肝癌细胞分泌蛋白和糖蛋白的蛋白质组学研究
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摘要
本博士论文工作的主要贡献为:针对分泌蛋白质组研究的难点问题,开展了一系列的研究。发展了一种快速、简单、高效和普适性的富集方法来浓缩纯化大体积高盐体系中的分泌蛋白质,并且在肝癌细胞的分泌蛋白质组研究中得到了很好的应用;通过特别设计的实验来富集大体积溶液中的低分子量蛋白质,富集到的蛋白质分子量最小只有4 KDa;建立了具有高转移潜能的人肝癌细胞分泌蛋白质组表达谱数据库,这一数据库对肝癌转移相关研究具有很高的价值。应用糖蛋白质组学的方法,对分泌蛋白质的糖基化位点进行了高通量研究,鉴定到了172个新位点,包括CD44和laminin等与肝癌转移密切相关的蛋白质的新位点信息。利用这种方法,获得了高纯度的分泌蛋白(高达96.4%),解决了分泌蛋白质研究中的污染难题。应用生物信息学分析方法对肝癌细胞分泌蛋白质组数据进行了分子功能、信号通路和蛋白质相互作用等深入的数据分析和挖掘;针对糖基化位点鉴定的可靠性问题,对基于~(16)O/~(18)O标记的糖基化位点进行分析比较,发现经两种标记鉴定到的糖基化位点一致性很好,首次用实验数据回答了糖基化位点研究中常见的一个问题。
分泌蛋白质被认为包含了疾病早期检测和诊断的标志物,正吸引着越来越多的科研工作者的目光,近年来,分泌蛋白质组研究也取得了长足的进步。除了比较正常的和疾病的,或者不同生理状态的分泌蛋白质的表达差异的研究外,分泌蛋白质的数据库的建立也备受关注。然而,分泌蛋白质组研究存在下面四个亟待解决的问题:1)分泌蛋白容易被胞内蛋白污染,难鉴定到纯的分泌蛋白;2)鉴定低丰度和低分子量的分泌蛋白质还具有很大的挑战;3)有关分泌蛋白质的翻译后修饰研究开展得很少;4)对鉴定到的重要分泌蛋白质的功能研究开展得很少。本论文针对上述难题,开展了一系列的研究,获得了满意的结果。
由于蛋白质糖基化在许多重要的生理和病理过程中起着重要的作用,近年来,蛋白质糖基化修饰研究也备受关注。由于糖蛋白/糖肽富集技术和生物质谱技术等的发展,大规模的糖蛋白质组学研究得以实现,尤其对N-型糖蛋白的糖基化位点的鉴定已经积累了不少研究成果。N-型糖蛋白具有和经典的分泌蛋白一样的基于内质网-高尔基体的生物合成转运路径,经典的分泌蛋白几乎都是N-型糖蛋白。基于此,本论文对肝癌细胞分泌蛋白质的糖基化位点进行了大规模的鉴定,对其糖型进行了初步分析。
肿瘤相关的研究尽管备受重视,也取得了相当多的成绩,但是肿瘤如何蔓延并破坏它们的宿主器官仍然是一个谜。因实体瘤(包括肝癌)而死亡的人数的90%是由于肿瘤转移造成的,因此,肿瘤转移的问题越来越受到重视。肝癌是一种常见的恶性肿瘤,在我国,肝癌是癌症中的第二大杀手。为了更好的研究肝癌转移的机制,复旦大学中山医院肝癌研究所建立了人肝细胞癌转移裸鼠模型和具有不同转移潜能的细胞模型。本论文的研究以其中具有高转移潜能的HCCLM3和转移潜能相对低的MHCC97L为材料。研究主要是将蛋白质组学和糖蛋白质组学方法应用于肝癌细胞分泌蛋白的研究,在肝癌及其转移的基础研究中获得了非常丰富的蛋白质组信息。
本论文由七部分组成。
第一章绪论首先概述了分泌蛋白质组研究的七个方面,及其研究进展和研究瓶颈;综述了蛋白质糖基化修饰研究现状,尤其对糖蛋白/糖肽的分离富集研究进展做了很好的综述;概述了肝癌及其转移的蛋白质组学研究进展,说明了本论文选题目的和意义。
第二章所述工作对肝癌细胞分泌蛋白进行了初步研究。在第一节,对常用的三种浓缩纯化分泌蛋白质的方法(超滤法,透析法,沉淀法)进行了比较研究,实现了研究的基本目的:探索肝癌细胞系LM3在条件培养基中的最佳培养方法;比较不同的浓缩纯化分泌蛋白质的方法;初步了解转移潜能高的肝癌细胞分泌蛋白质组;找到目前分泌蛋白质组研究的瓶颈;为后续分泌蛋白质组研究打下基础。
第三章所述工作发展了一种快速、简单、高效的和普适性的富集方法来浓缩纯化分泌蛋白质。首次采用纳米LTL沸石材料富集大体积培养基中的分泌蛋白,使用SDS-PAGE和Tricine-SDS-PAGE(小分子电泳胶)进行一维电泳分离,切取蛋白条带进行胶内酶解,再进行在线的HPLC-ESI-MS液质联用分析。共鉴定到1474个非冗余蛋白,其中有97个蛋白质的分子量小于15 KDa,这些分子在分泌蛋白质组研究的相关文献中鲜有报道。鉴定到的蛋白质很多是与肝癌的发生发展和侵袭转移密切相关的,如甲胎蛋白(alpha-fetoprotein,AFP)是被广泛应用的肝癌的生物标志物,骨桥蛋白(Osteopontin,OPN)能够促进肝癌细胞的侵袭,基质金属蛋白酶(matrix metalloproteinase,MMPs)是肝癌细胞破坏基底膜,进行侵袭转移的重要分子。结果表明,分泌蛋白质组研究是寻找疾病生物标志物的有效方法,纳米沸石LTL富集分泌蛋白,接1D SDS-PAGE-LC-ESI-MS/MS分离鉴定,是研究分泌蛋白质组的有效的方法。尤其较传统的浓缩纯化方法,纳米沸石富集法是更灵活的方法,它可以特别地、有效地富集低分子量蛋白质,富集鉴定到的蛋白质中,分子量最小的只有4 KDa。
