基于LC-MS/MS技术的肺癌血浆代谢组学研究
|
摘要
本论文采用快速高分辨液相色谱(RRLC)分离系统与MS/MS技术联用的高通量分析方法,建立了适用于肺癌血浆样本的非靶向与靶向代谢组学相结合的研究方法,并将其应用于大批量肺癌样本的研究,筛选出一批与肺癌诊断密切相关的可靠的潜在生物标志物。本研究在实验室前期研究的基础上,进一步优化并建立了基于RRLC-MS/MS技术的血浆非靶向代谢组学分析方法。首先,采用该方法对不同温度下放置不同时间的全血和血浆样本中内源性代谢物的短期稳定性,血浆样本在-80℃条件下放置约5年的长期稳定性进行了系统考察,探究了血浆内源性代谢物在不同条件下的变化规律。本研究结果不仅可辅助潜在生物标志物的识别,还可为代谢组学研究中针对血液样本的采集、制备和储存等操作提供指导依据。在此基础上,将非靶向RRLC-MS/MS分析方法应用于大批量肺癌患者与健康志愿者血浆样本的非靶向代谢组学研究,发现了74个存在显著性差异的内源性小分子代谢物。随后,以这些差异代谢物为研究对象,建立了基于RRLC-MS/MS联用技术的靶向代谢组学高通量分析方法,并将其应用于对肺癌诊断生物标志物的验证与确认。通过对新一批肺癌患者与健康志愿者的血浆样品进行分析,筛选出38个与肺癌诊断密切相关的潜在生物标志物。采用高分辨MS及MS/MS谱分析及其质谱裂解规律的解析,结合代谢相关性网络分析、数据库检索分析推断可能生物标志物的结构,共鉴定其中32个潜在生物标志物的结构。此外,通过对潜在生物标志物的代谢途径进行分析,推断肺癌可能与脂肪酸代谢存在密切关系。
本论文的研究内容主要包括以下四个部分：
1.血浆非靶向代谢组学的LC-MS/MS分析方法研究
本论文在实验室前期血浆代谢组学研究的基础上,通过对血浆样品的前处理条件、色谱条件和质谱条件的进一步优化,尤其对复溶溶剂进行了细致的考察,对该方法的精密度、灵敏度和稳定性进行验证,建立了稳定、可靠的适用于非靶向代谢组学研究中血浆样品分析的RRLC-MS/MS检测方法。研究发现离心浓缩后的血浆样品采用不同溶剂复溶会引起不同的色谱行为,该现象严重影响了分析数据的重复性,以及后期代谢物的识别。因此,本研究对不同比例的乙腈水溶液引起溶剂效应的影响范围与程度进行了系统考察,最终确定初始流动相(乙腈：水；2：98)作为复溶溶剂。此外,对优化后的分析方法进行了一系列方法学考察,包括精密度、灵敏度和稳定性,结果表明本方法的精密度符合生物样品分析的要求,大部分代谢物的检测灵敏度较高,且分析过程中样本稳定性良好,适用于血浆样品的非靶向代谢组学分析。
2.血浆代谢物的稳定性研究
本研究采用正、负离子模式相结合的RRLC-MS/MS分析方法,结合多变量统计分析,针对不同温度(37℃和4℃)下放置不同时间(0、1、2、4、8、12、24h)的全血和血浆样本中内源性代谢物的短期稳定性,以及血浆样本在-80℃条件下放置约5年的长期稳定性进行了系统考察。通过主成分分析(PCA)法分析代谢物整体轮廓的变化,并结合10类代谢物时间依赖性变化规律的分析,对代谢物的短期稳定性进行了考察。在血浆样本的长期稳定性研究中,采用以正交最小二乘判别分析(OPLS-DA)为核心的多变量统计方法,分析识别了具有显著性变化的内源性代谢物,并鉴定出其中36个代谢物的结构,还发现了一类具有百倍变化的代谢物。本研究结果不仅通过不稳定代谢物的差异变化辅助潜在生物标志物的识别,还可以为靶向与非靶向代谢组学研究中血液样本的采集、制备和储存等操作提供指导依据。
3.非靶向分析方法在肺癌代谢组学研究中的应用
将以上建立的非靶向代谢组学RRLC-MS/MS分析方法,应用于232例肺癌患者与155例健康志愿者血浆样品的代谢组学研究中,以寻找与肺癌诊断相关的潜在生物标志物。运用该方法发现了74个代谢物的含量在肺癌患者与健康志愿者血浆中存在显著性差异,而且这些代谢物的异常可能与肺癌患者体内的代谢紊乱存在密切关系。进一步采用高分辨MS及MS/MS谱分析及其质谱裂解规律的解析,结合代谢相关性网络、数据库检索分析推断这些差异代谢物的结构,共鉴定出50个差异代谢物的结构,其中包括18个溶血磷酸胆碱、17个脂酰肉碱(含8个p-羟基脂酰肉碱)、7个β-羟基脂肪酸等。同时,选择相关代谢物的标准品进行LC-MS/MS谱比较分析,确认了12个差异代谢物的结构。
4.基于靶向代谢组学的肺癌诊断生物标志物验证
采用正、负离子检测模式相结合的RRLC与QTRAP型MS/MS联用技术,以非靶向代谢组学发现的74个差异代谢物为研究对象,开展了基于RRLC-MS/MS的MRM靶向分析方法研究,并针对该方法的精密度、灵敏度进行了验证,建立了可靠、灵敏、快速的适用于血浆中可能生物标志物的靶向分析方法。结果表明,本方法的精密度符合生物样品的分析要求,具有较高的检测灵敏度,能够满足血浆样品靶向代谢组学分析的要求。采用该分析方法对91例肺癌患者与66例健康志愿者的血浆样品进行分析,结果从上述74个差异代谢物中筛选出38个与肺癌诊断密切相关的潜在诊断标志物。分析鉴定出其中32个代谢物的结构,包括17个脂酰肉碱(含8个β-羟基脂酰肉碱)、7个β-羟基脂肪酸、1个溶血磷酸胆碱等。此外,研究发现脂酰肉碱以及β-羟基脂肪酸的含量在肺癌患者血浆中明显下降,通过潜在生物标志物之间相关性和代谢途径的进一步考察,分析推断肺癌可能与脂肪酸代谢存在密切关系。
A high-throughput metabonomics analysis method has been implemented by using rapid resolution liquid chromatography-tandem mass spectrometry (RRLC-MS/MS) with intensive validation of endogenous metabolites'stability and precision. Untargeted and targeted analysis methods based on RRLC-MS/MS have been applied to investigate a large scale of lung cancer subjects and many reliable potential biomarkers for diagnosis have been found.
Based on the previous study of plasma metabonomics in our laboratory, a high-throughput metabonomics analysis method has been implemented by using RRLC-MS/MS. Further the RRLC-MS/MS analysis method in both positive and negative ion modes combined with multivariate statistics was employed to investigate the short-term stability of metabolites in blood and plasma at different temperatures, as well as the long-term stability of metabolites in plasma delayed at-80℃for5years to summarize the change rules of metabolites systematically. The thorough understanding of the effects of metabolites'stability is not only necessary for improving the reliability of potential biomarkers, but also useful to provide pre-analytical operation guidance to both untargeted and targeted metabonomics. Untargeted LC-MS/MS analysis method established previously was applied to investigate lung cancer subjects to find biochemically significant metabolite biomarkers. After a large scale plasma samples were studied,74endogenous metabolites showing significant differences between lung cancer patients and healthy volunteers were found. Then a targeted LC-MS/MS metabonomics method, based on the74endogenous metabolites referred before, was developed and applied to validate the diagnosis biomarkers of lung cancer. By analyzing other batch plasma samples of lung cancer,38potential biomarkers related to the diagnosis of lung cancer were selected. Subsequently, high resolution MS and MS/MS analyses, as well as inter-metabolite correlation analysis were performed for the identification of the metabolites of interest. Until now,32potential biomarkers have been identified. The further metabonomics pathway analysis indicated that lung cancer was related to fatty acid metabolism.
1. Untargeted metabonomics analysis method based on LC-MS/MS technology
Our laboratory has developed the method of plasma metabonomics based on LC-MS/MS in previous study. And on this basis a untargeted LC-MS/MS-based metabonomics using electrospray ionization (ESI) in both positive and negative ion modes has been further optimized in the field of pretreatment method of plasma samples and the analytical condition of liquid chromatography and mass spectrometry analysis, especially the re-dissolution solvents. The method has also been intensively validated for its precision, sensitivity and stability, ensuring the method is appropriate for untargeted plasma metabonomics study. The different chromatographic behavior was observed when the dried plasma samples were redissolved with different re-dissolution solvents, which affected chromatographic behavior and made some potential biomarkers identified by mistake. Systematic analysis of the effect of re-dissolution solvent on chromatographic behavior confirmed that initial mobile phase was the optimal re-dissolution solvent to acquire accurate data of metabolites. In addition, the methodology of this method was investigated and the results demonstrated that this method had high precision, good sensitivity, and could be used for the metabolomics analysis of plasma. Therefore, an accurate untargeted metabonomics based on LC-MS using ESI in both positive and negative ion modes was developed to analyze plasma samples.
2. Investigation on the stability of plasma metabolites
The short-term stability of metabolites in plasma at4℃or37℃after a delay in sample processing (0,1,2,4,8,12, and24h) and the long-term stability of metabolites in plasma at-80℃for five years were investigated by RRLC-MS/MS-based metabonomics analysis in positive and negative ion modes combined with multivariate statistics. Principal components analysis (PCA) and ten kinds of identified metabolites were used to summarize the time-dependent change rules in metabolites systematically at different temperatures in short-term stability study. The long-term stability of metabolites in plasma specimens stored at-80℃for five years was studied by OPLS-DA. Analysis of these subjects identified36metabolites with statistically significant changes in expression and found a kind of metabolites with hundred-fold change. These studies show that a thorough understanding of the effects of metabolite stability is necessary for improving the reliability of potential biomarkers. Meanwhile, it is useful to provide pre-analytical operation guidance to both untargeted and targeted metabonomics.
3. Untargeted metabonomics study on lung cancer
A metabonomics strategy method of untargeted plasma metabonomics based on LC-MS/MS has been applied to find biologically significant metabolite biomarkers in lung cancer. A large number of biological samples including232lung cancer patients and155healthy volunteers were analyzed to obtain abundant information and find the potential biomarkers that were closely associated with the diagnosis of lung cancer. Until now,74potential biomarkers have been found by RRLC-ESIMS in positive and negative ion modes in this metabonomics study, which demonstrates that these abnormal metabolites were related to metabolic disorders in lung cancer patients. Up to now50potential biomarkers have been identified by using high resolution MS and MS/MS analyses, as well as fragmentation rules of metabolites, which included18lysophosphatidylcholines,17acylcarnitines (8hydroxylacylcarnitines),7P-hydroxy fatty acids, et al. Twelve potential biomarkers have been identified by comparison with related standards by LC-MS/MS analysis.
4. Targeted metabonomics study on lung cancer:validating potential diagnosis biomarkers
In present study, a targeted metabonomics based on74discriminating metabolites screened from untargeted metabonomics was developed using LC-MS/MS technology with electrospray ionization (ESI) in both positive and negative ion modes, and multivariate statistics. Another batch of biological samples including91lung cancer patients and66healthy volunteers were analyzed to validate the accuracy of discriminating metabolites and found more reliable potential biomarkers that were closely associated with the diagnosis of lung cancer. Analysis of these subjects found38metabolites with statistically significant changes in expression, and the identified32metabolites including17acylcarnitines (8hydroxylacylcarnitines),7β-hydroxy fatty acids,1lysophosphatidylcholines, et al. The level of17acylcarnitines and7β-hydroxy fatty acids was significantly lower in lung cancer plasma. Then the metabolic correlation and pathway analysis have been studied, suggesting lung cancer may be closely related with fatty acid metabolism.
