基于色谱质谱联用技术的代谢组学方法研究及大肠癌代谢组学研究
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摘要
本论文基于超高效液相色谱质谱联用(UPLC-QTOFMS)技术,探索建立了血清代谢组学方法和人大肠癌组织代谢组学方法。同时,本论文基于色谱质谱联用技术,包括气相色谱飞行时间质谱联用(GC-TOFMS)和UPLC-QTOFMS,进行了人大肠癌尿液样本的代谢组学研究,分析了临床大肠癌患者与正常对照人群尿液样本的代谢谱差异,找到35个重要差异代谢物表征了与大肠癌相关的生物信息变化；并从中进一步筛选出了具有标志代谢物组作用的7个关键代谢物,在训练集和测试集的受试者工作特征曲线(ROC)分析中,曲线下面积AUC分别为0.993和0.998,具有较高的诊断准确性。
主要内容和结果如下：
1、采用超高效液相色谱-四极杆飞行时间高分辨质谱(UPLC-QTOFMS)联用技术,对血清前处理进行考察和优化,对液质联用分析条件进行优化,建立了适用于血清中广谱小分子代谢物分析(global metabolic profiling)的高通量强耐用的代谢组学分析方法,特别是能满足生物样本数超过400个的大批次代谢组学研究的要求。对最终确定的血清前处理和分析方法进行了方法学考察,结果表明,本方法重现性、精密度及稳定性均良好。对重现性和48小时稳定性考察的检测结果进行变异系数(CV)分析和主成分分析法(Principal component analysis, PCA)的多维分析,证明本方法在代谢组学研究中的可重复性及获得的数据的可靠性良好。
2、采用超高效液相色谱-四极杆飞行时间高分辨质谱(UPLC-QTOFMS)联用技术,对人大肠癌组织前处理方法进行考察和优化,对液质联用分析条件进行优化,建立了适用于人大肠癌组织中广谱小分子代谢物分析(global metabolic profiling)的高通量强耐用的代谢组学分析方法,特别是能满足生物样本数超过400个的大批次代谢组学研究的要求。对最终确定的前处理和分析方法进行了方法学考察,结果表明,本方法重现性、精密度及稳定性均良好。对重现性和48小时稳定性考察的检测结果进行变异系数(CV)分析,证明本方法在人大肠癌组织代谢组学研究中的可重复性及获得的数据的可靠性良好。
3、采用TMS衍生和气相色谱-飞行时间质谱(GC-TOFMS)联用的代谢组学技术,分析了大肠癌患者和正常对照人群的尿液中代谢谱的差异。利用非监督的PCA方法,能得到大肠癌患者与正常人代谢谱分离的趋势,进一步利用有监督的正交偏最小方差-判别分析(OPLS-DA)的方法能清晰地观察到大肠癌患者与正常对照之间的代谢轮廓的差异,其中包括24例病理分期为Ⅰ期的患者也能与正常人完全分开。
4、采用超高效液相四级杆串联飞行时间质谱联用仪(UPLC-QTOFMS)技术,分析了大肠癌患者与正常对照人群的尿液中代谢谱的差异。实验中得到与基于GC-TOFMS代谢组学分析类似的结果,利用非监督的PCA方法,能得到大肠癌患者与正常人代谢谱分离的趋势,进一步利用有监督的正交偏最小方差-判别分析(OPLS-DA)的方法能清晰地观察到大肠癌患者与正常对照之间的代谢轮廓的差异,其中包括24例病理分期为Ⅰ期的患者也能与正常人完全分开。
5、利用高灵敏度、高选择性和强定性能力的GC-TOFMS和UPLC-QTOFMS两种分析仪器的联合应用,可一次性分析出尿液样本中更多的可鉴定代谢物,在临床大肠癌患者与正常对照人群尿液的代谢谱差异中,研究了35个重要差异代谢物所表征的与大肠癌相关的代谢路径变化,验证了本研究前期研究的结果,确认与大肠癌密切相关的代谢变化包括能量代谢、色氨酸的代谢、酪氨酸的代谢、苯丙氨酸的代谢、尿素循环和多胺代谢以及肠道菌群结构的变化。并从中进一步筛选出了具有标志代谢物组作用的7个关键代谢物,柠檬酸、马尿酸、对甲基苯酚、2-氨基丁酸、肉豆蔻酸、腐胺和犬尿烯酸,在训练集和测试集的ROC分析中,曲线下面积AUC分别为0.993和0.998,具有较高的诊断准确性。不但为监测疾病状况下代谢物的体内变化提供了坚实、可靠的依据,同时为今后治疗大肠癌疾病的药物靶点的寻找提供了可能。
本论文覆盖了不同生物样本的代谢组学方法研究和尿液代谢组学在大肠癌疾病上的应用研究。本论文的研究结果对延伸代谢组学的研究领域将起到一些作用,同时,对于应用代谢组学在大肠癌早期诊断方法的开发方面有可能具有促进作用。
In this dissertation, we developed robust and high throughput metabonomic methods on different bio-samples, including serum and human colorectal cancer (CRC) tissue, using Ultra Performance Liquid Chromatography and Quadruple/Time-of-flight mass spectrometry (UPLC-QTOFMS). Furthermore, we made a urinary metabonomic study on a cohort of CRC (n=101) and healthy subjects (n=103), based on gas chromatography time-of-flight mass spectrometry (GC-TOFMS) and UPLC-QTOFMS.