第四章所述工作对肝癌细胞分泌蛋白质的糖基化修饰进行了研究。在第一节,研究了肝癌细胞分泌蛋白质的糖基化位点。首先采用亲水法和肼化学法这两种互补的方法富集糖肽,结合nanoLC-ESI-MS/MS分离分析,对分泌蛋白质的糖基化位点进行了高通量研究,鉴定到了194个糖蛋白的300个糖基化位点,其中172个是新位点,包括CD44和laminin等肝癌转移相关重要蛋白的新位点信息。利用这种方法,获得了高纯度的分泌蛋白(高达96.4%),解答了分泌蛋白研究中的污染问题。首次通过实验直接比较这两种方法。就对糖肽的选择性而言,肼化学法优于亲水法,选择率分别是92.9%和51.3%,然而,就富集鉴定到的糖基化位点而言,亲水法优于肼化学法,分别鉴定到265个和159个糖基化位点。这些发现为今后糖蛋白质组学研究的方法选择提供了很好的参考。在第二节,使用多种凝集素印迹,对肝癌细胞分泌蛋白质的糖型进行了初步的分析,初步比较了具有不同转移潜能的人肝癌细胞(HCCLM3和MHCC97L)分泌蛋白的糖基化差异,为后续研究打下了基础。
第五章工作主要是整合了第二、第三和第四章肝癌细胞分泌蛋白质组数据,利用Ingenuity Pathway Analysis(IPA)等软件对整合数据进行了深入的数据挖掘。了解了其可能的亚网络构成和相互作用,其介导肝癌细胞间,胞外与胞内相互作用,及肝癌细胞分泌蛋白参与的重要的信号通路。了解了其重要的节点分子及其与相互作用分子的联络,以便指导进一步研究的开展。
第六章工作主要是针对糖基化位点鉴定的可靠性问题,对基于~(16)O/~(18)O标记的糖基化位点进行分析比较。发现经两种标记鉴定到的糖基化位点一致性很好,证明在富集糖肽后进行位点标记,基于~(16)O标记的结果是可信的。基于~(16)O标记的糖基化位点鉴定结果是否可靠和基于~(18)O标记是否具有很大的优势是常见的问题,无疑,本章工作首次基于实验数据很好地回答了这一问题。
第七章是全文总结和展望。总结了主要的研究结论,提出了进一步的研究设想。
The main contributions of this dissertation were: to solve the difficulties on secretome research, a series of studies have been conducted. For the enrichment of secretome in large volume of growth media rich in salt, a simple, fast, effective and universal approach was developed and applied well on secretome research of HCC cells. And highly-efficient enrichment for the low molecular weight proteins (LMWPs) in secretome has been achieved via a specially designed experiment. Notably, the smallest MW touched 4 KDa. A secretomic profile of HCC cells was built up, and this database would exhibit valuable information in the research of HCC metastasis. A large-scale detection of glycosylation sites of secreted proteins was successfully performed. 172 new N-glycosites were determined experimentally, including the new N-glycosites of CD44 and laminin etc, which have been reported to be implicated invasion and metastasis of HCC. Importantly, the utility of N-glycoproteomic strategy has been proved to be a great way to profile the genuinely secreted proteins. The fact that 96.4% of the identified glycoproteins could be secretory proteins positively confirmed this good point. To get the information including molecular function, signal pathways and protein-protein interactions, bioinformatic analyses on the secretome identified were conducted deeply. To exam the reliability of glycosites identified according to the mass increased of 1 Da (~(16)O labeled) or 3 Da (~(18)O labeled), the excellent consistence of glycosites from two labeled was founded. Thus, a frequently asked question was answered by experimental data for the first time.