本论文的研究内容主要包括以下四个部分：
1.血浆非靶向代谢组学的LC-MS/MS分析方法研究
本论文在实验室前期血浆代谢组学研究的基础上,通过对血浆样品的前处理条件、色谱条件和质谱条件的进一步优化,尤其对复溶溶剂进行了细致的考察,对该方法的精密度、灵敏度和稳定性进行验证,建立了稳定、可靠的适用于非靶向代谢组学研究中血浆样品分析的RRLC-MS/MS检测方法。研究发现离心浓缩后的血浆样品采用不同溶剂复溶会引起不同的色谱行为,该现象严重影响了分析数据的重复性,以及后期代谢物的识别。因此,本研究对不同比例的乙腈水溶液引起溶剂效应的影响范围与程度进行了系统考察,最终确定初始流动相(乙腈：水；2：98)作为复溶溶剂。此外,对优化后的分析方法进行了一系列方法学考察,包括精密度、灵敏度和稳定性,结果表明本方法的精密度符合生物样品分析的要求,大部分代谢物的检测灵敏度较高,且分析过程中样本稳定性良好,适用于血浆样品的非靶向代谢组学分析。
2.血浆代谢物的稳定性研究
本研究采用正、负离子模式相结合的RRLC-MS/MS分析方法,结合多变量统计分析,针对不同温度(37℃和4℃)下放置不同时间(0、1、2、4、8、12、24h)的全血和血浆样本中内源性代谢物的短期稳定性,以及血浆样本在-80℃条件下放置约5年的长期稳定性进行了系统考察。通过主成分分析(PCA)法分析代谢物整体轮廓的变化,并结合10类代谢物时间依赖性变化规律的分析,对代谢物的短期稳定性进行了考察。在血浆样本的长期稳定性研究中,采用以正交最小二乘判别分析(OPLS-DA)为核心的多变量统计方法,分析识别了具有显著性变化的内源性代谢物,并鉴定出其中36个代谢物的结构,还发现了一类具有百倍变化的代谢物。本研究结果不仅通过不稳定代谢物的差异变化辅助潜在生物标志物的识别,还可以为靶向与非靶向代谢组学研究中血液样本的采集、制备和储存等操作提供指导依据。
3.非靶向分析方法在肺癌代谢组学研究中的应用
将以上建立的非靶向代谢组学RRLC-MS/MS分析方法,应用于232例肺癌患者与155例健康志愿者血浆样品的代谢组学研究中,以寻找与肺癌诊断相关的潜在生物标志物。运用该方法发现了74个代谢物的含量在肺癌患者与健康志愿者血浆中存在显著性差异,而且这些代谢物的异常可能与肺癌患者体内的代谢紊乱存在密切关系。进一步采用高分辨MS及MS/MS谱分析及其质谱裂解规律的解析,结合代谢相关性网络、数据库检索分析推断这些差异代谢物的结构,共鉴定出50个差异代谢物的结构,其中包括18个溶血磷酸胆碱、17个脂酰肉碱(含8个p-羟基脂酰肉碱)、7个β-羟基脂肪酸等。同时,选择相关代谢物的标准品进行LC-MS/MS谱比较分析,确认了12个差异代谢物的结构。
4.基于靶向代谢组学的肺癌诊断生物标志物验证
采用正、负离子检测模式相结合的RRLC与QTRAP型MS/MS联用技术,以非靶向代谢组学发现的74个差异代谢物为研究对象,开展了基于RRLC-MS/MS的MRM靶向分析方法研究,并针对该方法的精密度、灵敏度进行了验证,建立了可靠、灵敏、快速的适用于血浆中可能生物标志物的靶向分析方法。结果表明,本方法的精密度符合生物样品的分析要求,具有较高的检测灵敏度,能够满足血浆样品靶向代谢组学分析的要求。采用该分析方法对91例肺癌患者与66例健康志愿者的血浆样品进行分析,结果从上述74个差异代谢物中筛选出38个与肺癌诊断密切相关的潜在诊断标志物。分析鉴定出其中32个代谢物的结构,包括17个脂酰肉碱(含8个β-羟基脂酰肉碱)、7个β-羟基脂肪酸、1个溶血磷酸胆碱等。此外,研究发现脂酰肉碱以及β-羟基脂肪酸的含量在肺癌患者血浆中明显下降,通过潜在生物标志物之间相关性和代谢途径的进一步考察,分析推断肺癌可能与脂肪酸代谢存在密切关系。
A high-throughput metabonomics analysis method has been implemented by using rapid resolution liquid chromatography-tandem mass spectrometry (RRLC-MS/MS) with intensive validation of endogenous metabolites'stability and precision. Untargeted and targeted analysis methods based on RRLC-MS/MS have been applied to investigate a large scale of lung cancer subjects and many reliable potential biomarkers for diagnosis have been found.
Based on the previous study of plasma metabonomics in our laboratory, a high-throughput metabonomics analysis method has been implemented by using RRLC-MS/MS. Further the RRLC-MS/MS analysis method in both positive and negative ion modes combined with multivariate statistics was employed to investigate the short-term stability of metabolites in blood and plasma at different temperatures, as well as the long-term stability of metabolites in plasma delayed at-80℃for5years to summarize the change rules of metabolites systematically. The thorough understanding of the effects of metabolites'stability is not only necessary for improving the reliability of potential biomarkers, but also useful to provide pre-analytical operation guidance to both untargeted and targeted metabonomics. Untargeted LC-MS/MS analysis method established previously was applied to investigate lung cancer subjects to find biochemically significant metabolite biomarkers. After a large scale plasma samples were studied,74endogenous metabolites showing significant differences between lung cancer patients and healthy volunteers were found. Then a targeted LC-MS/MS metabonomics method, based on the74endogenous metabolites referred before, was developed and applied to validate the diagnosis biomarkers of lung cancer. By analyzing other batch plasma samples of lung cancer,38potential biomarkers related to the diagnosis of lung cancer were selected. Subsequently, high resolution MS and MS/MS analyses, as well as inter-metabolite correlation analysis were performed for the identification of the metabolites of interest. Until now,32potential biomarkers have been identified. The further metabonomics pathway analysis indicated that lung cancer was related to fatty acid metabolism.
1. Untargeted metabonomics analysis method based on LC-MS/MS technology
Our laboratory has developed the method of plasma metabonomics based on LC-MS/MS in previous study. And on this basis a untargeted LC-MS/MS-based metabonomics using electrospray ionization (ESI) in both positive and negative ion modes has been further optimized in the field of pretreatment method of plasma samples and the analytical condition of liquid chromatography and mass spectrometry analysis, especially the re-dissolution solvents. The method has also been intensively validated for its precision, sensitivity and stability, ensuring the method is appropriate for untargeted plasma metabonomics study. The different chromatographic behavior was observed when the dried plasma samples were redissolved with different re-dissolution solvents, which affected chromatographic behavior and made some potential biomarkers identified by mistake. Systematic analysis of the effect of re-dissolution solvent on chromatographic behavior confirmed that initial mobile phase was the optimal re-dissolution solvent to acquire accurate data of metabolites. In addition, the methodology of this method was investigated and the results demonstrated that this method had high precision, good sensitivity, and could be used for the metabolomics analysis of plasma. Therefore, an accurate untargeted metabonomics based on LC-MS using ESI in both positive and negative ion modes was developed to analyze plasma samples.
2. Investigation on the stability of plasma metabolites
The short-term stability of metabolites in plasma at4℃or37℃after a delay in sample processing (0,1,2,4,8,12, and24h) and the long-term stability of metabolites in plasma at-80℃for five years were investigated by RRLC-MS/MS-based metabonomics analysis in positive and negative ion modes combined with multivariate statistics. Principal components analysis (PCA) and ten kinds of identified metabolites were used to summarize the time-dependent change rules in metabolites systematically at different temperatures in short-term stability study. The long-term stability of metabolites in plasma specimens stored at-80℃for five years was studied by OPLS-DA. Analysis of these subjects identified36metabolites with statistically significant changes in expression and found a kind of metabolites with hundred-fold change. These studies show that a thorough understanding of the effects of metabolite stability is necessary for improving the reliability of potential biomarkers. Meanwhile, it is useful to provide pre-analytical operation guidance to both untargeted and targeted metabonomics.
3. Untargeted metabonomics study on lung cancer
A metabonomics strategy method of untargeted plasma metabonomics based on LC-MS/MS has been applied to find biologically significant metabolite biomarkers in lung cancer. A large number of biological samples including232lung cancer patients and155healthy volunteers were analyzed to obtain abundant information and find the potential biomarkers that were closely associated with the diagnosis of lung cancer. Until now,74potential biomarkers have been found by RRLC-ESIMS in positive and negative ion modes in this metabonomics study, which demonstrates that these abnormal metabolites were related to metabolic disorders in lung cancer patients. Up to now50potential biomarkers have been identified by using high resolution MS and MS/MS analyses, as well as fragmentation rules of metabolites, which included18lysophosphatidylcholines,17acylcarnitines (8hydroxylacylcarnitines),7P-hydroxy fatty acids, et al. Twelve potential biomarkers have been identified by comparison with related standards by LC-MS/MS analysis.
4. Targeted metabonomics study on lung cancer:validating potential diagnosis biomarkers
In present study, a targeted metabonomics based on74discriminating metabolites screened from untargeted metabonomics was developed using LC-MS/MS technology with electrospray ionization (ESI) in both positive and negative ion modes, and multivariate statistics. Another batch of biological samples including91lung cancer patients and66healthy volunteers were analyzed to validate the accuracy of discriminating metabolites and found more reliable potential biomarkers that were closely associated with the diagnosis of lung cancer. Analysis of these subjects found38metabolites with statistically significant changes in expression, and the identified32metabolites including17acylcarnitines (8hydroxylacylcarnitines),7β-hydroxy fatty acids,1lysophosphatidylcholines, et al. The level of17acylcarnitines and7β-hydroxy fatty acids was significantly lower in lung cancer plasma. Then the metabolic correlation and pathway analysis have been studied, suggesting lung cancer may be closely related with fatty acid metabolism.
引文
[1]Tweeddale, H.; Notley-McRobb, L.; Ferenci, T. Effect of slow growth on metabolism of Escherichia coli, as revealed by global metabolite pool ("metabolome") analysis [J]. J. Bacteriol, 1998,180 (19):5109-5116.
[2]Nicholson, J. K.; D.Wilson, I. Understanding'Global'Systems Biology:Metabonomics and the Continuum of Metabolism [J]. Nat. Rev. DrugDiscov.,2003,2:668-676.
[3]Bu, Q.; Huang, Y.; Yan, G.; Cen, X.; Zhao, Y. L. Metabolomics:a revolution for novel cancer marker identification [J]. Comb. Chem. High Throughput Screen.,2012,15 (3):266-275.
[4]Fiehn, O. Metabolomics-the link between genotypes and phenotypes [J]. Plant Mol. Biol.,2002, 48:155-171.
[5]Hall, R.; Beale, M.; Fiehn, O.; Hardy, N.; Sumner, L.; Bino, R. Plant Metabolomics:The Missing Link in Functional Genomics Strategies [J]. The Plant Cell,2002,14:1437-1440.
[6]Nicholson, J. K.; Lindon, J. C.; Holmes, E. Metabonomies understaniding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data [J]. Xenobiotica,1999,29:1181-1189.
[7]Nicholson, J. K.; Connelly, J.; Lindon, J. C.; Holmes, E. Metabonomics:a platform for studying drug toxicity and gene function [J]. Nat. Rev. Drug Discov.,2002,1:152-161.
[8]Bernini, P.; Bertini, I.; Luchinat, C.; Nincheri, P.; Staderini, S.; Turano, P. Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks [J]. J. Biomol. NMR,2011,49 (3-4):231-243.
[9]Dettmer, K.; Aronov, P. A.; Hammock. B. D. Mass spectrometry-based metabolomics [J]. Mass Spectrom. Rev.,2007,26 (1):51-78.
[10]Weiss, R.H; Kim, K. Metabolomics in the study of kidney diseases [J]. Nature Reviews Nephrology,2012,8,22-33.
[11]Ceglarek, U.; Leichtle, A.; Brugel, M.; Kortz, L.; Brauer, R.; Bresler, K.; Thiery, J.; Fiedler, G. M. Challenges and developments in tandem mass spectrometry based clinical metabolomics [J]. Mol. Cell. Endocrinol,2009,301 (1-2):266-271.
[12]Fardet, A.; Llorach, R.; Martin, J. F.; Besson, C.; Lyan, B.; Pujos-Guillot, E.; Scalbert, A. 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.
[13]Bobeldijk, I.; Hekman, M.; de Vries-van der Weij, J.; Coulier, L.; Ramaker, R.; Kleemann, R.; Kooistra, T.; Rubingh, C.; Freidig, A.; Verheij, E. Quantitative profiling of bile acids in biofluids and tissues based on accurate mass high resolution LC-FT-MS:compound class targeting in a metabolomics workflow [J]. J. Chromatogr. B,2008,871 (2):306-313.
[14]Harrigan, G. G.; LaPlante, R. H.; Cosma, G. N.; Cockerell, G.; Goodacre, R.; Maddox, J. F; Luyendyk, J. P.; Ganey, P. E.; Roth, R. A. 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.
[15]Mishur, R. J.; Rea, S. L. Applications of mass spectrometry to metabolomics and metabonomics: Detection of biomarkers of aging and of age-related diseases [J]. Mass Spectrom. Rev.,2012,31 (1):70-95.
[16]An, Z. L.; Chen, Y. H.; Zhang, R. P.; Song, Y. M.; Sun, J. H.; He, J. M.; Bai, J. F; Dong, L. J.; Zhan, Q. M.; Abliz, Z. Integrated Ionization Approach for RRLC-MS/MS-based Metabonomics: Finding Potential Biomarkers for Lung Cancer [J]. J. Proteome Res.,2010,9:4071-4081.