Firstly, a robust and high throughput method was developed on UPLC-QTOFMS for performing global metabolic profiling analysis on a large batch of human serum, for example, over400bio-samples in a batch. Using38reference standards, we compared serum preparation methods for protein precipitation by different organic solvents. Different conditions for global metabolic profiling analysis were performed and optimized on UPLC-QTOFMS. We also assayed the reproducibility, precision, and stability of the method of profiling. The results indicated that methanol/acetonitrile (1/9) could effectively and reproducibly precipitated human serum proteins, performing the best extracting effect on the assayed reference standards. At least four fold of pre-cold organic solvents coupled with vortex for2min, ultrasonic treatment for1min and stayed at-20℃for10min were the most optimal for the extracting effect and protein precipitation. The results, according to the reproducibility, precision and stability, showed that the method was satisfactory in global metabolic profiling analysis of human serum. Furthermore, reproducibility of the method verified by PCA analysis was consistent with the CV analysis in the results. The method was adapted to metabolomics study, especially the large batch metabolomics study of over400samples, with a high throughput of over100samples a day (13min for each sample run).
Secondly, a robust and high throughput metabolic profiling method on human CRC tissue was developed on UPLC-QTOFMS for a large batch, for example, over400bio-samples in a batch. We used a two-step extraction method for the preparation of the CRC tissue. Using38reference standards, we compared different reconstituted solvents for protein precipitation and extration effeciency. Different conditions for global metabolic profiling analysis were performed and optimized on UPLC-QTOFMS. We also assayed the reproducibility, precision, and stability of the method of profiling. The results indicated that reconstituted solvent of water/methanol/acetonitrile (1/2/7) could effectively and reproducibly precipitated residued proteins in the extraction of CRC tissue, performing the best extracting effect on the assayed reference standards. The results, according to the reproducibility, precision and stability, showed that the method was satisfactory in global metabolic profiling analysis of human CRC tissue extration. Furthermore, reproducibility of the method was verified by CV analysis in the results. The method was adapted to metabolomics study, especially for a large batch research of over400samples, with a high throughput of over100samples a day (13min for each sample run).
Thirdly, we made a metabonomic research on urine samples from CRC patients and healthy control people. A full spectrum of metabolic aberrations that are directly linked to CRC at early curable stages is critical for developing and deploying molecular diagnostic and therapeutic approaches that will significantly improve patient survival. We reported previously a urinary metabonomic profiling study on CRC subjects (n=60) and health controls (n=63), in which a panel of urinary metabolite markers was identified. Here, we made a second urinary metabonomic study on a larger cohort of CRC (n=101) and healthy subjects (n=103), using GC-TOFMS and UPLC-QTOFMS. Consistent with our first metabonomic study, we discriminated the CRC subjects from the healthy controls, including24CRC cases of early pathological stage of TNM-I. Moreover, we observed a number of dysregulated metabolic pathways similar to our previous findings, such as glycolysis, TCA cycle, urea cycle, pyrimidine metabolism, polyamine metabolism as well as gut microbial-host co-metabolism in CRC subjects. Our findings confirm distinct urinary metabolic footprints of CRC patients characterized by altered levels of metabolites derived from gut microbial-host co-metabolism. A panel of metabolite markers composed of7metabolites (citrate, hippurate, p-cresol,2-aminobutyrate, myristate, putrescine, and kynurenate) was able to discriminate CRC subjects from their healthy counterparts. A receiver operating characteristic curve (ROC) analysis of these markers resulted in an area under the receiver operating characteristic curve (AUC) of0.993and0.998for the training set and the testing set, respectively. These metabolite markers provide a novel molecular diagnostic approach for the early detection of CRC.
These studies in this dissertation covered the metabonomics methodology on different bio-samples and the metabonomic application in a certain malignant disease of colorectal cancer. The results in these studies enriched the understanding of metabonomics, and powered the potential early diagnositic research on colorectal cancer.