Given the importance of secreted proteins as a source for early detection and diagnosis of disease, secreted proteins have been arousing considerable attentions, and the field of secreted proteome (secretome) has achieved substantial advances during recent years. In addition to the comparative proteomic analyses focusing on the characterization of differentially expressed proteins in two different, well controlled states, e.g. normal and disease, the global profiling of secretome received considerable attraction. However, the analysis of secreted proteins represents some challenges in four aspects as following. 1) The contamination of the authentic secreted proteins is unavoidable, resulting in severe interferences for the identification of secreted proteins; 2) those proteins with low molecular weight or with low abundance are difficult to be discovered; 3) little attention has been paid to the post-translational modifications (PTMs) of the secretory proteins; 4) the functional investigation of those detected secreted proteins has been also paid little attention. To find solutions for the questions mentioned above, a series of researches have been performed and some satisfactory results have been achieved.
Due to vital roles of glycosylation of proteins in lots of important physiological and pathological processes, interest in investigating glycosylation status of glycoprotein has greatly increased in recent years. Because the great progresses of techniques including enrichment methods for glycoproteins/glycopeptides and mass techniques for biomolecules have been made, the study of glycoproteome is progressing at tremendous speeds and has achieved substantial advances during recent years, special in the large-scale detection of glycosylation sites. N-glycoproteins and classical secreted proteins have the same dependence of the endoplasmic reticulum (ER) - Golgi network, and the classical secreted proteins are almost N-glycoproteins. Thus, a large-scale glycoproteomic identification and N-glycosylation site elucidation has been performed for secreted proteins of HCC cells. Also, the glycoform has been analyzed elementally.
How tumors spread and kill their host organism remains an enigma, but not for lack of attention. A renewed focus on the problem of metastasis is now apparent, and for good reason—metastasis remains the cause of 90% of deaths from solid tumors. Hepatocellular Carcinoma (HCC) is a common malignancy worldwide and is a second leading cause of death in our country. To understand better the mechanism of HCC metastasis, the human HCC metastasis nude mice model and HCC cell lines with different metastasis potential were built up by the Liver Cancer Institute of Zhongshan Hospital, Fudan University. This work is made on the two HCC cell lines (HCCLM3 and MHCC97L) with different metastasis potential. The investigation has focused on the application of proteomic and glycoproteomic methods in the secretomic research, and a lot of valuable proteome information has been obtained.
This dissertation consists of 7 parts and the contents are summarized as follows:
In the first chapter, the seven aspects of secretomic research were summarized, also, the research progresses and the bottle neck. In addition, the advances in the research of glycosylation were summarized, specially, in the separation and enrichment of Glycoproteins/Glycopeptides. Furthermore, a review of liver proteomic study was presented. Importantly, the aims and significance of this dissertation were presented.
In the second chapter, primary research of secretome of HCC cells has been made. In the first section, three most often used concentration methods (ultrafiltration, dialysis and precipitation) were used together and compared. The merits and shortcomings of each method were discussed based on the obtained results. Four aims of this section have been achieved: the optimized method for cell culture in conditioned medium was developed; three concentration methods (ultrafiltration, dialysis and precipitation) were compared; the secretome of HCC cells with metastasis potential was studied primarily; the bottle neck of secretomic research was founded our. And all these works were basic for further research.