[17]Spagou, K.; Theodoridis, G.; Wilson, I.; Raikos, N.; Greaves, P.; Edwards, R.; Nolan, B.; Klapa, M. I. A GC-MS metabolic profiling study of plasma samples from mice on low-and high-fat diets [J]. J. Chromatogr. B,2011,879 (17-18):1467-1475.
[18]Koh, Y.; Pasikanti, K. K.; Yap, C. W.; Chan, E. C. Comparative evaluation of software for retention time alignment of gas chromatography/time-of-flight mass spectrometry-based metabonomic data [J]. J. Chromatogr. A,2010,1217 (52):8308-8316.
[19]Nicholson, J. K.; Lindon, J. C. Metabonomics [J]. Nature,2008,455:1054-1056.
[20]Cho, S. H.; Jung, B. H.; Lee, S. H.; Lee, W. Y; Kong, G.; Chung, B. C. Direct determination of nucleosides in the urine of patients with breast cancer using column-switching liquid chromatography-tandem mass spectrometry [J]. Biomed. Chromatogr.,2006,20 (11):1229-1236.
[21]Cubbon, S.; Antonio, C.; Wilson, J.; Thomas-Oates, J. Metabolomic applications of HILIC-LC-MS [J]. Mass Spectrom. Rev.,2010,29 (5):671-684.
[22]Guo, Y; Gaiki, S. Retention and selectivity of stationary phases for hydrophilic interaction chromatography [J].J. Chromatogr. A,2011,1218 (35):5920-5938.
[23]Shulaev, V. Metabolomics technology and bioinformatics [J]. Brief. Bioinform.,2006,7 (2): 128-139.
[24]Yao, H.; Shi, P.; Zhang, L.; Fan, X.; Shao, Q.; Cheng, Y. Untargeted metabolic profiling reveals potential biomarkers in myocardial infarction and its application [J]. Mol. BioSyst.,2010,6 (6): 1061-1070.
[25]Katajamaa, M; Miettinen, J.; Oresic, M. MZmine:toolbox for processing and visualization of mass spectrometry based molecular profile data [J]. Bioinformatics,2006,22 (5):634-636.
[26]Pluskal, T.; Castillo, S.; Villar-Briones, A.; Oresic, M. MZmine 2:Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data [J]. BMC Bioinformatics,2010,11 (1):395-406.
[27]Bijlsma, S.; Bobeldijk, I.; Verheij, E. R.; Ramaker, R.; Kochhar, S.; Macdonald, I. A.; van Ommen, B.; Smilde, A. K. Large-scale human metabolomics studies:a strategy for data (pre-) processing and validation [J]. Anal. Chem.,2006,78 (2):567-574.
[28]Idborg-Bjorkman, H.; Edlund, P. O.; Kvalheim, O. M.; Schuppe-Koistinen, I.; Jacobsson, S. P. Screening of biomarkers in rat urine using LC/electrospray ionization-MS and two-way data analysis [J]. Anal. Chem.,2003,75 (18):4784-4792.
[29]van den Berg, R. A.; Hoefsloot, H. C.; Westerhuis, J. A.; Smilde, A. K.; van der Werf, M. J. Centering, scaling, and transformations:improving the biological information content of metabolomics data [J]. BMC Genomics,2006,7:142-157.
[30]Trygg, J.; Holmes, E.; Lundstedt, T. Chemometrics in metabonomics [J]. J. Proteome Res.,2007, 6 (2):469-479.
[31]Eriksson, L.; Antti, H.; Gottfries, J.; Holmes, E.; Johansson, E.; Lindgren, F.; Long, L; Lundstedt, T.; Trygg, J.; Wold, S. Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm) [J]. Anal. Bioanal. Chem.,2004,380 (3):419-429.
[32]Geng, L.; Sun, H.; Yuan, Y.; Liu, Z.; Cui, Y.; Bi, K.; Chen, X. Discrimination of raw and vinegar-processed Genkwa Flos using metabolomics coupled with multivariate data analysis:A discrimination study with metabolomics coupled with PCA [J]. Fitoterapia,2012,84:286-294.
[33]Issaq, H. J.; Nativ, O.; Waybright, T.; Luke, B.; Veenstra, T. D.; Issaq, E. J.; Kravstov, A.; Mullerad, M. 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.
[34]Keun, H. C.; Ebbels, T. M.; Bollard, M. E.; Beckonert, O.; Antti, H.; Holmes, E.; Lindon, J. C. Nicholson, J. K. Geometric trajectory analysis of metabolic responses to toxicity can define treatment specific profiles [J]. Chem. Res. Toxicol,2004,17 (5):579-587.
[35]Lindon, J. C.; Nicholson, J. K.; Holmes, E. The Handbook of Metabonomics and Metabolomics [M]. Amsterdam:Elsevier,2007.
[36]Sreekumar, A.; Poisson, L. M.; Rajendiran, T. M.; Khan, A. P.; Cao, Q.; Yu, J.; Laxman, B.; Mehra, R.; Lonigro, R. J.; Li, Y.; Nyati, M. K.; Ahsan, A.; Kalyana-Sundaram, S.; Han, B.; Cao, X.; Byun, J.; Omenn, G. S.; Ghosh, D.; Pennathur, S.; Alexander, D. C.; Berger, A.; Shuster, J. R.; Wei, J. T.; Varambally, S.; Beecher, C.; Chinnaiyan, A. M. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression [J]. Nature,2009,457 (7231):910-914.
[37]Yang, J.; Xu, G.; Hong, Q.; Liebich, H. M.; Lutz, K.; Schmulling, R. M.; Wahl, H. G. Discrimination of Type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles [J]. J. Chromatogr. B,2004,813 (1-2):53-58.
[38]Yang, J.; Xu, G.; Zheng, Y.; Kong, H.; Pang, T.; Lv, S.; Yang, Q. 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.
[39]Li, H.; Shi, X.; Qiao, L.; Lu, X.; Xu, G. Synthesis of a new type of echinus-like Fe3O4@TiO2 core-shell-structured microspheres and their applications in selectively enriching phosphopeptides and removing phospholipids [J]. J. Chromatogr. A,2013,1275:9-16.
[40]Yang, H.; Pang, W.; Lu, H.; Cheng, D.; Yan, X.; Cheng, Y; Jiang, Y. Comparison of metabolic profiling of cyanidin-3-O-galactoside and extracts from blueberry in aged mice [J]. J. Agric. Food Chem.,2011,59 (5):2069-2076.
[41]Lu, C.; Wang, Y; Sheng, Z.; Liu, G.; Fu, Z.; Zhao, J.; Yan, X.; Zhu, B.; Peng, S. NMR-based metabonomic analysis of the hepatotoxicity induced by combined exposure to PCBs and TCDD in rats [J]. Toxicol. Appl. Pharmacol.,2010,248 (3),178-184.
[42]Jiang, L.; Huang, J.; Wang, Y; Tang, H. Eliminating the dication-induced intersample chemical-shift variations for NMR-based biofluid metabonomic analysis [J]. Analyst,2012,137 (18):4209-4019.
[43]Zhao, X. J.; Hao, F.; Huang, C.; Rantalainen, M.; Lei, H.; Tang, H.; Wang, Y. Systems responses of rats to mequindox revealed by metabolic and transcriptomic profiling [J]. J. Proteome. Res., 2012,11 (9):4712-4721.
[44]Zhang, Z. M.; Liang, Y Z.; Lu, H. M.; Tan, B. B.; Xu, X. N.; Ferro, M. Multiscale peak alignment for chromatographic datasets [J]. J. Chromatogr. A,2012,1223:93-106.
[45]Cao, D. S.; Zeng, M. M.; Yi, L. Z.; Wang, B.; Xu, Q. S.; Hu, Q. N.; Zhang, L. X.; Lu, H. M.; Liang, Y. Z. A novel kernel Fisher discriminant analysis:constructing informative kernel by decision tree ensemble for metabolomics data analysis [J]. Anal. Chim. Acta.,2011,706 (1), 97-104.
[46]Cong, W.; Liang, Q.; Li, L.; Shi, J.; Liu, Q.; Feng, Y.; Wang, Y.; Luo, G. Metabonomic study on the cumulative cardiotoxicity of a pirarubicin liposome powder [J]. Talanta,2012,89:91-98.
[47]Zhao, T.; Zhang, H.; Zhang, X.; Lu, J.; Yin, T.; Liang, Q.; Wang, Y; Luo, G.; Lan, H.; Li, P. Intrarenal metabolomics reveals the association of local organic toxins with the progression of diabetic kidney disease [J]. J. Pharm. Biomed. Anal.,2012,60:32-43.
[48]Li, X.; Chen, H.; Jia, W.; Xie, G. A metabolomics-based strategy for the quality control of traditional chinese medicine:shengmai injection as a case study [J]. Evid. Based Complement Alternat. Med,2013,2013,1-8.
[49]Lu, J.; Xie, G.; Jia, W. Metabolomics in human type 2 diabetes research [J]. Front. Med.,2013,7 (1):4-13.
[50]陈艳华,张瑞萍,宋咏梅,董立佳,詹启敏,再帕尔·阿不力孜.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.
[51]徐婧,田亚平,陈艳华,张瑞萍,杨芬,宋咏梅,再帕尔·阿不力孜.代谢组学血浆样品前处理及其快速高分辨液相色谱-质谱分析方法研究[J].分析化学,2011,39(12)：1793-1797.
[52]An, Z.; Chen, Y.; Zhang, R.; Song, Y; Sun, J.; He, J.; Bai, J.; Dong, L.; Zhan, Q.; Abliz, Z. Integrated ionization approach for RRLC-MS/MS-based metabonomics:finding potential biomarkers for lung cancer [J]. J. Proteome Res.,2010,9 (8):4071-4081.
[53]Chen, Y.; Zhang, R.; Song, Y.; He, J.; Sun, J.; Bai, J.; An, Z.; Dong, L.; Zhan, Q.; Abliz, Z. RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network:finding potential biomarkers for breast cancer [J]. Analyst,2009,134 (10):2003-2011.
[54]Shen, G.; Chen, Y.; Sun, J.; Zhang, R.; Zhang, Y.; He, J.; Tian, Y.; Song, Y.; Chen, X.; Abliz, Z. Time-course changes in potential biomarkers detected using a metabonomic approach in Walker 256 tumor-bearing rats [J]. J. Proteome Res.,2011,10 (4),1953-1961.
[55]Xu, J.; Chen, Y.; Zhang, R.; Song, Y.; Cao, J.; Bi, N.; Wang, J.; He, J.; Bai, J.; Dong, L.; Wang, L.; Zhan, Q.; Abliz, Z. Global and Targeted Metabolomics of Esophageal Squamous Cell Carcinoma Discovers Potential Diagnostic and Therapeutic Biomarkers [J]. Mol. Cell Proteomics 2013, PMID:23397110.
[56]Huang, J.; Sun, J.; Chen, Y.; Song, Y.; Dong, L.; Zhan, Q.; Zhang, R.; Abliz, Z. Analysis of multiplex endogenous estrogen metabolites in human urine using ultra-fast liquid chromatography-tandem mass spectrometry:a case study for breast cancer [J]. Anal. Chim. Acta., 2012,711:60-68.
[57]Jemal, A.; Bray, F.; Center, M. M.; Ferlay, J.; Ward, E.; Forman, D. Global cancer statistics [J]. CA Cancer J. Clin.,2011,61 (2):69-90.
[58]陈万青,张思维,郑荣寿,雷正龙,李光琳,邹小农,赵平.中国肿瘤登记地区2007年肿瘤发病和死亡分析[J].中国肿瘤,2011,20(3)：162-169.
[59]陈万青,郑荣寿,张思维,王宁,杨雷,赫捷.2003.2007年中国肺癌发病与死亡分析[J].实用肿瘤学杂志,2012,26(1)：6-10.
[60]Granville, C. A.; Dennis, P. A. An overview of lung cancer genomics and proteomics [J]. Am. J. Respir. Cell. Mol. Biol,2005,32 (3):169-176.
[61]Vineis, P. Environmental tobacco smoke and risk of respiratory cancer and chronic obstructive pulmonary disease in former smokers and never smokers in the EPIC prospective study [J]. BMJ, 2005,330 (7486),277-282.
[62]Vineis, P.; Alavanja, M.; Buffler, P.; Fontham, E.; Franceschi, S.; Gao, Y. T.; Gupta, P. C.; Hackshaw, A.; Matos, E.; Samet, J.; Sitas, F.; Smith, J.; Stayner, L.; Straif, K.; Thun, M. J.; Wichmann, H. E.; Wu, A. H.; Zaridze, D.; Peto, R.; Doll, R. Tobacco and Cancer:Recent Epidemiological Evidence [J]. J. Natl. Cancer Inst.,2004,96 (2):99-106.
