(3191):309-314.
[23]Sassone-Corsi, P. The cyclic AMP pathway[J]. Cold Spring Harb Perspect Biol,2012,4(12): 1-3.
[24]Fallahian, F., Karami-Tehrani, F., Salami, S. et al. Cyclic GMP induced apoptosis via protein kinase G in oestrogen receptor-positive and -negative breast cancer cell lines[J]. FEBS J,1997, 278(18):3360-3369.
[25]Loweth, A. C., Williams, G. T., Scarpello, J. H., et al. Evidence for the involvement of cGMP and protein kinase G in nitric oxide-induced apoptosis in the pancreatic B-cell line, HIT-T15[J]. FEBS Lett,1997,400(3):285-288.
[26]Mills, G. C., Schmalstieg, F. C., Goldblum, R. M. Urinary purines and nucleosides in immune deficiencies[J]. Ann N Y Acad Sci,1985,451:66-77.
[1]Schmerler, D., Neugebauer, S., Ludewig, K., et al. Targeted metabolomics for discrimination of systemic inflammatory disorders in critically ill patients[J]. J Lipid Res,53(7):1369-1375.
[2]Guo, B., Chen, B., Liu, A. M., et al. Liquid chromatography-mass spectrometric multiple reaction monitoring-based strategies for expanding targeted profiling towards quantitative metabolomics[J]. Current Drug Metabolism,2012,13:1226-1243.
[3]Griffiths, W. J., Therese, K., Wang, Y. Q., et al. Targeted metabolomics for biomarker discovery [J]. Angew Chem Int Ed,2010,49:5426-5445.
[4]Vuk, M., Curk, T. ROC curve, lift chart and calibration plot[J]. Metodoloski zvezki,2006,3(1): 89-108.
[5]Weinstein, J. N. A postgenomic visual icon[J]. Science,2008,319:1772-1773.
[6]Fan, Y., Murphy, T. B., Byrne, J. C., et al. Applying random forests to identify biomarker panels in serum 2D-DIGE data for the detection and staging of prostate cancer[J]. J Proteome Res,2011, 10:1361-1373.