In the third chapter, a simple, fast, efficient and universal enrichment process for secretome was developed. Nanozeolite LTL was used to capture secreted proteins for the first time. Highly-efficient enrichment for the low molecular weight proteins (LMWPs) in secretome has been achieved, and attracted most of attentions in a specially designed experiment. Followed by 1D SDS-PAGE for protein fractionation and then by LC-ESI-MS/MS for protein identification. Totally 1474 unique proteins were confidently identified and 97 proteins of 1474 proteins were those notably with MW less than 15 KDa, which were seldom captured previously by traditional methods. Notably, the smallest MW touches 4 KDa. Many proteins identified above were found to be involved in the processes associated with the cancer development and metastasis. For instance, alpha-fetoprotein (AFP), a widely known biomarker of HCC, was detected. Osteopontin, a secreted phosphoprotein, is a significant factor in HCC metastasis, and its over-expression correlates with metastatic potential of primary hepatocellular carcinoma, and with invasiveness of liver tumor-derived cell lines in vitro. Elevated levels of matrix metalloproteinase (MMPs) have been shown in many tumors with strong association with the invasive and metastatic potentials. These observations demonstrate the contention that secretomic approach is a great way to the discovery of potential biomarkers. And the strategy that started with a capture of secreted proteins by nanozeolite LTL, followed by a separation of 1D SDS-PAGE and by an identification of LC/ESI-MS/MS has been proven to be perfect. Notably, for special enrichment of LMWPs, compared to conventional methods, this strategy was special and efficient.
In the fourth chapter, glycosylation status of HCC cells was investigated. In the first section, a large-scale detection of glycosylation sites of secreted proteins of HCC cells was successfully performed. For the enrichment of glycopeptides, capture methods with hydrophihc affinity (HA) and hydrazide chemistry (HC) were used complementarity. Using both methods in combination with nano-LC-ESI-MS/MS analysis, 300 different glycosylation sites within 194 unique glycoproteins were identified, and 172 glycosites have not been determined experimentally previously, including the new N-glycosites of CD44 and laminin etc, which have been reported to be implicated invasion and metastasis of HCC. Importantly, the utility of N-glycoproteomic strategy is a great way to profile the genuinely secreted proteins. The fact that 96.4% of the identified glycoproteins could be secretory proteins positively confirmed this good point. A direct comparison between HA and HC methods was also investigated for the first time. In brief, in terms of selectivity for glycopeptides, HC is superior to HA (92.9% VS 51.3%), however, based on the number of glycosites identified, HA outweighs HC (265 VS 159). This result based on the compared experiment could be useful for the selection of methods. In the second section, glycoforms of secretome of HCC cells were profiled primarily by multi-lectin blot. The difference of glycosylation status of two HCC cell lines (HCCLM3 and MHCC97L) with different metastasis potential were compared primarily, and based on these results more researches would be done in the near future.
In the fifth chapter, to get the information including molecular function, signal pathways and protein-protein interactions, bioinformatic analyses on the secretome identified were conducted deeply. Ingenuity Pathway Analysis system was used to excavate the secretome data from chapter two, chapter three and chapter four. The sub-network, protein-protein interactions, cell-cell interactions and crucial pathways were investigated. Also, some key node molecules were discussed. All the explorations are basic and helpful for HCC research in future.
In the sixth chapter, to exam the reliability of glycosites identified according to the mass increased of 1 Da (~(16)O labeled) or 3 Da (~(18)O labeled), deglycosylation experiments were carried out parallelly H_2~(16)O in or H_2~(18)O. As a result, the excellent consistence of glycosites from two labeled was founded. Thus, the results from ~(16)O labeled were proven to be credible, and a frequently asked question was answered by experimental data for the first time.
In the last chapter, a summary was made and a prospect was presented.