[63]高国福,姚树祥,孙秀娣,常闰生,姜勇,罗学昌,晋萍,乔友林.云南锡矿工肺癌的队列研究[J].中国肺癌杂质,2002,5(2)：87-91.
[64]张国钦,王宁,王涛,祝伟星,曲宸绪,邢秀梅.北京市城区居民烟草消费和大气污染对肺癌死亡率的影响[J].环境与健康杂志,2009,(8)：666-669.
[65]Schuller, H. M.; Porter, B.; Riechert, A.; Walker, K.; Schmoyer, R. Neuroendocrine lung carcinogenesis in hamsters is inhibited by green tea or theophylline while the development of adenocarcinomas is promoted:implications for chemoprevention in smokers [J]. Lung Cancer, 2004,45(1):11-18.
[66]De Stefani, E.; Fontham, E. T.; Chen, V.; Correa, P.; Deneo-Pellegrini, H.; Ronco, A.; Mendilaharsu, M. Fatty foods and the risk of lung cancer:a case-control study from Uruguay [J]. Int. J. Cancer,1997,71 (5):760-766.
[67]Minna, J. D.; Roth, J. A.; Gazdar, A. F. Focus on lung cancer [J]. Cancer Cell,2002,1 (1):49-52.
[68]Goldstraw, P.; Crowley, J.; Chansky, K.; Giroux, D. J.; Groome, P. A.; Rami-Porta, R.; Postmus, P. E.; Rusch, V.; Sobin, L. The IASLC Lung Cancer Staging Project:proposals for the revision of the TNM stage groupings in the forthcoming (seventh) edition of the TNM Classification of malignant tumours [J]. J. Thorac. Oncol.,2007,2 (8):706-714.
[69]Groome, P. A.; Bolejack, V.; Crowley, J. J.; Kennedy, C.; Krasnik, M.; Sobin, L. H.; Goldstraw, P. The IASLC Lung Cancer Staging Project:validation of the proposals for revision of the T, N, and M descriptors and consequent stage groupings in the forthcoming (seventh) edition of the TNM classification of malignant tumours [J]. J. Thorac. Oncol,2007,2 (8):694-705.
[70]Vallieres, E.; Shepherd, F. A.; Crowley, J.; Van Houtte, P.; Postmus, P. E.; Carney, D.; Chansky, K.; Shaikh, Z.; Goldstraw, P. The IASLC Lung Cancer Staging Project:proposals regarding the relevance of TNM in the pathologic staging of small cell lung cancer in the forthcoming (seventh) edition of the TNM classification for lung cancer [J]. J. Thorac. Oncol,2009,4 (9):1049-1059.
[71]Detterbeck, F. C. The New Lung Cancer Staging System [J]. CHEST,2009,136 (1):260-271.
[72]Hang, Z. Q.; Zheng, M. F.; Huang, J. H. Detection and diagnostic value of serum carcinoembryonic antigen and cytokeratin 19 fragment in lung cancer patients [J]. Zhong hua Zhong Liu Za Zhi,2011,33 (11):847-849.
[73]Grunnet, M.; Sorensen, J. B. Carcinoembryonic antigen (CEA) as tumor marker in lung cancer [J]. Lung Cancer,2012,76 (2):138-143.
[74]Pavicevic, R.; Bubanovic, G.; Franjevic, A.; Stancic-Rokotov, D.; Samarzija, M. CYFRA21-1 in non-small cell lung cancer--standardisation and application during diagnosis [J]. Coll. Antropol., 2008,32 (2):485-498.
[75]Niklinski, J.; Furman, M.; Chyczewski, L.; Chyczewska, E.; Rogowski, F.; Jaroszewicz, E. Cyfra 21-1 new marker for non-small cell lung cancer [J]. Pneumonol. Alergol. Pol,1994,62 (5-6): 227-232.
[76]Ferrigno, D.; Buccheri, G.; Giordano, C. Neuron-specific enolase is an effective rumour marker in non-small cell lung cancer (NSCLC) [J]. Lung Cancer,2003,41 (3):311-320.
[77]Holdenrieder, S.; Von Pawel, J.; Duell, T.; Feldmann, K.; Raith, H.; Schollen, A.; Nagel, D.; Stieber, P. Clinical relevance of thymidine kinase for the diagnosis, therapy monitoring and prognosis of non-operable lung cancer [J]. Anticancer Res.,2010,30 (5):1855-1862.
[78]Chu, X. Y.; Hou, X. B.; Song, W. A.; Xue, Z. Q.; Wang, B.; Zhang, L. B. Diagnostic values of SCC, CEA, Cyfra21-1 and NSE for lung cancer in patients with suspicious pulmonary masses:a single center analysis [J]. Cancer Biol. Then,2011,11 (12):995-1000.
[79]Song, W. A.; Liu, X.; Tian, X. D.; Wang, W.; Liang, C. Y.; Zhang, T.; Guo, J. T.; Peng, Y. H.; Zhou, N. K. Utility of squamous cell carcinoma antigen, carcinoembryonic antigen, Cyfra 21-1 and neuron specific enolase in lung cancer diagnosis:a prospective study from China [J]. Chin. Med. J (Engl),2011,124 (20):3244-3248.
[80]Xue, X.; Xue, Q.; Wang, N.; Zhang, L.; Guo, L.; Li, X.; Sun, J; Wang, J. Early clinical diagnosis of synchronous multiple primary lung cancer [J]. Oncol. Lett.,2012,3(1):234-237.
[81]Lee, G.; Ge, B.; Huang, T. K.; Zheng, G.; Duan, J.; Wang, I. H. Positive identification of CA215 pan cancer biomarker from serum specimens of cancer patients [J]. Cancer Biomark,2010,6 (2): 111-117.
[82]Jordan, K. W.; Adkins, C. B.; Su, L.; Halpern, E. F.; Mark, E. J.; Christiani, D. C.; Cheng, L. L. Comparison of squamous cell carcinoma and adenocarcinoma of the lung by metabolomic analysis of tissue-serum pairs [J]. Lung Cancer,2010,68 (1):44-50.
[83]Maeda, J.; Higashiyama, M.; Imaizumi, A.; Nakayama, T.; Yamamoto, H.; Daimon, T.; Yamakado, M.; Imamura, F.; Kodama, K. 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 [J]. BMC Cancer,2010,10:690-698.
[84]Hori, S.; Nishiumi, S.; Kobayashi, K.; Shinohara, M.; Hatakeyama, Y.; Kotani, Y.; Hatano, N.; Maniwa, Y.; Nishio, W.; Bamba, T.; Fukusaki, E.; Azuma, T.; Takenawa, T.; Nishimura, Y.; Yoshida, M. A metabolomic approach to lung cancer [J]. Lung Cancer,2011,74 (2):284-292.
[85]Rocha, C. M.; Carrola, J.; Barros, A. S.; Gil, A. M.; Goodfellow, B. J.; Carreira, I. M.; Bernardo, J.; Gomes, A.; Sousa, V.; Carvalho, L.; Duarte, I. F. Metabolic signatures of lung cancer in biofluids:NMR-based metabonomics of blood plasma [J]. J. Proteome Res.,2011,10 (9): 4314-4324.
[86]Niu, Y.; Jiang, Y.; Xu, C.; Wang, X.; Liu, Y.; Zhao, H.; Han, B.; Jiang, L. Preliminary results of metabolite in serum and urine of lung cancer patients detected by metabolomics [J]. Zhong guo Fei Ai Za Zhi,2012,15(4):195-201.
[87]Lokhov, P. G.; Trifonova, O. P.; Maslov, D. L.; Archakov, A. I. Blood plasma metabolites and the risk of developing lung cancer in Russia [J]. Eur. J. Cancer Prev.,2012:1-7.
[88]王文昭,赵欣捷,李响,陈静,李方楼,许国旺.尿中修饰核苷代谢轮廓分析在肺癌诊断中的作用[J].中国医学科学院学报,2007,29(6)：738-741.
[89]Yang, Q.; Shi, X.; Wang, Y.; Wang, W.; He, H.; Lu, X.; Xu, G. Urinary metabonomic study of lung cancer by a fully automatic hyphenated hydrophilic interaction/RPLC-MS system [J]. J. Sep. Sci.,2010,33 (10):1495-1503.
[90]Carrola, J.; Rocha, C. M.; Barros, A. S.; Gil, A. M.; Goodfellow, B. J.; Carreira, I. M.; Bernardo, J.; Gomes, A.; Sousa, V; Carvalho, L.; Duarte, I. F. Metabolic signatures of lung cancer in biofluids:NMR-based metabonomics of urine [J]. J. Proteome Res.,2011,10 (1):221-230.
[91]Fan, T. W.; Lane, A. N.; Higashi, R. M.; Farag, M. A.; Gao, H.; Bousamra, M.; Miller, D. M. Altered regulation of metabolic pathways in human lung cancer discerned by (13)C stable isotope-resolved metabolomics (SIRM) [J]. Mol. Cancer,2009,8:41-60.
[92]Rocha, C. M.; Barros, A. S.; Gil, A. M.; Goodfellow, B. J.; Humpfer, E.; Spraul, M.; Carreira, I. M.; Melo, J. B.; Bernardo, J.; Gomes, A.; Sousa, V.; Carvalho, L.; Duarte, I. F. Metabolic profiling of human lung cancer tissue by'H high resolution magic angle spinning (HRMAS) NMR spectroscopy [J]. J. Proteome Res.,2010,9 (1):319-332.
[1]Bernini, P.; Bertini, I.; Luchinat, C.; Nincheri, P.; Staderini, S.; Turano, P. Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks [J]. J. Biomol. NMR,2011,49 (3-4):231-243.
[2]Dettmer, K.; Aronov, P. A.; Hammock. B. D. Mass spectrometry-based metabolomics [J]. Mass Spectrom. Rev.,2007,26 (1):51-78.
[3]Weiss, R.H; Kim, K. Metabolomics in the study of kidney diseases [J]. Nat. Rev. Nephroi,2012, 8:22-33.
[4]Sreekumar, A.; Poisson, L. M.; Rajendiran, T. M.; Khan, A. P.; Cao, Q.; Yu, J.; Laxman, B.; Mehra, R.; Lonigro, R. J.; Li, Y.; Nyati, M. K.; Ahsan, A.; Kalyana-Sundaram, S.; Han, B.; Cao, X.; Byun, J.; Omenn, G. S.; Ghosh, D.; Pennathur, S.; Alexander, D. C.; Berger, A.; Shuster, J. R.; Wei, J. T.; Varambally, S.; Beecher, C.; Chinnaiyan, A. M. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression [J]. Nature,2009,457 (7231):910-914.
[5]Gillett, M. P. Comparative studies of the lecithin:cholesterol acyltransferase reaction in the plasma of reptiles and amphibians [J]. Scand. J. Clin. Lab. Invest, Suppl 1978,150:32-39.
[6]An, Z.; Chen, Y; Zhang, R.; Song, Y; Sun, J.; He, J.; Bai, J.; Dong, L.; Zhan, Q.; Abliz, Z. Integrated ionization approach for RRLC-MS/MS-based metabonomics:finding potential biomarkers for lung cancer [J]. J. Proteome Res.,2010,9 (8):4071-4081.
[7]Yoshida, T.; Majors, R. E. High-speed analyses using rapid resolution liquid chromatography on 1.8-microm porous particles [J]. J. Sep. Sci.,2006,29 (16):2421-2432.
[8]Wang, Y.; Wang J. S.; Yao, M.; Zhao, X. J.; Fritsche, J.; Schmitt-Kopplin, P.; Cai, Z. W.; Wan, D. F.; Lu, X.; Yang S. L.; Gu, J. R.; Haring, H. U.;Schleicher, E. D.; Lehmann, R.; Xu, G. W. Metabonomics Study on the Effects of the Ginsenoside Rg3 in a β-Cyclodextrin-Based Formulation on Tumor-Bearing Rats by a Fully Automatic Hydrophilic Interaction/Reversed-Phase Column-Switching HPLC-ESI-MS Approach [J]. Anal. Chem.,2008, 80:4680-4688.
[1]Sreekumar, A.; Poisson, L. M.; Rajendiran, T. M.; Khan, A. P.; Cao, Q.; Yu, J.; Laxman, B.; Mehra, R.; Lonigro, R. J.; Li, Y.; Nyati, M. K.; Ahsan, A.; Kalyana-Sundaram, S.; Han, B.; Cao, X.; Byun, J.; Omenn, G. S.; Ghosh, D.; Pennathur, S.; Alexander, D. C.; Berger, A.; Shuster, J. R.; Wei, J. T.; Varambally, S.; Beecher, C; Chinnaiyan, A. M. Metabolomic profiles delineate potential role forsarcosine in prostate cancer progression [J].Nature,2009,457 (7231):910-914.