主要内容和结果如下：
1、采用超高效液相色谱-四极杆飞行时间高分辨质谱(UPLC-QTOFMS)联用技术,对血清前处理进行考察和优化,对液质联用分析条件进行优化,建立了适用于血清中广谱小分子代谢物分析(global metabolic profiling)的高通量强耐用的代谢组学分析方法,特别是能满足生物样本数超过400个的大批次代谢组学研究的要求。对最终确定的血清前处理和分析方法进行了方法学考察,结果表明,本方法重现性、精密度及稳定性均良好。对重现性和48小时稳定性考察的检测结果进行变异系数(CV)分析和主成分分析法(Principal component analysis, PCA)的多维分析,证明本方法在代谢组学研究中的可重复性及获得的数据的可靠性良好。
2、采用超高效液相色谱-四极杆飞行时间高分辨质谱(UPLC-QTOFMS)联用技术,对人大肠癌组织前处理方法进行考察和优化,对液质联用分析条件进行优化,建立了适用于人大肠癌组织中广谱小分子代谢物分析(global metabolic profiling)的高通量强耐用的代谢组学分析方法,特别是能满足生物样本数超过400个的大批次代谢组学研究的要求。对最终确定的前处理和分析方法进行了方法学考察,结果表明,本方法重现性、精密度及稳定性均良好。对重现性和48小时稳定性考察的检测结果进行变异系数(CV)分析,证明本方法在人大肠癌组织代谢组学研究中的可重复性及获得的数据的可靠性良好。
3、采用TMS衍生和气相色谱-飞行时间质谱(GC-TOFMS)联用的代谢组学技术,分析了大肠癌患者和正常对照人群的尿液中代谢谱的差异。利用非监督的PCA方法,能得到大肠癌患者与正常人代谢谱分离的趋势,进一步利用有监督的正交偏最小方差-判别分析(OPLS-DA)的方法能清晰地观察到大肠癌患者与正常对照之间的代谢轮廓的差异,其中包括24例病理分期为Ⅰ期的患者也能与正常人完全分开。
4、采用超高效液相四级杆串联飞行时间质谱联用仪(UPLC-QTOFMS)技术,分析了大肠癌患者与正常对照人群的尿液中代谢谱的差异。实验中得到与基于GC-TOFMS代谢组学分析类似的结果,利用非监督的PCA方法,能得到大肠癌患者与正常人代谢谱分离的趋势,进一步利用有监督的正交偏最小方差-判别分析(OPLS-DA)的方法能清晰地观察到大肠癌患者与正常对照之间的代谢轮廓的差异,其中包括24例病理分期为Ⅰ期的患者也能与正常人完全分开。
5、利用高灵敏度、高选择性和强定性能力的GC-TOFMS和UPLC-QTOFMS两种分析仪器的联合应用,可一次性分析出尿液样本中更多的可鉴定代谢物,在临床大肠癌患者与正常对照人群尿液的代谢谱差异中,研究了35个重要差异代谢物所表征的与大肠癌相关的代谢路径变化,验证了本研究前期研究的结果,确认与大肠癌密切相关的代谢变化包括能量代谢、色氨酸的代谢、酪氨酸的代谢、苯丙氨酸的代谢、尿素循环和多胺代谢以及肠道菌群结构的变化。并从中进一步筛选出了具有标志代谢物组作用的7个关键代谢物,柠檬酸、马尿酸、对甲基苯酚、2-氨基丁酸、肉豆蔻酸、腐胺和犬尿烯酸,在训练集和测试集的ROC分析中,曲线下面积AUC分别为0.993和0.998,具有较高的诊断准确性。不但为监测疾病状况下代谢物的体内变化提供了坚实、可靠的依据,同时为今后治疗大肠癌疾病的药物靶点的寻找提供了可能。
本论文覆盖了不同生物样本的代谢组学方法研究和尿液代谢组学在大肠癌疾病上的应用研究。本论文的研究结果对延伸代谢组学的研究领域将起到一些作用,同时,对于应用代谢组学在大肠癌早期诊断方法的开发方面有可能具有促进作用。
In this dissertation, we developed robust and high throughput metabonomic methods on different bio-samples, including serum and human colorectal cancer (CRC) tissue, using Ultra Performance Liquid Chromatography and Quadruple/Time-of-flight mass spectrometry (UPLC-QTOFMS). Furthermore, we made a urinary metabonomic study on a cohort of CRC (n=101) and healthy subjects (n=103), based on gas chromatography time-of-flight mass spectrometry (GC-TOFMS) and UPLC-QTOFMS.
Firstly, a robust and high throughput method was developed on UPLC-QTOFMS for performing global metabolic profiling analysis on a large batch of human serum, for example, over400bio-samples in a batch. Using38reference standards, we compared serum preparation methods for protein precipitation by different organic solvents. Different conditions for global metabolic profiling analysis were performed and optimized on UPLC-QTOFMS. We also assayed the reproducibility, precision, and stability of the method of profiling. The results indicated that methanol/acetonitrile (1/9) could effectively and reproducibly precipitated human serum proteins, performing the best extracting effect on the assayed reference standards. At least four fold of pre-cold organic solvents coupled with vortex for2min, ultrasonic treatment for1min and stayed at-20℃for10min were the most optimal for the extracting effect and protein precipitation. The results, according to the reproducibility, precision and stability, showed that the method was satisfactory in global metabolic profiling analysis of human serum. Furthermore, reproducibility of the method verified by PCA analysis was consistent with the CV analysis in the results. The method was adapted to metabolomics study, especially the large batch metabolomics study of over400samples, with a high throughput of over100samples a day (13min for each sample run).
Secondly, a robust and high throughput metabolic profiling method on human CRC tissue was developed on UPLC-QTOFMS for a large batch, for example, over400bio-samples in a batch. We used a two-step extraction method for the preparation of the CRC tissue. Using38reference standards, we compared different reconstituted solvents for protein precipitation and extration effeciency. Different conditions for global metabolic profiling analysis were performed and optimized on UPLC-QTOFMS. We also assayed the reproducibility, precision, and stability of the method of profiling. The results indicated that reconstituted solvent of water/methanol/acetonitrile (1/2/7) could effectively and reproducibly precipitated residued proteins in the extraction of CRC tissue, performing the best extracting effect on the assayed reference standards. The results, according to the reproducibility, precision and stability, showed that the method was satisfactory in global metabolic profiling analysis of human CRC tissue extration. Furthermore, reproducibility of the method was verified by CV analysis in the results. The method was adapted to metabolomics study, especially for a large batch research of over400samples, with a high throughput of over100samples a day (13min for each sample run).