分泌蛋白质被认为包含了疾病早期检测和诊断的标志物,正吸引着越来越多的科研工作者的目光,近年来,分泌蛋白质组研究也取得了长足的进步。除了比较正常的和疾病的,或者不同生理状态的分泌蛋白质的表达差异的研究外,分泌蛋白质的数据库的建立也备受关注。然而,分泌蛋白质组研究存在下面四个亟待解决的问题:1)分泌蛋白容易被胞内蛋白污染,难鉴定到纯的分泌蛋白;2)鉴定低丰度和低分子量的分泌蛋白质还具有很大的挑战;3)有关分泌蛋白质的翻译后修饰研究开展得很少;4)对鉴定到的重要分泌蛋白质的功能研究开展得很少。本论文针对上述难题,开展了一系列的研究,获得了满意的结果。
由于蛋白质糖基化在许多重要的生理和病理过程中起着重要的作用,近年来,蛋白质糖基化修饰研究也备受关注。由于糖蛋白/糖肽富集技术和生物质谱技术等的发展,大规模的糖蛋白质组学研究得以实现,尤其对N-型糖蛋白的糖基化位点的鉴定已经积累了不少研究成果。N-型糖蛋白具有和经典的分泌蛋白一样的基于内质网-高尔基体的生物合成转运路径,经典的分泌蛋白几乎都是N-型糖蛋白。基于此,本论文对肝癌细胞分泌蛋白质的糖基化位点进行了大规模的鉴定,对其糖型进行了初步分析。
肿瘤相关的研究尽管备受重视,也取得了相当多的成绩,但是肿瘤如何蔓延并破坏它们的宿主器官仍然是一个谜。因实体瘤(包括肝癌)而死亡的人数的90%是由于肿瘤转移造成的,因此,肿瘤转移的问题越来越受到重视。肝癌是一种常见的恶性肿瘤,在我国,肝癌是癌症中的第二大杀手。为了更好的研究肝癌转移的机制,复旦大学中山医院肝癌研究所建立了人肝细胞癌转移裸鼠模型和具有不同转移潜能的细胞模型。本论文的研究以其中具有高转移潜能的HCCLM3和转移潜能相对低的MHCC97L为材料。研究主要是将蛋白质组学和糖蛋白质组学方法应用于肝癌细胞分泌蛋白的研究,在肝癌及其转移的基础研究中获得了非常丰富的蛋白质组信息。
本论文由七部分组成。
第一章绪论首先概述了分泌蛋白质组研究的七个方面,及其研究进展和研究瓶颈;综述了蛋白质糖基化修饰研究现状,尤其对糖蛋白/糖肽的分离富集研究进展做了很好的综述;概述了肝癌及其转移的蛋白质组学研究进展,说明了本论文选题目的和意义。
第二章所述工作对肝癌细胞分泌蛋白进行了初步研究。在第一节,对常用的三种浓缩纯化分泌蛋白质的方法(超滤法,透析法,沉淀法)进行了比较研究,实现了研究的基本目的:探索肝癌细胞系LM3在条件培养基中的最佳培养方法;比较不同的浓缩纯化分泌蛋白质的方法;初步了解转移潜能高的肝癌细胞分泌蛋白质组;找到目前分泌蛋白质组研究的瓶颈;为后续分泌蛋白质组研究打下基础。
第三章所述工作发展了一种快速、简单、高效的和普适性的富集方法来浓缩纯化分泌蛋白质。首次采用纳米LTL沸石材料富集大体积培养基中的分泌蛋白,使用SDS-PAGE和Tricine-SDS-PAGE(小分子电泳胶)进行一维电泳分离,切取蛋白条带进行胶内酶解,再进行在线的HPLC-ESI-MS液质联用分析。共鉴定到1474个非冗余蛋白,其中有97个蛋白质的分子量小于15 KDa,这些分子在分泌蛋白质组研究的相关文献中鲜有报道。鉴定到的蛋白质很多是与肝癌的发生发展和侵袭转移密切相关的,如甲胎蛋白(alpha-fetoprotein,AFP)是被广泛应用的肝癌的生物标志物,骨桥蛋白(Osteopontin,OPN)能够促进肝癌细胞的侵袭,基质金属蛋白酶(matrix metalloproteinase,MMPs)是肝癌细胞破坏基底膜,进行侵袭转移的重要分子。结果表明,分泌蛋白质组研究是寻找疾病生物标志物的有效方法,纳米沸石LTL富集分泌蛋白,接1D SDS-PAGE-LC-ESI-MS/MS分离鉴定,是研究分泌蛋白质组的有效的方法。尤其较传统的浓缩纯化方法,纳米沸石富集法是更灵活的方法,它可以特别地、有效地富集低分子量蛋白质,富集鉴定到的蛋白质中,分子量最小的只有4 KDa。
第四章所述工作对肝癌细胞分泌蛋白质的糖基化修饰进行了研究。在第一节,研究了肝癌细胞分泌蛋白质的糖基化位点。首先采用亲水法和肼化学法这两种互补的方法富集糖肽,结合nanoLC-ESI-MS/MS分离分析,对分泌蛋白质的糖基化位点进行了高通量研究,鉴定到了194个糖蛋白的300个糖基化位点,其中172个是新位点,包括CD44和laminin等肝癌转移相关重要蛋白的新位点信息。利用这种方法,获得了高纯度的分泌蛋白(高达96.4%),解答了分泌蛋白研究中的污染问题。首次通过实验直接比较这两种方法。就对糖肽的选择性而言,肼化学法优于亲水法,选择率分别是92.9%和51.3%,然而,就富集鉴定到的糖基化位点而言,亲水法优于肼化学法,分别鉴定到265个和159个糖基化位点。这些发现为今后糖蛋白质组学研究的方法选择提供了很好的参考。在第二节,使用多种凝集素印迹,对肝癌细胞分泌蛋白质的糖型进行了初步的分析,初步比较了具有不同转移潜能的人肝癌细胞(HCCLM3和MHCC97L)分泌蛋白的糖基化差异,为后续研究打下了基础。
第五章工作主要是整合了第二、第三和第四章肝癌细胞分泌蛋白质组数据,利用Ingenuity Pathway Analysis(IPA)等软件对整合数据进行了深入的数据挖掘。了解了其可能的亚网络构成和相互作用,其介导肝癌细胞间,胞外与胞内相互作用,及肝癌细胞分泌蛋白参与的重要的信号通路。了解了其重要的节点分子及其与相互作用分子的联络,以便指导进一步研究的开展。
第六章工作主要是针对糖基化位点鉴定的可靠性问题,对基于~(16)O/~(18)O标记的糖基化位点进行分析比较。发现经两种标记鉴定到的糖基化位点一致性很好,证明在富集糖肽后进行位点标记,基于~(16)O标记的结果是可信的。基于~(16)O标记的糖基化位点鉴定结果是否可靠和基于~(18)O标记是否具有很大的优势是常见的问题,无疑,本章工作首次基于实验数据很好地回答了这一问题。
第七章是全文总结和展望。总结了主要的研究结论,提出了进一步的研究设想。
The main contributions of this dissertation were: to solve the difficulties on secretome research, a series of studies have been conducted. For the enrichment of secretome in large volume of growth media rich in salt, a simple, fast, effective and universal approach was developed and applied well on secretome research of HCC cells. And highly-efficient enrichment for the low molecular weight proteins (LMWPs) in secretome has been achieved via a specially designed experiment. Notably, the smallest MW touched 4 KDa. A secretomic profile of HCC cells was built up, and this database would exhibit valuable information in the research of HCC metastasis. A large-scale detection of glycosylation sites of secreted proteins was successfully performed. 