[2]Dunn, W. B.; Broadhurst, D.; Begley, P.; Zelena, E.; Francis-Mclntyre, S.; Anderson, N.; Brown, M.; Knowles, J. D.; Halsall, A.; Haselden, J. N.; Nicholls, A. W.; Wilson, I. D.; Kell, D. B.; Goodacre, R. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry [J]. Nat. Protoc.,2011, 6(7):1060-1083.
[3]Chen, J.; Zhang, X.; Cao, R.; Lu, X.; Zhao, S.; Fekete, A.; Huang, Q.; Schmitt-Kopplin, P.; Wang, Y; Xu, Z.; Wan, X.; Wu, X.; Zhao, N.; Xu, C.; Xu, G. Serum 27-nor-5β-Cholestane-3,7,12,24,25 Pentol Glucuronide Discovered by Metabolomics as Potential Diagnostic Biomarker for Epithelium Ovarian Cancer [J]. J. Proteome Res.,2011,10 (5):2625-2632.
[4]Guo, X. Z.; Qiu, L. Investigation and Improvement of Workflow in Biochemistry Laboratory [J]. J. Mod. Lab. Med,2007,22 (4):100-101.
[5]Zwart, S. R.; Wolf, M.; Rogers, A.; Rodgers, S.; Gillman, P. L.; Hitchcox, K.; Ericson, K. L. Smith, S. M. Stability of analytes related to clinical chemistry and bone metabolism in blood specimens after delayed processing [J]. Clin. Biochem.,2009,42 (9):907-910.
[6]Rosenling, T.; Slim, C. L.; Christin, C.; Coulier, L.; Shi, S.; Stoop, M. P.; Bosman, J.; Suits, F.; Horvatovich, P. L.; Stockhofe-Zurwieden, N.; Vreeken, R.; Hankemeier, T.; van Gool, A. J.; Luider, T. M.; Bischoff, R. The Effect of Preanalytical Factors on Stability of the Proteome and Selected Metabolites in Cerebrospinal Fluid (CSF) [J]. J. Proteome Res.,2009,8:5511-5522.
[7]Fingerhut, R.; Ensenauer R.; ROschinger, W.; Arnecke, R.; OlgemOller, B.; Roscher, A. A. Stability of Acylcarnitines and Free Carnitine in Dried Blood Samples:Implications for Retrospective Diagnosis of Inborn Errors of Metabolism and Neonatal Screening for Carnitine Transporter Deficiency [J]. Anal. Chem.,2009,81:3571-3575.
[8]Mancinelli, A.; Iannoni, E.; Duran, M.; Calvani, M. Effect of temperature on the stability of long-chain acylcarnitines in human blood prior to plasma separation [J]. Clinica. Chimica. Acta., 2007,375(1-2):169-170.
[9]Maslanka, K.; Smolenska-Sym, G.; Michur, H.; Wrobel, A.; Lachert, E.; Brojer, E. Lysophosphatidylcholines:Bioactive Lipids Generated During Storage of Blood Components [J]. Arch. Immunol. Ther. Ex.,2011,60 (1),55-60.
[10]Vlaar, A. P. J.; Kulik, W.; Nieuwland, R.; Peters, C. P.; Tool, A. T. J.; van Bruggen, R.; Juffermans, N. P.; de Korte, D. Accumulation of bioactive lipids during storage of blood products is not cell but plasma derived and temperature dependent [J]. Transfusion,2011,51 (11): 2358-2366.
[11]Hustad, S.; Eussen, S.; Midttun, O.; Ulvik, A.; van de Kant, P. M.; Morkrid, L.; Gislefoss, R.; Ueland, P. M. Kinetic Modeling of Storage Effects on Biomarkers Related to B Vitamin Status and One-Carbon Metabolism [J]. Clin. Chem.,2011,58 (2):402-410.
[12]Drammeh, B. S.; Schleicher, R. L.; Pfeiffer, C. M.; Jain, R. B.; Zhang, M.; Nguyen, P. H. Effects of delayed sample processing and freezing on serum concentrations of selected nutritional indicators [J]. Clin Chem.,2008,54 (11):1883-1891.
[13]Bernini, P.; Bertini, I.; Luchinat, C.; Nincheri, P.; Staderini, S.; Turano, P. Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks [J]. J. Biomol. NMR,2011,49 (3-4):231-243.
[14]Gika, H. G.; Theodoridis, G. A.; Wilson, I. D. Liquid chromatography and ultra-performance liquid chromatography-mass spectrometry fingerprinting of human urine [J]. J. Chromatogr. A, 2008,1189 (1-2):314-322.
[15]Lauridsen, M.; Hansen, S. H.; Jaroszewski, J. W.; Cornett, C. Human Urine as Test Material in 1H NMR-Based Metabonomics:Recommendations for Sample Preparation and Storage [J]. Anal. Chem.,2007,79:1181-1186.
[16]Oddoze, C.; Lombard, E.; Portugal, H. Stability study of 81 analytes in human whole blood, in serum and in plasma [J]. Clin. Biochem.,2012,45 (6):464-469.
[17]Bobby L.; Boyanton, J.; Kenneth, E. B. stability studies of tewnty-four ananlytes in human plasma ans serum [J]. Clin. Biochem.,2002,48 (12):2242-2247.
[18]Wiklund, S.; Johansson, E.; Sjostrom, L.; Mellerowicz, E. J.; Edlund, U.; Shockcor, J. P.; Gottfries, J.; Moritz, T.; Trygg, J. 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.
[19]Glomset, J. A. The plasma lecithin:cholesterol acyltransferase reaction [J]. J. Lipid Res.,1968,9: 155-167.
[20]Percy, A. K.; Schmell, E.; Earles, B. J.; Lennarz, W. J. Phospholipid biosynthesis in the membranes of immature and mature red blood cells [J]. Biochemistry,1973,12 (13):2456-2461.
[21]Gevi, F.; D'Alessandro, A.; Rinalducci, S.; Zolla, L. Alterations of red blood cell metabolome during cold liquid storage of erythrocyte concentrates in CPD-SAGM [J]. J. Proteomics,2012, 76:168-180.
[22]Grant, R. A.; Zucker, M. B. Altered uptake and release of I4C-serotonin in platelets following incubation of platelet-rich plasma with EDTA [J]. Thromb. Res.,1979,14 (6):981-986.
[23]Kuci, Z.; Hins, J.; Kuci, S.; Renner, S.; Flottmann, D.; Bruchelt, G. Determination of glucose metabolites in stored erythrocytes and in erythrocytes from patients with thalassemia by analytical isotachophoresis [J]. J. Biochem. Biophys. Methods,2006,69 (1-2):79-87.
[24]de los Reyes, B.; Navarro, J. A.; Perez-Garcia, R.; Liras, A.; Campos, Y.; Bornstein, B.; Arenas, J. Effects of L-carnitine on erythrocyte acyl-CoA, free CoA, and glycerophospholipid acyltransferase in uremia [J]. Am. J. Clin. Nutr.,1998,67 (3):386-390.
[25]Borum, P. R. Plasma carnitine compartment and red blood cell carnitine compartment of healthy adults [J]. Am. J. Clin. Nutr.,1987,46 (3):437-441.
[26]Fox, I. H.; Burk, L.; Planet, G.; Goren, M.; Kaminska, J. Pyrimidine nucleotide biosynthesis. A study of normal and purine enzyme-deficient cells [J]. J. Biol. Chem.,1978,253 (19): 6794-6800.
[27]Norlin, M.; Wikvall, K. Enzymes in the conversion of cholesterol into bile acids [J]. Curr. Mol. Med.,2007,7 (2):199-218.
[1]Granville, C. A.; Dennis, P. A. An overview of lung cancer genomics and proteomics [J]. Am. J. Respir. CellMol. Biol,2005,32 (3):169-176.
[2]Wiklund, S.; Johansson, E.; Sjostrom, L.; Mellerowicz, E. J.; Edlund, U.; Shockcor, J. P.; Gottfries, J.; Moritz, T.; Trygg, J. 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.
[3]Werner, E.; Croixmarie, V.; Umbdenstock, T.; Ezan, E.; Chaminade, P.; Tabet, J. C.; Junot, C. Mass spectrometry-based metabolomics:accelerating the characterization of discriminating signals by combining statistical correlations and ultrahigh resolution [J]. Anal. Chem.,2008,80 (13):4918-4932.
[4]Kuhl, C.; Tautenhahn, R.; Bottcher, C.; Larson, T. R.; Neumann, S. CAMERA:an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets [J]. Anal. Chem.,2012,84 (1):283-289.
[5]Chen, Y.; Zhang, R.; Song, Y.; He, J.; Sun, J.; Bai, J.; An, Z.; Dong, L.; Zhan, Q.; Abliz, Z. RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network:finding potential biomarkers for breast cancer [J]. Analyst,2009,134 (10):2003-2011.
[6]van den Berg, R. A.; Hoefsloot, H. C.; Westerhuis, J. A.; Smilde, A. K.; van der Werf, M. J. Centering, scaling, and transformations:improving the biological information content of metabolomics data [J]. BMC Genomics,2006,7:142-157.
[7]Eriksson, L.; Johansson; E.Wold, N. K. Multi-and Megavariate Data Analysis:Principles and Applications [M]. Sweden:Umetrics Academy,2001:39-62.
[8]沈国庆.基于动物肿瘤模型的LC-MS/MS代谢组学及药物代谢组学方法研究[D].北京：北京协和医学院,2011：
[9]安卓玲.组合式离子化方式的RRLC-MS/MS分析方法及其肺癌的代谢组学研究[D].北京：北京协和医学院,2010：
[1]Zhu, Z. J.; Schultz, A. W.; Wang, J.; Johnson, C. H.; Yannone, S. M.; Patti, G. J.; Siuzdak, G. Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLEN database [J]. Nat. Protoc.,2013,8 (3):451-460.
[2]Theodoridis, G. A.; Gika, H. G.; Want, E. J.; Wilson, I. D. Liquid chromatography-mass spectrometry based global metabolite profiling:a review [J]. Anal. Chim. Acta.,2012,711:7-16.
[3]Sachan, D. S.; Dodson, W. L. The serum carnitine status of cancer patients [J]. J. Am. Coll. Nutr., 1987,6(2):145-150.
[4]Sewell, A. C.; Bohles, H. J. Acylcarnitines in intermediary metabolism [J]. Eur. J. Pediatr.,1995, 154 (11):871-877.
[5]Chen, J.; Wang, W.; Lv, S.; Yin, P.; Zhao, X.; Lu, X.; Zhang, F.; Xu, G. 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 (1):3-9.
[6]石如玲,姜玲玲.过氧化物酶体脂肪酸p氧化[J].中国生物化学与分子生物学报2009,25(1)：12-16.
[7]Moore, K. H.; Radloff, J. F.; Koen, A. E.; Hull, F. E. Incomplete fatty acid oxidation by heart mitochondria:beta-hydroxy fatty acid production [J]. J. Mol. Cell. Cardiol.,1982,14 (8): 451-459.
[2]Nicholson, J. K.; D.Wilson, I. Understanding'Global'Systems Biology:Metabonomics and the Continuum of Metabolism [J]. Nat. Rev. DrugDiscov.,2003,2:668-676.
[3]Bu, Q.; Huang, Y.; Yan, G.; Cen, X.; Zhao, Y. L. Metabolomics:a revolution for novel cancer marker identification [J]. Comb. Chem. High Throughput Screen.,2012,15 (3):266-275.
[4]Fiehn, O. Metabolomics-the link between genotypes and phenotypes [J]. Plant Mol. Biol.,2002, 48:155-171.
[5]Hall, R.; Beale, M.; Fiehn, O.; Hardy, N.; Sumner, L.; Bino, R. Plant Metabolomics:The Missing Link in Functional Genomics Strategies [J]. The Plant Cell,2002,14:1437-1440.
[6]Nicholson, J. K.; Lindon, J. C.; Holmes, E. Metabonomies understaniding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data [J]. Xenobiotica,1999,29:1181-1189.
[7]Nicholson, J. K.; Connelly, J.; Lindon, J. C.; Holmes, E. Metabonomics:a platform for studying drug toxicity and gene function [J]. Nat. Rev. Drug Discov.,2002,1:152-161.
[8]Bernini, P.; Bertini, I.; Luchinat, C.; Nincheri, P.; Staderini, S.; Turano, P. Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks [J]. J. Biomol. NMR,2011,49 (3-4):231-243.
[9]Dettmer, K.; Aronov, P. A.; Hammock. B. D. Mass spectrometry-based metabolomics [J]. Mass Spectrom. Rev.,2007,26 (1):51-78.
[10]Weiss, R.H; Kim, K. Metabolomics in the study of kidney diseases [J]. Nature Reviews Nephrology,2012,8,22-33.