Thirdly, we made a metabonomic research on urine samples from CRC patients and healthy control people. A full spectrum of metabolic aberrations that are directly linked to CRC at early curable stages is critical for developing and deploying molecular diagnostic and therapeutic approaches that will significantly improve patient survival. We reported previously a urinary metabonomic profiling study on CRC subjects (n=60) and health controls (n=63), in which a panel of urinary metabolite markers was identified. Here, we made a second urinary metabonomic study on a larger cohort of CRC (n=101) and healthy subjects (n=103), using GC-TOFMS and UPLC-QTOFMS. Consistent with our first metabonomic study, we discriminated the CRC subjects from the healthy controls, including24CRC cases of early pathological stage of TNM-I. Moreover, we observed a number of dysregulated metabolic pathways similar to our previous findings, such as glycolysis, TCA cycle, urea cycle, pyrimidine metabolism, polyamine metabolism as well as gut microbial-host co-metabolism in CRC subjects. Our findings confirm distinct urinary metabolic footprints of CRC patients characterized by altered levels of metabolites derived from gut microbial-host co-metabolism. A panel of metabolite markers composed of7metabolites (citrate, hippurate, p-cresol,2-aminobutyrate, myristate, putrescine, and kynurenate) was able to discriminate CRC subjects from their healthy counterparts. A receiver operating characteristic curve (ROC) analysis of these markers resulted in an area under the receiver operating characteristic curve (AUC) of0.993and0.998for the training set and the testing set, respectively. These metabolite markers provide a novel molecular diagnostic approach for the early detection of CRC.
These studies in this dissertation covered the metabonomics methodology on different bio-samples and the metabonomic application in a certain malignant disease of colorectal cancer. The results in these studies enriched the understanding of metabonomics, and powered the potential early diagnositic research on colorectal cancer.
引文
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7. Nicholson JK, Lindon JC, Holmes E (1999) 'Metabonomics':understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29:1181-1189.
8. Nicholson JK, O'Flynn MP, Sadler PJ, Macleod AF, Juul SM, et al. (1984) Proton-nuclear-magnetic-resonance studies of serum, plasma and urine from fasting normal and diabetic subjects. Biochem J 217:365-375.
9. Want EJ, O'Maille G, Smith CA, Brandon TR, Uritboonthai W, et al. (2006) Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry. Anal Chem 78:743-752.
10. Michopoulos F, Lai L, Gika H, Theodoridis G, Wilson I (2009) UPLC-MS-based analysis of human plasma for metabonomics using solvent precipitation or solid phase extraction. J Proteome Res 8:2114-2121.
11. Bruce SJ, Tavazzi I, Parisod V, Rezzi S, Kochhar S, et al. (2009) Investigation of human blood plasma sample preparation for performing metabolomics using ultrahigh performance liquid chromatography/mass spectrometry. Anal Chem 81:3285-3296.
12. Zelena E, Dunn WB, Broadhurst D, Francis-McIntyre S, Carroll KM, et al. (2009) Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. Anal Chem 81:1357-1364.
13. Plumb RS, Johnson KA, Rainville P, Smith BW, Wilson ID, et al. (2006) UPLC/MS(E); a new approach for generating molecular fragment information for biomarker structure elucidation. Rapid Commun Mass Spectrom 20:1989-1994.
14. Crews B, Wikoff WR, Patti GJ, Woo HK, Kalisiak E, et al. (2009) Variability analysis of human plasma and cerebral spinal fluid reveals statistical significance of changes in mass spectrometry-based metabolomics data. Anal Chem 81:8538-8544.
15. Liu Y, Chen T, Qiu Y, Cheng Y, Cao Y, et al. (2011) An ultrasonication-assisted extraction and derivatization protocol for GC-TOFMS-based metabolite profiling. Anal Bioanal Chem 400: 1405-1417.
1. Chae, Y.K., et al., Combining Information of Common Metabolites Reveals Global Differences between Colorectal Cancerous and Normal Tissues. Bulletin of the Korean Chemical Society, 2010.31(2):p.379-383.
2. Chan, E.C., et al., Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). J Proteome Res,2009.8(1):p.352-61.
3. Ong, E.S., et al., Metabolic profiling in colorectal cancer reveals signature metabolic shifts during tumorigenesis. Mol Cell Proteomics,2010.
4. Denkert, C., et al., Metabolite profiling of human colon carcinoma--deregulation of TCA cycle and amino acid turnover. Molecular Cancer,2008.7:p.72.
5. Mal, M., et al., Development and validation of a gas chromatography/mass spectrometry method for the metabolic profiling of human colon tissue. Rapid Communications in Mass Spectrometry,2009.23(4):p.487-494.
6. Piotto, M., Metabolic characterization of primary human colorectal cancers using high resolution magic angle spinning IE magnetic resonance spectroscopy Metabolomics,2008. 5(3):p.292-301.
