鼻咽癌血清代谢组学初步研究
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摘要
鼻咽癌(nasopharyngeal carcinoma,NPC)是一种具有罕见的地理和种族分布特征的恶性肿瘤。在东南亚和我国南方地区(广东、广西、湖南、福建、江西)常见,在中国南方,其发病率和死亡率均居世界首位,严重危害国人的生命和健康。虽然目前的研究结果认为NPC的发病与EB病毒感染、遗传易感性和接触化学致癌物有关,但NPC的发病机制仍然不明。发现新的有效的NPC分子标志物将有助于NPC的诊断、临床治疗、预后判断及其发病机制的研究,寻找新的有效的NPC诊断方法将有助于NPC的早期诊断。
以高通量为特征的组学技术具有发现肿瘤相关分子的潜能。如cDNA芯片技术分析NPC的基因表达谱,已发现一些可能与NPC发病相关的异常表达基因;比较蛋白质组学技术分析NPC细胞/组织的蛋白质表达谱,已发现一些可能与NPC发生发展及转移有关的差异表达蛋白质。代谢组学是继基因组学、蛋白质组学等之后的一个新兴学科,被定义为生物系统对病理生理刺激或遗传修饰等发生的动态的、多参数的反应,其研究的是代谢产物的完整模式,根据细胞、组织、器官或有机体的发育、生理或病理状态的不同而有质和量的差异。代谢组学可被用于阐明来源于基因组和蛋白质组改变的下游与疾病相关的生物化学反应的改变。在药物的药效和毒性评价、药物作用机理研究、营养干预、疾病诊断、转基因食品的开发、植物表型分型、中药指纹识别等方面,代谢组学技术展现了广阔的应用前景。同样,它在识别与肿瘤发生发展相关的小分子物质、发现肿瘤分子标志物、肿瘤代谢特征和治疗靶标方面具有独特的优势。
当前,代谢组学研究中存在两个难点同时也是热点问题:一是对细胞、组织或体液中所有的代谢产物进行定性、定量分析;二是对细胞、组织或体液分析得到的代谢产物复杂数据体系中的有用信息进行提取。本研究利用色谱-质谱联用技术,结合能有效提取和利用复杂二维数据系统中有用信息的化学计量学多元校正、多元分辨和模式识别方法,达到有效地提取血清代谢物指纹中有用信息的目的。
本研究采用气相色谱-质谱联用技术(Gas Chromatography-MassSpectrometry,GC-MS)对102例NPC初诊患者和107例健康对照血清进行分析,建立了NPC与健康对照血清的代谢指纹图谱;将代谢指纹图谱(GC-MS分析得到的总离子流图)导入NIST107质谱数据库,进行检索,共鉴定出47种内源性化合物。在此基础上,采用分类特征变量法寻找到潜在的代谢标志物4个,分别为:乳酸(lactic acid),赖氨酸(L-Lysine),甘氨酸(glycine)及葡糖醛酸内酯(glucuronolactone);并采用化学计量学模式识别的方法(PCA、PLS-DA、ULDA),建立了针对NPC和健康对照的判别模型,并计算其判别能力,十折交互校验法对所得模型的预测能力进行评估,结果证实所得模型具有良好的判别与预测能力;双盲实验对另30例NPC初诊患者和30例健康对照血清代谢指纹图谱的ULDA判别模型的判别能力进行验证,结果证实了判别模型的可靠性与稳定性。研究结果为寻找NPC分子标志物、NPC代谢相关通路研究及NPC的诊断提供了新的思路与方法。
Nasopharyngeal carcinoma (NPC) is a malignancy with an unusual geographical and ethnic distribution across the world. It is prevalent in southern China and Southeast Asia, particularly in the Cantonese population, where its incidence has remained high for decades. And it remains a serious healthcare problem in these regions nowadays. Recent studies have demonstrated that the etiology of NPC is complex, involving multiple factors including genetic susceptibility, infection with the Epstein-Barr virus (EBV) and exposure to chemical carcinogens. Although numerous efforts have been made to reveal the molecular mechanism of NPC carcinogenesis and development, it remains poorly understood. Finding effective biomarkers for NPC will benefit its diagnosis, treatment effect, prognosis judgement and mechanism study.