[11]Ceglarek, U.; Leichtle, A.; Brugel, M.; Kortz, L.; Brauer, R.; Bresler, K.; Thiery, J.; Fiedler, G. M. Challenges and developments in tandem mass spectrometry based clinical metabolomics [J]. Mol. Cell. Endocrinol,2009,301 (1-2):266-271.
[12]Fardet, A.; Llorach, R.; Martin, J. F.; Besson, C.; Lyan, B.; Pujos-Guillot, E.; Scalbert, A. 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.
[13]Bobeldijk, I.; Hekman, M.; de Vries-van der Weij, J.; Coulier, L.; Ramaker, R.; Kleemann, R.; Kooistra, T.; Rubingh, C.; Freidig, A.; Verheij, E. Quantitative profiling of bile acids in biofluids and tissues based on accurate mass high resolution LC-FT-MS:compound class targeting in a metabolomics workflow [J]. J. Chromatogr. B,2008,871 (2):306-313.
[14]Harrigan, G. G.; LaPlante, R. H.; Cosma, G. N.; Cockerell, G.; Goodacre, R.; Maddox, J. F; Luyendyk, J. P.; Ganey, P. E.; Roth, R. A. 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.
[15]Mishur, R. J.; Rea, S. L. Applications of mass spectrometry to metabolomics and metabonomics: Detection of biomarkers of aging and of age-related diseases [J]. Mass Spectrom. Rev.,2012,31 (1):70-95.
[16]An, Z. L.; Chen, Y. H.; Zhang, R. P.; Song, Y. M.; Sun, J. H.; He, J. M.; Bai, J. F; Dong, L. J.; Zhan, Q. M.; Abliz, Z. Integrated Ionization Approach for RRLC-MS/MS-based Metabonomics: Finding Potential Biomarkers for Lung Cancer [J]. J. Proteome Res.,2010,9:4071-4081.
[17]Spagou, K.; Theodoridis, G.; Wilson, I.; Raikos, N.; Greaves, P.; Edwards, R.; Nolan, B.; Klapa, M. I. A GC-MS metabolic profiling study of plasma samples from mice on low-and high-fat diets [J]. J. Chromatogr. B,2011,879 (17-18):1467-1475.
[18]Koh, Y.; Pasikanti, K. K.; Yap, C. W.; Chan, E. C. Comparative evaluation of software for retention time alignment of gas chromatography/time-of-flight mass spectrometry-based metabonomic data [J]. J. Chromatogr. A,2010,1217 (52):8308-8316.
[19]Nicholson, J. K.; Lindon, J. C. Metabonomics [J]. Nature,2008,455:1054-1056.
[20]Cho, S. H.; Jung, B. H.; Lee, S. H.; Lee, W. Y; Kong, G.; Chung, B. C. Direct determination of nucleosides in the urine of patients with breast cancer using column-switching liquid chromatography-tandem mass spectrometry [J]. Biomed. Chromatogr.,2006,20 (11):1229-1236.
[21]Cubbon, S.; Antonio, C.; Wilson, J.; Thomas-Oates, J. Metabolomic applications of HILIC-LC-MS [J]. Mass Spectrom. Rev.,2010,29 (5):671-684.
[22]Guo, Y; Gaiki, S. Retention and selectivity of stationary phases for hydrophilic interaction chromatography [J].J. Chromatogr. A,2011,1218 (35):5920-5938.
[23]Shulaev, V. Metabolomics technology and bioinformatics [J]. Brief. Bioinform.,2006,7 (2): 128-139.
[24]Yao, H.; Shi, P.; Zhang, L.; Fan, X.; Shao, Q.; Cheng, Y. Untargeted metabolic profiling reveals potential biomarkers in myocardial infarction and its application [J]. Mol. BioSyst.,2010,6 (6): 1061-1070.
[25]Katajamaa, M; Miettinen, J.; Oresic, M. MZmine:toolbox for processing and visualization of mass spectrometry based molecular profile data [J]. Bioinformatics,2006,22 (5):634-636.
[26]Pluskal, T.; Castillo, S.; Villar-Briones, A.; Oresic, M. MZmine 2:Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data [J]. BMC Bioinformatics,2010,11 (1):395-406.
[27]Bijlsma, S.; Bobeldijk, I.; Verheij, E. R.; Ramaker, R.; Kochhar, S.; Macdonald, I. A.; van Ommen, B.; Smilde, A. K. Large-scale human metabolomics studies:a strategy for data (pre-) processing and validation [J]. Anal. Chem.,2006,78 (2):567-574.
[28]Idborg-Bjorkman, H.; Edlund, P. O.; Kvalheim, O. M.; Schuppe-Koistinen, I.; Jacobsson, S. P. Screening of biomarkers in rat urine using LC/electrospray ionization-MS and two-way data analysis [J]. Anal. Chem.,2003,75 (18):4784-4792.
[29]van den Berg, R. A.; Hoefsloot, H. C.; Westerhuis, J. A.; Smilde, A. K.; van der Werf, M. J. Centering, scaling, and transformations:improving the biological information content of metabolomics data [J]. BMC Genomics,2006,7:142-157.
[30]Trygg, J.; Holmes, E.; Lundstedt, T. Chemometrics in metabonomics [J]. J. Proteome Res.,2007, 6 (2):469-479.
[31]Eriksson, L.; Antti, H.; Gottfries, J.; Holmes, E.; Johansson, E.; Lindgren, F.; Long, L; Lundstedt, T.; Trygg, J.; Wold, S. Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm) [J]. Anal. Bioanal. Chem.,2004,380 (3):419-429.
[32]Geng, L.; Sun, H.; Yuan, Y.; Liu, Z.; Cui, Y.; Bi, K.; Chen, X. Discrimination of raw and vinegar-processed Genkwa Flos using metabolomics coupled with multivariate data analysis:A discrimination study with metabolomics coupled with PCA [J]. Fitoterapia,2012,84:286-294.
[33]Issaq, H. J.; Nativ, O.; Waybright, T.; Luke, B.; Veenstra, T. D.; Issaq, E. J.; Kravstov, A.; Mullerad, M. 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.
[34]Keun, H. C.; Ebbels, T. M.; Bollard, M. E.; Beckonert, O.; Antti, H.; Holmes, E.; Lindon, J. C. Nicholson, J. K. Geometric trajectory analysis of metabolic responses to toxicity can define treatment specific profiles [J]. Chem. Res. Toxicol,2004,17 (5):579-587.
[35]Lindon, J. C.; Nicholson, J. K.; Holmes, E. The Handbook of Metabonomics and Metabolomics [M]. Amsterdam:Elsevier,2007.
[36]Sreekumar, A.; Poisson, L. M.; Rajendiran, T. M.; Khan, A. P.; Cao, Q.; Yu, J.; Laxman, B.; Mehra, R.; Lonigro, R. J.; Li, Y.; Nyati, M. K.; Ahsan, A.; Kalyana-Sundaram, S.; Han, B.; Cao, X.; Byun, J.; Omenn, G. S.; Ghosh, D.; Pennathur, S.; Alexander, D. C.; Berger, A.; Shuster, J. R.; Wei, J. T.; Varambally, S.; Beecher, C.; Chinnaiyan, A. M. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression [J]. Nature,2009,457 (7231):910-914.
[37]Yang, J.; Xu, G.; Hong, Q.; Liebich, H. M.; Lutz, K.; Schmulling, R. M.; Wahl, H. G. Discrimination of Type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles [J]. J. Chromatogr. B,2004,813 (1-2):53-58.
[38]Yang, J.; Xu, G.; Zheng, Y.; Kong, H.; Pang, T.; Lv, S.; Yang, Q. 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.
[39]Li, H.; Shi, X.; Qiao, L.; Lu, X.; Xu, G. Synthesis of a new type of echinus-like Fe3O4@TiO2 core-shell-structured microspheres and their applications in selectively enriching phosphopeptides and removing phospholipids [J]. J. Chromatogr. A,2013,1275:9-16.
[40]Yang, H.; Pang, W.; Lu, H.; Cheng, D.; Yan, X.; Cheng, Y; Jiang, Y. Comparison of metabolic profiling of cyanidin-3-O-galactoside and extracts from blueberry in aged mice [J]. J. Agric. Food Chem.,2011,59 (5):2069-2076.
[41]Lu, C.; Wang, Y; Sheng, Z.; Liu, G.; Fu, Z.; Zhao, J.; Yan, X.; Zhu, B.; Peng, S. NMR-based metabonomic analysis of the hepatotoxicity induced by combined exposure to PCBs and TCDD in rats [J]. Toxicol. Appl. Pharmacol.,2010,248 (3),178-184.
[42]Jiang, L.; Huang, J.; Wang, Y; Tang, H. Eliminating the dication-induced intersample chemical-shift variations for NMR-based biofluid metabonomic analysis [J]. Analyst,2012,137 (18):4209-4019.
[43]Zhao, X. J.; Hao, F.; Huang, C.; Rantalainen, M.; Lei, H.; Tang, H.; Wang, Y. Systems responses of rats to mequindox revealed by metabolic and transcriptomic profiling [J]. J. Proteome. Res., 2012,11 (9):4712-4721.
[44]Zhang, Z. M.; Liang, Y Z.; Lu, H. M.; Tan, B. B.; Xu, X. N.; Ferro, M. Multiscale peak alignment for chromatographic datasets [J]. J. Chromatogr. A,2012,1223:93-106.
[45]Cao, D. S.; Zeng, M. M.; Yi, L. Z.; Wang, B.; Xu, Q. S.; Hu, Q. N.; Zhang, L. X.; Lu, H. M.; Liang, Y. Z. A novel kernel Fisher discriminant analysis:constructing informative kernel by decision tree ensemble for metabolomics data analysis [J]. Anal. Chim. Acta.,2011,706 (1), 97-104.
[46]Cong, W.; Liang, Q.; Li, L.; Shi, J.; Liu, Q.; Feng, Y.; Wang, Y.; Luo, G. Metabonomic study on the cumulative cardiotoxicity of a pirarubicin liposome powder [J]. Talanta,2012,89:91-98.
[47]Zhao, T.; Zhang, H.; Zhang, X.; Lu, J.; Yin, T.; Liang, Q.; Wang, Y; Luo, G.; Lan, H.; Li, P. Intrarenal metabolomics reveals the association of local organic toxins with the progression of diabetic kidney disease [J]. J. Pharm. Biomed. Anal.,2012,60:32-43.
[48]Li, X.; Chen, H.; Jia, W.; Xie, G. A metabolomics-based strategy for the quality control of traditional chinese medicine:shengmai injection as a case study [J]. Evid. Based Complement Alternat. Med,2013,2013,1-8.
[49]Lu, J.; Xie, G.; Jia, W. Metabolomics in human type 2 diabetes research [J]. Front. Med.,2013,7 (1):4-13.
[50]陈艳华,张瑞萍,宋咏梅,董立佳,詹启敏,再帕尔·阿不力孜.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.
[51]徐婧,田亚平,陈艳华,张瑞萍,杨芬,宋咏梅,再帕尔·阿不力孜.代谢组学血浆样品前处理及其快速高分辨液相色谱-质谱分析方法研究[J].分析化学,2011,39(12)：1793-1797.
[52]An, Z.; Chen, Y.; Zhang, R.; Song, Y; Sun, J.; He, J.; Bai, J.; Dong, L.; Zhan, Q.; Abliz, Z. Integrated ionization approach for RRLC-MS/MS-based metabonomics:finding potential biomarkers for lung cancer [J]. J. Proteome Res.,2010,9 (8):4071-4081.
[53]Chen, Y.; Zhang, R.; Song, Y.; He, J.; Sun, J.; Bai, J.; An, Z.; Dong, L.; Zhan, Q.; Abliz, Z. RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network:finding potential biomarkers for breast cancer [J]. Analyst,2009,134 (10):2003-2011.
[54]Shen, G.; Chen, Y.; Sun, J.; Zhang, R.; Zhang, Y.; He, J.; Tian, Y.; Song, Y.; Chen, X.; Abliz, Z. Time-course changes in potential biomarkers detected using a metabonomic approach in Walker 256 tumor-bearing rats [J]. J. Proteome Res.,2011,10 (4),1953-1961.
[55]Xu, J.; Chen, Y.; Zhang, R.; Song, Y.; Cao, J.; Bi, N.; Wang, J.; He, J.; Bai, J.; Dong, L.; Wang, L.; Zhan, Q.; Abliz, Z. Global and Targeted Metabolomics of Esophageal Squamous Cell Carcinoma Discovers Potential Diagnostic and Therapeutic Biomarkers [J]. Mol. Cell Proteomics 2013, PMID:23397110.