7. Hirayama, A., et al., Quantitative Metabolome Profiling of Colon and Stomach Cancer Microenvironment by Capillary Electrophoresis Time-of-Flight Mass Spectrometry. Cancer Research,2009.69(11):p.4918-4925.
8. Qi, Y., et al., Urinary metabolite markers of precocious puberty. Mol Cell Proteomics,2012. 11(1):p. Mill 011072.
9. Chen, J.L., et al., Metabolomics of gastric cancer metastasis detected by gas chromatography and mass spectrometry. World J Gastroenterol,2010.16(46):p.5874-80.
10. Bruce, S.J., et al., Investigation of human blood plasma sample preparation for performing metabolomics using ultrahigh performance liquid chromatography/mass spectrometry. Anal Chem,2009.81(9):p.3285-96.
11. Plumb, R.S., et al., UPLC/MS(E); a new approach for generating molecular fragment information for biomarker structure elucidation. Rapid Commun Mass Spectrom,2006. 20(13):p.1989-94.
12. Pan, L., et al., An optimized procedure for metabonomic analysis of rat liver tissue using gas chromatography/time-of-flight mass spectrometry. J Pharm Biomed Anal,2010.52(4):p. 589-96.
1. Cancer Facts and Figures 2011 U. S.
2. Fletcher, R.H., Carcinoembryonic antigen. Ann Intern Med,1986.104(1):p.66-73.
3. Kronborg, O., et al., Randomised study of screening for colorectal cancer with faecal-occult-blood test. Lancet,1996.348(9040):p.1467-71.
4. Mandel, J.S., et al., Reducing mortality from colorectal cancer by screening for fecal occult blood. Minnesota Colon Cancer Control Study. N Engl J Med,1993.328(19):p.1365-71.
5. Nicholson, J.K., J.C. Lindon, and E. Holmes,'Metabonomics':understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica,1999.29(11):p.1181-9.
6. Qiu, Y, et al., Urinary metabonomic study on colorectal cancer. J Proteome Res,2010.9(3):p. 1627-34.
7. Qiu, Y.P., et al., Serum Metabolite Profiling of Human Colorectal Cancer Using GC-TOFMS and UPLC-QTOFMS. Journal of Proteome Research,2009.8(10):p.4844-4850.
8. Chen, J.L., et al., Metabolomics of gastric cancer metastasis detected by gas chromatography and mass spectrometry. World J Gastroenterol,2010.16(46):p.5874-80.
9. Qi, Y., et al., Urinary metabolite markers of precocious puberty. Mol Cell Proteomics,2012. 11(1):p. M1ll 011072.
10. Warrack, B.M., et al., Normalization strategies for metabonomic analysis of urine samples. J Chromatogr B Analyt Technol Biomed Life Sci,2009.877(5-6):p.547-52.
11. Craig, A., et al., Scaling and normalization effects in NMR spectroscopic metabonomic data sets. Anal Chem,2006.78(7):p.2262-7.
1. Plumb, R.S., et al., UPLC/MS(E); a new approach for generating molecular fragment information for biomarker structure elucidation. Rapid Commun Mass Spectrom,2006. 20(13):p.1989-94.
2. Craig, A., et al., Scaling and normalization effects in NMR spectroscopic metabonomic data sets. Anal Chem,2006.78(7):p.2262-7.
3. Warrack, B.M., et al., Normalization strategies for metabonomic analysis of urine samples. J Chromatogr B Analyt Technol Biomed Life Sci,2009.877(5-6):p.547-52.
4. Qiu, Y., et al., Urinary metabonomic study on colorectal cancer. J Proteome Res,2010.9(3):p. 1627-34.
5. Qiu, Y.P., et al., Serum Metabolite Profiling of Human Colorectal Cancer Using GC-TOFMS and UPLC-QTOFMS. Journal of Proteome Research,2009.8(10):p.4844-4850.
1. Cancer Facts and Figures 2011 U.S.
2. Fletcher, R.H., Carcinoembryonic antigen. Ann Intern Med,1986.104(1):p.66-73.
3. Kronborg, O., et al., Randomised study of screening for colorectal cancer with faecal-occult-blood test. Lancet,1996.348(9040):p.1467-71.
4. Mandel, J.S., et al., Reducing mortality from colorectal cancer by screening for fecal occult blood. Minnesota Colon Cancer Control Study. N Engl J Med,1993.328(19):p.1365-71.
5. Nicholson, J.K., J.C. Lindon, and E. Holmes,'Metabonomics':understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of'biological NMR spectroscopic data. Xenobiotica,1999.29(11):p.1181-9.
6. Nicholson, J.K. and I.D. Wilson, Opinion:understanding 'global' systems biology: metabonomics and the continuum of metabolism. Nat Rev Drug Discov,2003.2(8):p.668-76.
7. Nordstrom, A and R. Lewensohn, Metabolomics:Moving to the Clinic. Journal of Neuroimmune Pharmacology,2010.5(1):p.4-17.