172 new N-glycosites were determined experimentally, including the new N-glycosites of CD44 and laminin etc, which have been reported to be implicated invasion and metastasis of HCC. Importantly, the utility of N-glycoproteomic strategy has been proved to be a great way to profile the genuinely secreted proteins. The fact that 96.4% of the identified glycoproteins could be secretory proteins positively confirmed this good point. To get the information including molecular function, signal pathways and protein-protein interactions, bioinformatic analyses on the secretome identified were conducted deeply. To exam the reliability of glycosites identified according to the mass increased of 1 Da (~(16)O labeled) or 3 Da (~(18)O labeled), the excellent consistence of glycosites from two labeled was founded. Thus, a frequently asked question was answered by experimental data for the first time.
Given the importance of secreted proteins as a source for early detection and diagnosis of disease, secreted proteins have been arousing considerable attentions, and the field of secreted proteome (secretome) has achieved substantial advances during recent years. In addition to the comparative proteomic analyses focusing on the characterization of differentially expressed proteins in two different, well controlled states, e.g. normal and disease, the global profiling of secretome received considerable attraction. However, the analysis of secreted proteins represents some challenges in four aspects as following. 1) The contamination of the authentic secreted proteins is unavoidable, resulting in severe interferences for the identification of secreted proteins; 2) those proteins with low molecular weight or with low abundance are difficult to be discovered; 3) little attention has been paid to the post-translational modifications (PTMs) of the secretory proteins; 4) the functional investigation of those detected secreted proteins has been also paid little attention. To find solutions for the questions mentioned above, a series of researches have been performed and some satisfactory results have been achieved.
Due to vital roles of glycosylation of proteins in lots of important physiological and pathological processes, interest in investigating glycosylation status of glycoprotein has greatly increased in recent years. Because the great progresses of techniques including enrichment methods for glycoproteins/glycopeptides and mass techniques for biomolecules have been made, the study of glycoproteome is progressing at tremendous speeds and has achieved substantial advances during recent years, special in the large-scale detection of glycosylation sites. N-glycoproteins and classical secreted proteins have the same dependence of the endoplasmic reticulum (ER) - Golgi network, and the classical secreted proteins are almost N-glycoproteins. Thus, a large-scale glycoproteomic identification and N-glycosylation site elucidation has been performed for secreted proteins of HCC cells. Also, the glycoform has been analyzed elementally.