High-throughput technologies such as microarrays and proteomics have the potential to find important molecules previously unidentified in NPC. Analysis for gene expression profiles of NPC have been reported using a cDNA array, and revealed that certain genes with aberrant expressions possibly contributed to pathogenesis of NPC. Comparative proteomics has introduced a new approach to cancer research, analysis of NPC cell/tissue has identified differential expression proteins associated with the development and progression of this disease. Metabonomics, defined as the "the quantitative measurement of the dynamic multiparametric response of a living system to pathophysiological stimuli or genetic modification", is a new discipline arise following genomics, transcriptomics and proteomics. Differences at metabolites level can be used to elucidate changes that occur downstream from genomic and proteomic alterations associated with disease-related biochemical reactions. Because these processes precede changes in cell morphology that predict disease, metabolomics approaches may permit early diagnosis or real-time monitoring of the effects of a disease or therapeutic intervention, and providing new opportunities to uncover biomarkers and therapeutic targets for NPC as well as reveal the molecular mechanism underlying this disease.
Metabolomics has been successfully applied to many fields such as toxicological screening, nutrition intervention, plant genotype discrimination, disease diagnosis, ect. However, extracting useful information from complex data system of all metabolites, is one of the difficult points existing in metabolomics research. To solve this problem, combination of hyphenated chromatographic instruments and effective chemometric approaches were adopted.
In this study, we adopted Gas Chromatography-Mass Spectrometry (GC-MS) technology to analyze the metabolites profiling of sera from 102 cases of NPC at first diagnosis and 107 cases of normal healthy people; acquired the spectrum data of each case, and established the metabolites profiling of NPC and healthy control separately. The spectrum data (totoal ion current, TIC) were input and searched in the NIST107 database, 47 endogenous compounds were identified. And then, Classified Characteristic Variable Method was adopted to find potential biomarkers, results in 4 biomarkers, they are lactic acid, L-Lysine, glycine and glucuronolactone. At the same time, model recognition methods (PCA, PLS-DA and ULDA) were adopted to build recognition models for discriminating between NPC and healthy controls, 10-fold cross validation analysis evaluated the prediction ability of ULDA model. Results turn out to be perfect. Double blind experiment validated the reliability and stability of recognition model ULDA.
These results provide a new way for NPC diagnosis and a new idea for finding biomarkers, NPC-associated metabolomic pathways.
以高通量为特征的组学技术具有发现肿瘤相关分子的潜能。如cDNA芯片技术分析NPC的基因表达谱,已发现一些可能与NPC发病相关的异常表达基因;比较蛋白质组学技术分析NPC细胞/组织的蛋白质表达谱,已发现一些可能与NPC发生发展及转移有关的差异表达蛋白质。代谢组学是继基因组学、蛋白质组学等之后的一个新兴学科,被定义为生物系统对病理生理刺激或遗传修饰等发生的动态的、多参数的反应,其研究的是代谢产物的完整模式,根据细胞、组织、器官或有机体的发育、生理或病理状态的不同而有质和量的差异。代谢组学可被用于阐明来源于基因组和蛋白质组改变的下游与疾病相关的生物化学反应的改变。在药物的药效和毒性评价、药物作用机理研究、营养干预、疾病诊断、转基因食品的开发、植物表型分型、中药指纹识别等方面,代谢组学技术展现了广阔的应用前景。同样,它在识别与肿瘤发生发展相关的小分子物质、发现肿瘤分子标志物、肿瘤代谢特征和治疗靶标方面具有独特的优势。
当前,代谢组学研究中存在两个难点同时也是热点问题:一是对细胞、组织或体液中所有的代谢产物进行定性、定量分析;二是对细胞、组织或体液分析得到的代谢产物复杂数据体系中的有用信息进行提取。本研究利用色谱-质谱联用技术,结合能有效提取和利用复杂二维数据系统中有用信息的化学计量学多元校正、多元分辨和模式识别方法,达到有效地提取血清代谢物指纹中有用信息的目的。
本研究采用气相色谱-质谱联用技术(Gas Chromatography-MassSpectrometry,GC-MS)对102例NPC初诊患者和107例健康对照血清进行分析,建立了NPC与健康对照血清的代谢指纹图谱;将代谢指纹图谱(GC-MS分析得到的总离子流图)导入NIST107质谱数据库,进行检索,共鉴定出47种内源性化合物。在此基础上,采用分类特征变量法寻找到潜在的代谢标志物4个,分别为:乳酸(lactic acid),赖氨酸(L-Lysine),甘氨酸(glycine)及葡糖醛酸内酯(glucuronolactone);并采用化学计量学模式识别的方法(PCA、PLS-DA、ULDA),建立了针对NPC和健康对照的判别模型,并计算其判别能力,十折交互校验法对所得模型的预测能力进行评估,结果证实所得模型具有良好的判别与预测能力;双盲实验对另30例NPC初诊患者和30例健康对照血清代谢指纹图谱的ULDA判别模型的判别能力进行验证,结果证实了判别模型的可靠性与稳定性。研究结果为寻找NPC分子标志物、NPC代谢相关通路研究及NPC的诊断提供了新的思路与方法。