[56]Huang, J.; Sun, J.; Chen, Y.; Song, Y.; Dong, L.; Zhan, Q.; Zhang, R.; Abliz, Z. Analysis of multiplex endogenous estrogen metabolites in human urine using ultra-fast liquid chromatography-tandem mass spectrometry:a case study for breast cancer [J]. Anal. Chim. Acta., 2012,711:60-68.
[57]Jemal, A.; Bray, F.; Center, M. M.; Ferlay, J.; Ward, E.; Forman, D. Global cancer statistics [J]. CA Cancer J. Clin.,2011,61 (2):69-90.
[58]陈万青,张思维,郑荣寿,雷正龙,李光琳,邹小农,赵平.中国肿瘤登记地区2007年肿瘤发病和死亡分析[J].中国肿瘤,2011,20(3)：162-169.
[59]陈万青,郑荣寿,张思维,王宁,杨雷,赫捷.2003.2007年中国肺癌发病与死亡分析[J].实用肿瘤学杂志,2012,26(1)：6-10.
[60]Granville, C. A.; Dennis, P. A. An overview of lung cancer genomics and proteomics [J]. Am. J. Respir. Cell. Mol. Biol,2005,32 (3):169-176.
[61]Vineis, P. Environmental tobacco smoke and risk of respiratory cancer and chronic obstructive pulmonary disease in former smokers and never smokers in the EPIC prospective study [J]. BMJ, 2005,330 (7486),277-282.
[62]Vineis, P.; Alavanja, M.; Buffler, P.; Fontham, E.; Franceschi, S.; Gao, Y. T.; Gupta, P. C.; Hackshaw, A.; Matos, E.; Samet, J.; Sitas, F.; Smith, J.; Stayner, L.; Straif, K.; Thun, M. J.; Wichmann, H. E.; Wu, A. H.; Zaridze, D.; Peto, R.; Doll, R. Tobacco and Cancer:Recent Epidemiological Evidence [J]. J. Natl. Cancer Inst.,2004,96 (2):99-106.
[63]高国福,姚树祥,孙秀娣,常闰生,姜勇,罗学昌,晋萍,乔友林.云南锡矿工肺癌的队列研究[J].中国肺癌杂质,2002,5(2)：87-91.
[64]张国钦,王宁,王涛,祝伟星,曲宸绪,邢秀梅.北京市城区居民烟草消费和大气污染对肺癌死亡率的影响[J].环境与健康杂志,2009,(8)：666-669.
[65]Schuller, H. M.; Porter, B.; Riechert, A.; Walker, K.; Schmoyer, R. Neuroendocrine lung carcinogenesis in hamsters is inhibited by green tea or theophylline while the development of adenocarcinomas is promoted:implications for chemoprevention in smokers [J]. Lung Cancer, 2004,45(1):11-18.
[66]De Stefani, E.; Fontham, E. T.; Chen, V.; Correa, P.; Deneo-Pellegrini, H.; Ronco, A.; Mendilaharsu, M. Fatty foods and the risk of lung cancer:a case-control study from Uruguay [J]. Int. J. Cancer,1997,71 (5):760-766.
[67]Minna, J. D.; Roth, J. A.; Gazdar, A. F. Focus on lung cancer [J]. Cancer Cell,2002,1 (1):49-52.
[68]Goldstraw, P.; Crowley, J.; Chansky, K.; Giroux, D. J.; Groome, P. A.; Rami-Porta, R.; Postmus, P. E.; Rusch, V.; Sobin, L. The IASLC Lung Cancer Staging Project:proposals for the revision of the TNM stage groupings in the forthcoming (seventh) edition of the TNM Classification of malignant tumours [J]. J. Thorac. Oncol.,2007,2 (8):706-714.
[69]Groome, P. A.; Bolejack, V.; Crowley, J. J.; Kennedy, C.; Krasnik, M.; Sobin, L. H.; Goldstraw, P. The IASLC Lung Cancer Staging Project:validation of the proposals for revision of the T, N, and M descriptors and consequent stage groupings in the forthcoming (seventh) edition of the TNM classification of malignant tumours [J]. J. Thorac. Oncol,2007,2 (8):694-705.
[70]Vallieres, E.; Shepherd, F. A.; Crowley, J.; Van Houtte, P.; Postmus, P. E.; Carney, D.; Chansky, K.; Shaikh, Z.; Goldstraw, P. The IASLC Lung Cancer Staging Project:proposals regarding the relevance of TNM in the pathologic staging of small cell lung cancer in the forthcoming (seventh) edition of the TNM classification for lung cancer [J]. J. Thorac. Oncol,2009,4 (9):1049-1059.
[71]Detterbeck, F. C. The New Lung Cancer Staging System [J]. CHEST,2009,136 (1):260-271.
[72]Hang, Z. Q.; Zheng, M. F.; Huang, J. H. Detection and diagnostic value of serum carcinoembryonic antigen and cytokeratin 19 fragment in lung cancer patients [J]. Zhong hua Zhong Liu Za Zhi,2011,33 (11):847-849.
[73]Grunnet, M.; Sorensen, J. B. Carcinoembryonic antigen (CEA) as tumor marker in lung cancer [J]. Lung Cancer,2012,76 (2):138-143.
[74]Pavicevic, R.; Bubanovic, G.; Franjevic, A.; Stancic-Rokotov, D.; Samarzija, M. CYFRA21-1 in non-small cell lung cancer--standardisation and application during diagnosis [J]. Coll. Antropol., 2008,32 (2):485-498.
[75]Niklinski, J.; Furman, M.; Chyczewski, L.; Chyczewska, E.; Rogowski, F.; Jaroszewicz, E. Cyfra 21-1 new marker for non-small cell lung cancer [J]. Pneumonol. Alergol. Pol,1994,62 (5-6): 227-232.
[76]Ferrigno, D.; Buccheri, G.; Giordano, C. Neuron-specific enolase is an effective rumour marker in non-small cell lung cancer (NSCLC) [J]. Lung Cancer,2003,41 (3):311-320.
[77]Holdenrieder, S.; Von Pawel, J.; Duell, T.; Feldmann, K.; Raith, H.; Schollen, A.; Nagel, D.; Stieber, P. Clinical relevance of thymidine kinase for the diagnosis, therapy monitoring and prognosis of non-operable lung cancer [J]. Anticancer Res.,2010,30 (5):1855-1862.
[78]Chu, X. Y.; Hou, X. B.; Song, W. A.; Xue, Z. Q.; Wang, B.; Zhang, L. B. Diagnostic values of SCC, CEA, Cyfra21-1 and NSE for lung cancer in patients with suspicious pulmonary masses:a single center analysis [J]. Cancer Biol. Then,2011,11 (12):995-1000.
[79]Song, W. A.; Liu, X.; Tian, X. D.; Wang, W.; Liang, C. Y.; Zhang, T.; Guo, J. T.; Peng, Y. H.; Zhou, N. K. Utility of squamous cell carcinoma antigen, carcinoembryonic antigen, Cyfra 21-1 and neuron specific enolase in lung cancer diagnosis:a prospective study from China [J]. Chin. Med. J (Engl),2011,124 (20):3244-3248.
[80]Xue, X.; Xue, Q.; Wang, N.; Zhang, L.; Guo, L.; Li, X.; Sun, J; Wang, J. Early clinical diagnosis of synchronous multiple primary lung cancer [J]. Oncol. Lett.,2012,3(1):234-237.
[81]Lee, G.; Ge, B.; Huang, T. K.; Zheng, G.; Duan, J.; Wang, I. H. Positive identification of CA215 pan cancer biomarker from serum specimens of cancer patients [J]. Cancer Biomark,2010,6 (2): 111-117.
[82]Jordan, K. W.; Adkins, C. B.; Su, L.; Halpern, E. F.; Mark, E. J.; Christiani, D. C.; Cheng, L. L. Comparison of squamous cell carcinoma and adenocarcinoma of the lung by metabolomic analysis of tissue-serum pairs [J]. Lung Cancer,2010,68 (1):44-50.
[83]Maeda, J.; Higashiyama, M.; Imaizumi, A.; Nakayama, T.; Yamamoto, H.; Daimon, T.; Yamakado, M.; Imamura, F.; Kodama, K. 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 [J]. BMC Cancer,2010,10:690-698.
[84]Hori, S.; Nishiumi, S.; Kobayashi, K.; Shinohara, M.; Hatakeyama, Y.; Kotani, Y.; Hatano, N.; Maniwa, Y.; Nishio, W.; Bamba, T.; Fukusaki, E.; Azuma, T.; Takenawa, T.; Nishimura, Y.; Yoshida, M. A metabolomic approach to lung cancer [J]. Lung Cancer,2011,74 (2):284-292.
[85]Rocha, C. M.; Carrola, J.; Barros, A. S.; Gil, A. M.; Goodfellow, B. J.; Carreira, I. M.; Bernardo, J.; Gomes, A.; Sousa, V.; Carvalho, L.; Duarte, I. F. Metabolic signatures of lung cancer in biofluids:NMR-based metabonomics of blood plasma [J]. J. Proteome Res.,2011,10 (9): 4314-4324.
[86]Niu, Y.; Jiang, Y.; Xu, C.; Wang, X.; Liu, Y.; Zhao, H.; Han, B.; Jiang, L. Preliminary results of metabolite in serum and urine of lung cancer patients detected by metabolomics [J]. Zhong guo Fei Ai Za Zhi,2012,15(4):195-201.
[87]Lokhov, P. G.; Trifonova, O. P.; Maslov, D. L.; Archakov, A. I. Blood plasma metabolites and the risk of developing lung cancer in Russia [J]. Eur. J. Cancer Prev.,2012:1-7.
[88]王文昭,赵欣捷,李响,陈静,李方楼,许国旺.尿中修饰核苷代谢轮廓分析在肺癌诊断中的作用[J].中国医学科学院学报,2007,29(6)：738-741.
[89]Yang, Q.; Shi, X.; Wang, Y.; Wang, W.; He, H.; Lu, X.; Xu, G. Urinary metabonomic study of lung cancer by a fully automatic hyphenated hydrophilic interaction/RPLC-MS system [J]. J. Sep. Sci.,2010,33 (10):1495-1503.
[90]Carrola, J.; Rocha, C. M.; Barros, A. S.; Gil, A. M.; Goodfellow, B. J.; Carreira, I. M.; Bernardo, J.; Gomes, A.; Sousa, V; Carvalho, L.; Duarte, I. F. Metabolic signatures of lung cancer in biofluids:NMR-based metabonomics of urine [J]. J. Proteome Res.,2011,10 (1):221-230.
[91]Fan, T. W.; Lane, A. N.; Higashi, R. M.; Farag, M. A.; Gao, H.; Bousamra, M.; Miller, D. M. Altered regulation of metabolic pathways in human lung cancer discerned by (13)C stable isotope-resolved metabolomics (SIRM) [J]. Mol. Cancer,2009,8:41-60.
[92]Rocha, C. M.; Barros, A. S.; Gil, A. M.; Goodfellow, B. J.; Humpfer, E.; Spraul, M.; Carreira, I. M.; Melo, J. B.; Bernardo, J.; Gomes, A.; Sousa, V.; Carvalho, L.; Duarte, I. F. Metabolic profiling of human lung cancer tissue by'H high resolution magic angle spinning (HRMAS) NMR spectroscopy [J]. J. Proteome Res.,2010,9 (1):319-332.
[1]Bernini, P.; Bertini, I.; Luchinat, C.; Nincheri, P.; Staderini, S.; Turano, P. Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks [J]. J. Biomol. NMR,2011,49 (3-4):231-243.
[2]Dettmer, K.; Aronov, P. A.; Hammock. B. D. Mass spectrometry-based metabolomics [J]. Mass Spectrom. Rev.,2007,26 (1):51-78.
[3]Weiss, R.H; Kim, K. Metabolomics in the study of kidney diseases [J]. Nat. Rev. Nephroi,2012, 8:22-33.
[4]Sreekumar, A.; Poisson, L. M.; Rajendiran, T. M.; Khan, A. P.; Cao, Q.; Yu, J.; Laxman, B.; Mehra, R.; Lonigro, R. J.; Li, Y.; Nyati, M. K.; Ahsan, A.; Kalyana-Sundaram, S.; Han, B.; Cao, X.; Byun, J.; Omenn, G. S.; Ghosh, D.; Pennathur, S.; Alexander, D. C.; Berger, A.; Shuster, J. R.; Wei, J. T.; Varambally, S.; Beecher, C.; Chinnaiyan, A. M. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression [J]. Nature,2009,457 (7231):910-914.
[5]Gillett, M. P. Comparative studies of the lecithin:cholesterol acyltransferase reaction in the plasma of reptiles and amphibians [J]. Scand. J. Clin. Lab. Invest, Suppl 1978,150:32-39.