How tumors spread and kill their host organism remains an enigma, but not for lack of attention. A renewed focus on the problem of metastasis is now apparent, and for good reason—metastasis remains the cause of 90% of deaths from solid tumors. Hepatocellular Carcinoma (HCC) is a common malignancy worldwide and is a second leading cause of death in our country. To understand better the mechanism of HCC metastasis, the human HCC metastasis nude mice model and HCC cell lines with different metastasis potential were built up by the Liver Cancer Institute of Zhongshan Hospital, Fudan University. This work is made on the two HCC cell lines (HCCLM3 and MHCC97L) with different metastasis potential. The investigation has focused on the application of proteomic and glycoproteomic methods in the secretomic research, and a lot of valuable proteome information has been obtained.
This dissertation consists of 7 parts and the contents are summarized as follows:
In the first chapter, the seven aspects of secretomic research were summarized, also, the research progresses and the bottle neck. In addition, the advances in the research of glycosylation were summarized, specially, in the separation and enrichment of Glycoproteins/Glycopeptides. Furthermore, a review of liver proteomic study was presented. Importantly, the aims and significance of this dissertation were presented.
In the second chapter, primary research of secretome of HCC cells has been made. In the first section, three most often used concentration methods (ultrafiltration, dialysis and precipitation) were used together and compared. The merits and shortcomings of each method were discussed based on the obtained results. Four aims of this section have been achieved: the optimized method for cell culture in conditioned medium was developed; three concentration methods (ultrafiltration, dialysis and precipitation) were compared; the secretome of HCC cells with metastasis potential was studied primarily; the bottle neck of secretomic research was founded our. And all these works were basic for further research.
In the third chapter, a simple, fast, efficient and universal enrichment process for secretome was developed. Nanozeolite LTL was used to capture secreted proteins for the first time. Highly-efficient enrichment for the low molecular weight proteins (LMWPs) in secretome has been achieved, and attracted most of attentions in a specially designed experiment. Followed by 1D SDS-PAGE for protein fractionation and then by LC-ESI-MS/MS for protein identification. Totally 1474 unique proteins were confidently identified and 97 proteins of 1474 proteins were those notably with MW less than 15 KDa, which were seldom captured previously by traditional methods. Notably, the smallest MW touches 4 KDa. Many proteins identified above were found to be involved in the processes associated with the cancer development and metastasis. For instance, alpha-fetoprotein (AFP), a widely known biomarker of HCC, was detected. Osteopontin, a secreted phosphoprotein, is a significant factor in HCC metastasis, and its over-expression correlates with metastatic potential of primary hepatocellular carcinoma, and with invasiveness of liver tumor-derived cell lines in vitro. Elevated levels of matrix metalloproteinase (MMPs) have been shown in many tumors with strong association with the invasive and metastatic potentials. These observations demonstrate the contention that secretomic approach is a great way to the discovery of potential biomarkers. And the strategy that started with a capture of secreted proteins by nanozeolite LTL, followed by a separation of 1D SDS-PAGE and by an identification of LC/ESI-MS/MS has been proven to be perfect. Notably, for special enrichment of LMWPs, compared to conventional methods, this strategy was special and efficient.
In the fourth chapter, glycosylation status of HCC cells was investigated. In the first section, a large-scale detection of glycosylation sites of secreted proteins of HCC cells was successfully performed. For the enrichment of glycopeptides, capture methods with hydrophihc affinity (HA) and hydrazide chemistry (HC) were used complementarity. Using both methods in combination with nano-LC-ESI-MS/MS analysis, 300 different glycosylation sites within 194 unique glycoproteins were identified, and 172 glycosites have not been determined experimentally previously, including the new N-glycosites of CD44 and laminin etc, which have been reported to be implicated invasion and metastasis of HCC. Importantly, the utility of N-glycoproteomic strategy is a great way to profile the genuinely secreted proteins. The fact that 96.4% of the identified glycoproteins could be secretory proteins positively confirmed this good point. A direct comparison between HA and HC methods was also investigated for the first time. In brief, in terms of selectivity for glycopeptides, HC is superior to HA (92.9% VS 51.3%), however, based on the number of glycosites identified, HA outweighs HC (265 VS 159). This result based on the compared experiment could be useful for the selection of methods. In the second section, glycoforms of secretome of HCC cells were profiled primarily by multi-lectin blot. The difference of glycosylation status of two HCC cell lines (HCCLM3 and MHCC97L) with different metastasis potential were compared primarily, and based on these results more researches would be done in the near future.
In the fifth chapter, to get the information including molecular function, signal pathways and protein-protein interactions, bioinformatic analyses on the secretome identified were conducted deeply. Ingenuity Pathway Analysis system was used to excavate the secretome data from chapter two, chapter three and chapter four. The sub-network, protein-protein interactions, cell-cell interactions and crucial pathways were investigated. Also, some key node molecules were discussed. All the explorations are basic and helpful for HCC research in future.