Nasopharyngeal carcinoma (NPC) is a malignancy with an unusual geographical and ethnic distribution across the world. It is prevalent in southern China and Southeast Asia, particularly in the Cantonese population, where its incidence has remained high for decades. And it remains a serious healthcare problem in these regions nowadays. Recent studies have demonstrated that the etiology of NPC is complex, involving multiple factors including genetic susceptibility, infection with the Epstein-Barr virus (EBV) and exposure to chemical carcinogens. Although numerous efforts have been made to reveal the molecular mechanism of NPC carcinogenesis and development, it remains poorly understood. Finding effective biomarkers for NPC will benefit its diagnosis, treatment effect, prognosis judgement and mechanism study.
High-throughput technologies such as microarrays and proteomics have the potential to find important molecules previously unidentified in NPC. Analysis for gene expression profiles of NPC have been reported using a cDNA array, and revealed that certain genes with aberrant expressions possibly contributed to pathogenesis of NPC. Comparative proteomics has introduced a new approach to cancer research, analysis of NPC cell/tissue has identified differential expression proteins associated with the development and progression of this disease. Metabonomics, defined as the "the quantitative measurement of the dynamic multiparametric response of a living system to pathophysiological stimuli or genetic modification", is a new discipline arise following genomics, transcriptomics and proteomics. Differences at metabolites level can be used to elucidate changes that occur downstream from genomic and proteomic alterations associated with disease-related biochemical reactions. Because these processes precede changes in cell morphology that predict disease, metabolomics approaches may permit early diagnosis or real-time monitoring of the effects of a disease or therapeutic intervention, and providing new opportunities to uncover biomarkers and therapeutic targets for NPC as well as reveal the molecular mechanism underlying this disease.
Metabolomics has been successfully applied to many fields such as toxicological screening, nutrition intervention, plant genotype discrimination, disease diagnosis, ect. However, extracting useful information from complex data system of all metabolites, is one of the difficult points existing in metabolomics research. To solve this problem, combination of hyphenated chromatographic instruments and effective chemometric approaches were adopted.
In this study, we adopted Gas Chromatography-Mass Spectrometry (GC-MS) technology to analyze the metabolites profiling of sera from 102 cases of NPC at first diagnosis and 107 cases of normal healthy people; acquired the spectrum data of each case, and established the metabolites profiling of NPC and healthy control separately. The spectrum data (totoal ion current, TIC) were input and searched in the NIST107 database, 47 endogenous compounds were identified. And then, Classified Characteristic Variable Method was adopted to find potential biomarkers, results in 4 biomarkers, they are lactic acid, L-Lysine, glycine and glucuronolactone. At the same time, model recognition methods (PCA, PLS-DA and ULDA) were adopted to build recognition models for discriminating between NPC and healthy controls, 10-fold cross validation analysis evaluated the prediction ability of ULDA model. Results turn out to be perfect. Double blind experiment validated the reliability and stability of recognition model ULDA.
These results provide a new way for NPC diagnosis and a new idea for finding biomarkers, NPC-associated metabolomic pathways.
引文
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