8. Qiu, Y., et al., Urinary metabonomic study on colorectal cancer. J Proteome Res,2010.9(3):p. 1627-34.
9. Qiu, Y.P., et al., Serum Metabolite Profiling of Human Colorectal Cancer Using GC-TOFMS and UPLC-QTOFMS. Journal of Proteome Research,2009.8(10):p.4844-4850.
10. Nicholson, J.K., et al., Proton-nuclear-magnetic-resonance studies of serum, plasma and urine from fasting normal and diabetic subjects. Biochem J,1984.217(2):p.365-75.
11. Nicholson, J.K., et al., Monitoring metabolic disease by proton NMR of urine. Lancet,1984. 2(8405):p.751-2.
12. Nicholson, J.K., et al., Susceptibility of human metabolic phenotypes to dietary modulation. Journal of Proteome Research,2006.5(10):p.2780-2788.
13. Warburg, O., On the origin of cancer cells. Science,1956.123(3191):p.309-14.
14. Chae, Y.K., et al., Combining Information of Common Metabolites Reveals Global Differences between Colorectal Cancerous and Normal Tissues. Bulletin of the Korean Chemical Society, 2010.31(2):p.379-383.
15. Chan, E.C., et al., Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). J Proteome Res,2009.8(1):p.352-61.
16. Hirayama, A., et al., Quantitative Metabolome Profiling of Colon and Stomach Cancer Microenvironment by Capillary Electrophoresis lime-of-Flight Mass Spectrometry. Cancer Research,2009.69(11):p.4918-4925.
17. Mal, M., et al., Development and validation of a gas chromatography/mass spectrometry method for the metabolic profiling of human colon tissue. Rapid Communications in Mass Spectrometry,2009.23(4):p.487-494.
18. DeBerardinis, R.J., et al., Beyond aerobic glycolysis:transformed cells can engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis. Proc Natl Acad Sci U S A,2007.104(49):p.19345-50.
19. Locasale, J.W., et al., Phosphoglycerate dehydrogenase diverts glycolytic flux and contributes to oncogenesis. Nat Genet,2011.43(9):p.869-74.
20. Possemato, R., et al., Functional genomics reveal that the serine synthesis pathway is essential in breast cancer. Nature,2011.476(7360):p.346-50.
21. Habano, W., et al., Reduced expression and loss of heterozygosity of the SDHD gene in colorectal and gastric cancer. Oncol Rep,2003.10(5):p.1375-80.
22. Mazzanti, R. and C. Giulivi, Coordination of nuclear-and mitochondrial-DNA encoded proteins in cancer and normal colon tissues. Biochim Biophys Acta,2006.1757(5-6):p. 618-23.
23. Wang, Z., et al., Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature,2011.472(7341):p.57-63.
2. Fiehn, O., et al., Metabolite profiling for plant functional genomics. Nat Biotechnol,2000.18(11): p.1157-61.
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4. Nicholson, J.K., et al., Monitoring metabolic disease by proton NMR of urine. Lancet,1984. 2(8405):p.751-2.
5. Monleon, D., et al., Metabolite profiling of fecal water extracts from human colorectal cancer. Nmr in Biomedicine,2009.22(3):p.342-348.
6. Plumb, R.S., et al., UPLC/MS(E); a new approach for generating molecular fragment information for biomarker structure elucidation. Rapid Commun Mass Spectrom,2006.20(13):p.1989-94.
7. Pan, L., et al., An optimized procedure for metabonomic analysis of rat liver tissue using gas chromatography/time-of-flight mass spectrometry. J Pharm Biomed Anal,2010.52(4):p.589-96.
8. Chan, E.C., et al., Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). J Proteome Res,2009.8(1):p.352-61.
9. Zelena, E., et al., Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. Anal Chem,2009.81(4):p.1357-64.
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1. Qiu Y, Cai G, Su M, Chen T, Liu Y, et al. (2010) Urinary metabonomic study on colorectal cancer. J Proteome Res 9:1627-1634.
2. Qiu YP, Cai GX, Su MM, Chen TL, Zheng XJ, et al. (2009) Serum Metabolite Profiling of Human Colorectal Cancer Using GC-TOFMS and UPLC-QTOFMS. Journal of Proteome Research 8: 4844-4850.
3. Monleon D, Morales JM, Barrasa A, Lopez J A, Vazquez C, et al. (2009) Metabolite profiling of fecal water extracts from human colorectal cancer. Nmr in Biomedicine 22:342-348.
4. Jansson J, Willing B, Lucio M, Fekete A, Dicksved J, et al. (2009) Metabolomics reveals metabolic biomarkers of Crohn's disease. PLoS One 4:e6386.
5. Nicholson JK, Lindon JC (2008) Systems biology:Metabonomics. Nature 455:1054-1056.
6. Nicholson JK, Wilson ID (2003) Opinion:understanding 'global' systems biology:metabonomics and the continuum of metabolism. Nat Rev Drug Discov 2:668-676.
7. Nicholson JK, Lindon JC, Holmes E (1999) 'Metabonomics':understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29:1181-1189.
8. Nicholson JK, O'Flynn MP, Sadler PJ, Macleod AF, Juul SM, et al. (1984) Proton-nuclear-magnetic-resonance studies of serum, plasma and urine from fasting normal and diabetic subjects. Biochem J 217:365-375.