[6]An, Z.; Chen, Y; Zhang, R.; Song, Y; Sun, J.; He, J.; Bai, J.; Dong, L.; Zhan, Q.; Abliz, Z. Integrated ionization approach for RRLC-MS/MS-based metabonomics:finding potential biomarkers for lung cancer [J]. J. Proteome Res.,2010,9 (8):4071-4081.
[7]Yoshida, T.; Majors, R. E. High-speed analyses using rapid resolution liquid chromatography on 1.8-microm porous particles [J]. J. Sep. Sci.,2006,29 (16):2421-2432.
[8]Wang, Y.; Wang J. S.; Yao, M.; Zhao, X. J.; Fritsche, J.; Schmitt-Kopplin, P.; Cai, Z. W.; Wan, D. F.; Lu, X.; Yang S. L.; Gu, J. R.; Haring, H. U.;Schleicher, E. D.; Lehmann, R.; Xu, G. W. Metabonomics Study on the Effects of the Ginsenoside Rg3 in a β-Cyclodextrin-Based Formulation on Tumor-Bearing Rats by a Fully Automatic Hydrophilic Interaction/Reversed-Phase Column-Switching HPLC-ESI-MS Approach [J]. Anal. Chem.,2008, 80:4680-4688.
[1]Sreekumar, A.; Poisson, L. M.; Rajendiran, T. M.; Khan, A. P.; Cao, Q.; Yu, J.; Laxman, B.; Mehra, R.; Lonigro, R. J.; Li, Y.; Nyati, M. K.; Ahsan, A.; Kalyana-Sundaram, S.; Han, B.; Cao, X.; Byun, J.; Omenn, G. S.; Ghosh, D.; Pennathur, S.; Alexander, D. C.; Berger, A.; Shuster, J. R.; Wei, J. T.; Varambally, S.; Beecher, C; Chinnaiyan, A. M. Metabolomic profiles delineate potential role forsarcosine in prostate cancer progression [J].Nature,2009,457 (7231):910-914.
[2]Dunn, W. B.; Broadhurst, D.; Begley, P.; Zelena, E.; Francis-Mclntyre, S.; Anderson, N.; Brown, M.; Knowles, J. D.; Halsall, A.; Haselden, J. N.; Nicholls, A. W.; Wilson, I. D.; Kell, D. B.; Goodacre, R. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry [J]. Nat. Protoc.,2011, 6(7):1060-1083.
[3]Chen, J.; Zhang, X.; Cao, R.; Lu, X.; Zhao, S.; Fekete, A.; Huang, Q.; Schmitt-Kopplin, P.; Wang, Y; Xu, Z.; Wan, X.; Wu, X.; Zhao, N.; Xu, C.; Xu, G. Serum 27-nor-5β-Cholestane-3,7,12,24,25 Pentol Glucuronide Discovered by Metabolomics as Potential Diagnostic Biomarker for Epithelium Ovarian Cancer [J]. J. Proteome Res.,2011,10 (5):2625-2632.
[4]Guo, X. Z.; Qiu, L. Investigation and Improvement of Workflow in Biochemistry Laboratory [J]. J. Mod. Lab. Med,2007,22 (4):100-101.
[5]Zwart, S. R.; Wolf, M.; Rogers, A.; Rodgers, S.; Gillman, P. L.; Hitchcox, K.; Ericson, K. L. Smith, S. M. Stability of analytes related to clinical chemistry and bone metabolism in blood specimens after delayed processing [J]. Clin. Biochem.,2009,42 (9):907-910.
[6]Rosenling, T.; Slim, C. L.; Christin, C.; Coulier, L.; Shi, S.; Stoop, M. P.; Bosman, J.; Suits, F.; Horvatovich, P. L.; Stockhofe-Zurwieden, N.; Vreeken, R.; Hankemeier, T.; van Gool, A. J.; Luider, T. M.; Bischoff, R. The Effect of Preanalytical Factors on Stability of the Proteome and Selected Metabolites in Cerebrospinal Fluid (CSF) [J]. J. Proteome Res.,2009,8:5511-5522.
[7]Fingerhut, R.; Ensenauer R.; ROschinger, W.; Arnecke, R.; OlgemOller, B.; Roscher, A. A. Stability of Acylcarnitines and Free Carnitine in Dried Blood Samples:Implications for Retrospective Diagnosis of Inborn Errors of Metabolism and Neonatal Screening for Carnitine Transporter Deficiency [J]. Anal. Chem.,2009,81:3571-3575.
[8]Mancinelli, A.; Iannoni, E.; Duran, M.; Calvani, M. Effect of temperature on the stability of long-chain acylcarnitines in human blood prior to plasma separation [J]. Clinica. Chimica. Acta., 2007,375(1-2):169-170.
[9]Maslanka, K.; Smolenska-Sym, G.; Michur, H.; Wrobel, A.; Lachert, E.; Brojer, E. Lysophosphatidylcholines:Bioactive Lipids Generated During Storage of Blood Components [J]. Arch. Immunol. Ther. Ex.,2011,60 (1),55-60.
[10]Vlaar, A. P. J.; Kulik, W.; Nieuwland, R.; Peters, C. P.; Tool, A. T. J.; van Bruggen, R.; Juffermans, N. P.; de Korte, D. Accumulation of bioactive lipids during storage of blood products is not cell but plasma derived and temperature dependent [J]. Transfusion,2011,51 (11): 2358-2366.
[11]Hustad, S.; Eussen, S.; Midttun, O.; Ulvik, A.; van de Kant, P. M.; Morkrid, L.; Gislefoss, R.; Ueland, P. M. Kinetic Modeling of Storage Effects on Biomarkers Related to B Vitamin Status and One-Carbon Metabolism [J]. Clin. Chem.,2011,58 (2):402-410.
[12]Drammeh, B. S.; Schleicher, R. L.; Pfeiffer, C. M.; Jain, R. B.; Zhang, M.; Nguyen, P. H. Effects of delayed sample processing and freezing on serum concentrations of selected nutritional indicators [J]. Clin Chem.,2008,54 (11):1883-1891.
[13]Bernini, P.; Bertini, I.; Luchinat, C.; Nincheri, P.; Staderini, S.; Turano, P. Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks [J]. J. Biomol. NMR,2011,49 (3-4):231-243.
[14]Gika, H. G.; Theodoridis, G. A.; Wilson, I. D. Liquid chromatography and ultra-performance liquid chromatography-mass spectrometry fingerprinting of human urine [J]. J. Chromatogr. A, 2008,1189 (1-2):314-322.
[15]Lauridsen, M.; Hansen, S. H.; Jaroszewski, J. W.; Cornett, C. Human Urine as Test Material in 1H NMR-Based Metabonomics:Recommendations for Sample Preparation and Storage [J]. Anal. Chem.,2007,79:1181-1186.
[16]Oddoze, C.; Lombard, E.; Portugal, H. Stability study of 81 analytes in human whole blood, in serum and in plasma [J]. Clin. Biochem.,2012,45 (6):464-469.
[17]Bobby L.; Boyanton, J.; Kenneth, E. B. stability studies of tewnty-four ananlytes in human plasma ans serum [J]. Clin. Biochem.,2002,48 (12):2242-2247.
[18]Wiklund, S.; Johansson, E.; Sjostrom, L.; Mellerowicz, E. J.; Edlund, U.; Shockcor, J. P.; Gottfries, J.; Moritz, T.; Trygg, J. 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.
[19]Glomset, J. A. The plasma lecithin:cholesterol acyltransferase reaction [J]. J. Lipid Res.,1968,9: 155-167.
[20]Percy, A. K.; Schmell, E.; Earles, B. J.; Lennarz, W. J. Phospholipid biosynthesis in the membranes of immature and mature red blood cells [J]. Biochemistry,1973,12 (13):2456-2461.
[21]Gevi, F.; D'Alessandro, A.; Rinalducci, S.; Zolla, L. Alterations of red blood cell metabolome during cold liquid storage of erythrocyte concentrates in CPD-SAGM [J]. J. Proteomics,2012, 76:168-180.
[22]Grant, R. A.; Zucker, M. B. Altered uptake and release of I4C-serotonin in platelets following incubation of platelet-rich plasma with EDTA [J]. Thromb. Res.,1979,14 (6):981-986.
[23]Kuci, Z.; Hins, J.; Kuci, S.; Renner, S.; Flottmann, D.; Bruchelt, G. Determination of glucose metabolites in stored erythrocytes and in erythrocytes from patients with thalassemia by analytical isotachophoresis [J]. J. Biochem. Biophys. Methods,2006,69 (1-2):79-87.
[24]de los Reyes, B.; Navarro, J. A.; Perez-Garcia, R.; Liras, A.; Campos, Y.; Bornstein, B.; Arenas, J. Effects of L-carnitine on erythrocyte acyl-CoA, free CoA, and glycerophospholipid acyltransferase in uremia [J]. Am. J. Clin. Nutr.,1998,67 (3):386-390.
[25]Borum, P. R. Plasma carnitine compartment and red blood cell carnitine compartment of healthy adults [J]. Am. J. Clin. Nutr.,1987,46 (3):437-441.
[26]Fox, I. H.; Burk, L.; Planet, G.; Goren, M.; Kaminska, J. Pyrimidine nucleotide biosynthesis. A study of normal and purine enzyme-deficient cells [J]. J. Biol. Chem.,1978,253 (19): 6794-6800.
[27]Norlin, M.; Wikvall, K. Enzymes in the conversion of cholesterol into bile acids [J]. Curr. Mol. Med.,2007,7 (2):199-218.
[1]Granville, C. A.; Dennis, P. A. An overview of lung cancer genomics and proteomics [J]. Am. J. Respir. CellMol. Biol,2005,32 (3):169-176.
[2]Wiklund, S.; Johansson, E.; Sjostrom, L.; Mellerowicz, E. J.; Edlund, U.; Shockcor, J. P.; Gottfries, J.; Moritz, T.; Trygg, J. 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.
[3]Werner, E.; Croixmarie, V.; Umbdenstock, T.; Ezan, E.; Chaminade, P.; Tabet, J. C.; Junot, C. Mass spectrometry-based metabolomics:accelerating the characterization of discriminating signals by combining statistical correlations and ultrahigh resolution [J]. Anal. Chem.,2008,80 (13):4918-4932.
[4]Kuhl, C.; Tautenhahn, R.; Bottcher, C.; Larson, T. R.; Neumann, S. CAMERA:an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets [J]. Anal. Chem.,2012,84 (1):283-289.
[5]Chen, Y.; Zhang, R.; Song, Y.; He, J.; Sun, J.; Bai, J.; An, Z.; Dong, L.; Zhan, Q.; Abliz, Z. RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network:finding potential biomarkers for breast cancer [J]. Analyst,2009,134 (10):2003-2011.
[6]van den Berg, R. A.; Hoefsloot, H. C.; Westerhuis, J. A.; Smilde, A. K.; van der Werf, M. J. Centering, scaling, and transformations:improving the biological information content of metabolomics data [J]. BMC Genomics,2006,7:142-157.
[7]Eriksson, L.; Johansson; E.Wold, N. K. Multi-and Megavariate Data Analysis:Principles and Applications [M]. Sweden:Umetrics Academy,2001:39-62.
[8]沈国庆.基于动物肿瘤模型的LC-MS/MS代谢组学及药物代谢组学方法研究[D].北京：北京协和医学院,2011：
[9]安卓玲.组合式离子化方式的RRLC-MS/MS分析方法及其肺癌的代谢组学研究[D].北京：北京协和医学院,2010：
[1]Zhu, Z. J.; Schultz, A. W.; Wang, J.; Johnson, C. H.; Yannone, S. M.; Patti, G. J.; Siuzdak, G. Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLEN database [J]. Nat. Protoc.,2013,8 (3):451-460.
[2]Theodoridis, G. A.; Gika, H. G.; Want, E. J.; Wilson, I. D. Liquid chromatography-mass spectrometry based global metabolite profiling:a review [J]. Anal. Chim. Acta.,2012,711:7-16.
[3]Sachan, D. S.; Dodson, W. L. The serum carnitine status of cancer patients [J]. J. Am. Coll. Nutr., 1987,6(2):145-150.
[4]Sewell, A. C.; Bohles, H. J. Acylcarnitines in intermediary metabolism [J]. Eur. J. Pediatr.,1995, 154 (11):871-877.
[5]Chen, J.; Wang, W.; Lv, S.; Yin, P.; Zhao, X.; Lu, X.; Zhang, F.; Xu, G. 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 (1):3-9.
[6]石如玲,姜玲玲.过氧化物酶体脂肪酸p氧化[J].中国生物化学与分子生物学报2009,25(1)：12-16.
[7]Moore, K. H.; Radloff, J. F.; Koen, A. E.; Hull, F. E. Incomplete fatty acid oxidation by heart mitochondria:beta-hydroxy fatty acid production [J]. J. Mol. Cell. Cardiol.,1982,14 (8): 451-459.