9. Want EJ, O'Maille G, Smith CA, Brandon TR, Uritboonthai W, et al. (2006) Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry. Anal Chem 78:743-752.
10. Michopoulos F, Lai L, Gika H, Theodoridis G, Wilson I (2009) UPLC-MS-based analysis of human plasma for metabonomics using solvent precipitation or solid phase extraction. J Proteome Res 8:2114-2121.
11. Bruce SJ, Tavazzi I, Parisod V, Rezzi S, Kochhar S, et al. (2009) Investigation of human blood plasma sample preparation for performing metabolomics using ultrahigh performance liquid chromatography/mass spectrometry. Anal Chem 81:3285-3296.
12. Zelena E, Dunn WB, Broadhurst D, Francis-McIntyre S, Carroll KM, et al. (2009) Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. Anal Chem 81:1357-1364.
13. Plumb RS, Johnson KA, Rainville P, Smith BW, Wilson ID, et al. (2006) UPLC/MS(E); a new approach for generating molecular fragment information for biomarker structure elucidation. Rapid Commun Mass Spectrom 20:1989-1994.
14. Crews B, Wikoff WR, Patti GJ, Woo HK, Kalisiak E, et al. (2009) Variability analysis of human plasma and cerebral spinal fluid reveals statistical significance of changes in mass spectrometry-based metabolomics data. Anal Chem 81:8538-8544.
15. Liu Y, Chen T, Qiu Y, Cheng Y, Cao Y, et al. (2011) An ultrasonication-assisted extraction and derivatization protocol for GC-TOFMS-based metabolite profiling. Anal Bioanal Chem 400: 1405-1417.
1. Chae, Y.K., et al., Combining Information of Common Metabolites Reveals Global Differences between Colorectal Cancerous and Normal Tissues. Bulletin of the Korean Chemical Society, 2010.31(2):p.379-383.
2. Chan, E.C., et al., Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). J Proteome Res,2009.8(1):p.352-61.
3. Ong, E.S., et al., Metabolic profiling in colorectal cancer reveals signature metabolic shifts during tumorigenesis. Mol Cell Proteomics,2010.
4. Denkert, C., et al., Metabolite profiling of human colon carcinoma--deregulation of TCA cycle and amino acid turnover. Molecular Cancer,2008.7:p.72.
5. Mal, M., et al., Development and validation of a gas chromatography/mass spectrometry method for the metabolic profiling of human colon tissue. Rapid Communications in Mass Spectrometry,2009.23(4):p.487-494.
6. Piotto, M., Metabolic characterization of primary human colorectal cancers using high resolution magic angle spinning IE magnetic resonance spectroscopy Metabolomics,2008. 5(3):p.292-301.
7. Hirayama, A., et al., Quantitative Metabolome Profiling of Colon and Stomach Cancer Microenvironment by Capillary Electrophoresis Time-of-Flight Mass Spectrometry. Cancer Research,2009.69(11):p.4918-4925.
8. Qi, Y., et al., Urinary metabolite markers of precocious puberty. Mol Cell Proteomics,2012. 11(1):p. Mill 011072.
9. Chen, J.L., et al., Metabolomics of gastric cancer metastasis detected by gas chromatography and mass spectrometry. World J Gastroenterol,2010.16(46):p.5874-80.
10. Bruce, S.J., et al., Investigation of human blood plasma sample preparation for performing metabolomics using ultrahigh performance liquid chromatography/mass spectrometry. Anal Chem,2009.81(9):p.3285-96.
11. Plumb, R.S., et al., UPLC/MS(E); a new approach for generating molecular fragment information for biomarker structure elucidation. Rapid Commun Mass Spectrom,2006. 20(13):p.1989-94.
12. Pan, L., et al., An optimized procedure for metabonomic analysis of rat liver tissue using gas chromatography/time-of-flight mass spectrometry. J Pharm Biomed Anal,2010.52(4):p. 589-96.
1. Cancer Facts and Figures 2011 U. S.
2. Fletcher, R.H., Carcinoembryonic antigen. Ann Intern Med,1986.104(1):p.66-73.
3. Kronborg, O., et al., Randomised study of screening for colorectal cancer with faecal-occult-blood test. Lancet,1996.348(9040):p.1467-71.
4. Mandel, J.S., et al., Reducing mortality from colorectal cancer by screening for fecal occult blood. Minnesota Colon Cancer Control Study. N Engl J Med,1993.328(19):p.1365-71.
5. Nicholson, J.K., J.C. Lindon, and E. Holmes,'Metabonomics':understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica,1999.29(11):p.1181-9.
6. Qiu, Y, et al., Urinary metabonomic study on colorectal cancer. J Proteome Res,2010.9(3):p. 1627-34.
7. Qiu, Y.P., et al., Serum Metabolite Profiling of Human Colorectal Cancer Using GC-TOFMS and UPLC-QTOFMS. Journal of Proteome Research,2009.8(10):p.4844-4850.
8. Chen, J.L., et al., Metabolomics of gastric cancer metastasis detected by gas chromatography and mass spectrometry. World J Gastroenterol,2010.16(46):p.5874-80.
9. Qi, Y., et al., Urinary metabolite markers of precocious puberty. Mol Cell Proteomics,2012. 11(1):p. M1ll 011072.
10. Warrack, B.M., et al., Normalization strategies for metabonomic analysis of urine samples. J Chromatogr B Analyt Technol Biomed Life Sci,2009.877(5-6):p.547-52.
11. Craig, A., et al., Scaling and normalization effects in NMR spectroscopic metabonomic data sets. Anal Chem,2006.78(7):p.2262-7.
1. Plumb, R.S., et al., UPLC/MS(E); a new approach for generating molecular fragment information for biomarker structure elucidation. Rapid Commun Mass Spectrom,2006. 20(13):p.1989-94.
2. Craig, A., et al., Scaling and normalization effects in NMR spectroscopic metabonomic data sets. Anal Chem,2006.78(7):p.2262-7.
3. Warrack, B.M., et al., Normalization strategies for metabonomic analysis of urine samples. J Chromatogr B Analyt Technol Biomed Life Sci,2009.877(5-6):p.547-52.
4. Qiu, Y., et al., Urinary metabonomic study on colorectal cancer. J Proteome Res,2010.9(3):p. 1627-34.
5. Qiu, Y.P., et al., Serum Metabolite Profiling of Human Colorectal Cancer Using GC-TOFMS and UPLC-QTOFMS. Journal of Proteome Research,2009.8(10):p.4844-4850.
1. Cancer Facts and Figures 2011 U.S.
2. Fletcher, R.H., Carcinoembryonic antigen. Ann Intern Med,1986.104(1):p.66-73.
3. Kronborg, O., et al., Randomised study of screening for colorectal cancer with faecal-occult-blood test. Lancet,1996.348(9040):p.1467-71.
4. Mandel, J.S., et al., Reducing mortality from colorectal cancer by screening for fecal occult blood. Minnesota Colon Cancer Control Study. N Engl J Med,1993.328(19):p.1365-71.
5. Nicholson, J.K., J.C. Lindon, and E. Holmes,'Metabonomics':understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of'biological NMR spectroscopic data. Xenobiotica,1999.29(11):p.1181-9.
6. Nicholson, J.K. and I.D. Wilson, Opinion:understanding 'global' systems biology: metabonomics and the continuum of metabolism. Nat Rev Drug Discov,2003.2(8):p.668-76.
7. Nordstrom, A and R. Lewensohn, Metabolomics:Moving to the Clinic. Journal of Neuroimmune Pharmacology,2010.5(1):p.4-17.
8. Qiu, Y., et al., Urinary metabonomic study on colorectal cancer. J Proteome Res,2010.9(3):p. 1627-34.
9. Qiu, Y.P., et al., Serum Metabolite Profiling of Human Colorectal Cancer Using GC-TOFMS and UPLC-QTOFMS. Journal of Proteome Research,2009.8(10):p.4844-4850.
10. Nicholson, J.K., et al., Proton-nuclear-magnetic-resonance studies of serum, plasma and urine from fasting normal and diabetic subjects. Biochem J,1984.217(2):p.365-75.
11. Nicholson, J.K., et al., Monitoring metabolic disease by proton NMR of urine. Lancet,1984. 2(8405):p.751-2.
12. Nicholson, J.K., et al., Susceptibility of human metabolic phenotypes to dietary modulation. Journal of Proteome Research,2006.5(10):p.2780-2788.
13. Warburg, O., On the origin of cancer cells. Science,1956.123(3191):p.309-14.
14. Chae, Y.K., et al., Combining Information of Common Metabolites Reveals Global Differences between Colorectal Cancerous and Normal Tissues. Bulletin of the Korean Chemical Society, 2010.31(2):p.379-383.
15. Chan, E.C., et al., Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). J Proteome Res,2009.8(1):p.352-61.
16. Hirayama, A., et al., Quantitative Metabolome Profiling of Colon and Stomach Cancer Microenvironment by Capillary Electrophoresis lime-of-Flight Mass Spectrometry. Cancer Research,2009.69(11):p.4918-4925.
17. Mal, M., et al., Development and validation of a gas chromatography/mass spectrometry method for the metabolic profiling of human colon tissue. Rapid Communications in Mass Spectrometry,2009.23(4):p.487-494.
18. DeBerardinis, R.J., et al., Beyond aerobic glycolysis:transformed cells can engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis. Proc Natl Acad Sci U S A,2007.104(49):p.19345-50.
19. Locasale, J.W., et al., Phosphoglycerate dehydrogenase diverts glycolytic flux and contributes to oncogenesis. Nat Genet,2011.43(9):p.869-74.
20. Possemato, R., et al., Functional genomics reveal that the serine synthesis pathway is essential in breast cancer. Nature,2011.476(7360):p.346-50.
21. Habano, W., et al., Reduced expression and loss of heterozygosity of the SDHD gene in colorectal and gastric cancer. Oncol Rep,2003.10(5):p.1375-80.
22. Mazzanti, R. and C. Giulivi, Coordination of nuclear-and mitochondrial-DNA encoded proteins in cancer and normal colon tissues. Biochim Biophys Acta,2006.1757(5-6):p. 618-23.
23. Wang, Z., et al., Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature,2011.472(7341):p.57-63.