In the sixth chapter, to exam the reliability of glycosites identified according to the mass increased of 1 Da (~(16)O labeled) or 3 Da (~(18)O labeled), deglycosylation experiments were carried out parallelly H_2~(16)O in or H_2~(18)O. As a result, the excellent consistence of glycosites from two labeled was founded. Thus, the results from ~(16)O labeled were proven to be credible, and a frequently asked question was answered by experimental data for the first time.
In the last chapter, a summary was made and a prospect was presented.
引文
[1]Hathout,Y.,Approaches to the study of the cell secretome.Expert Rev.Proteomics 2007,4,239-248.
[2]Xue,H.,Lu,B.J.,Lai,M.D.,The cancer secretome:a reservoir of biomarkers.J.Transl.Med.2008,6,1-12.
[3]Kulasingam,V.,Tissue culture-based breast cancer biomarker discovery platform.Int.J.Cancer 2008,123,2007-2012.
[4]Nickel,W.,Unconventional secretory routes:Direct protein export across the plasma membrane of mammalian cells.Traffic 2005,6,607-614.
[5]Seelenmeyer,C.,Stegrnayer,C.,Nickel,W.,Unconventional secretion of fibroblast growth factor 2 and galectin-1 does not require shedding of plasma membrane-derived vesicles.FEBS Lett.2008,582,1362-1368.
[6] Revest, J. M., DeMoerlooze, L., Dickson, C., Fibroblast growth factor 9 secretion is mediated by a non-cleaved amino-terminal signal sequence. J. Biol. Chem. 2000,275, 8083-8090.
[7] Wolters, D. A., Washburn, M. P., Yates, J. R. 3rd., An automated multidimensional protein identification technology for shotgun proteomics. Anal. Chem. 2001, 73,5683-5690.
[8] Gorg, A., Current two-dimensional electrophoresis technology for proteomics.Proteomics 2004, 4, 3665-3685.
[9] Wang, Y. et al., Nano-flow multidimensional liquid chromatography with electrospray ionization time-of-flight mass spectrometry for proteome analysis of hepatocellular carcinoma. Anal. Chim. Acta 2005, 530, 227-235.
[10] Han, D. K., Eng, J., Zhou, H., Aebersold, R. et al., Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry. Nat. Biotechnol. 2001,19, 946-951.
[11] Julka, S., Quantification in Proteomics through Stable Isotope Coding: A Review.J. Proteome Res. 2004, 3, 350-363.
[12] Wells, W. W., Wang, G. H., Baek, S. J., Shen, R. F., Comparative study of three proteomic quantitative methods, DIGE, cICAT, and iTRAQ, Using 2D Gel- or LC-MALDI TOF/TOF. J. Proteome Res. 2006, 5, 651-658.
[13] Huang, C. M., In vivo secretome sampling technology for proteomics. Proteomics Clin. Appl. 2007,1, 953-962.
[14] Bendtsen, J. D., Nielsen, H., Heijne, G. V., Brunak, S., Improved Prediction of Signal Peptides: SignalP 3.0. J. Mol. Biol. 2004, 340, 783-795.
[15] Hiller, K., Andreas, G., Maurice, S., Richard, M., PrediSi: prediction of signal peptides and their cleavage positions. Nucleic Acids Res. 2004, 32, W375-W379.
[16] Bendtsen, J. D., Jensen, L. J., Blom, N., Heijne, G. V. et al., Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng. Des. Sel. 2004,77,349-356.
[17] Klee, E. W., Daniel F. C., Scott C. F., Stephen C. E. et al., Identifying secretomes in people, pufferfish and pigs. Nucleic Acids Res. 2004, 32,1414-1421.
[18] Chen, Y. J., Zhang, Y., Yin, Y., Gao, G. et al., SPD--a web-based secreted protein database. Nucleic. Acids. Res. 2005, 33, 169-173.
[19] Grimmond, S. M., The Mouse Secretome: Functional Classification of the Proteins Secreted Into the Extracellular Environment. Genome Res. 2003, 13,1350-1359.
[20] Bushell, K. M., Large-scale screening for novel low-affinity extracellular protein interactions. Genome Res. 2008, 75, 622-630.
[21] Diehl, H. C, Stuhler, K., Scory, S. K., Volmer, M. W. et al., A catalogue of proteins released by colorectal cancer cells in vitro as an alternative source for biomarker discovery. Proteomics Clin. Appl. 2007,1, 47-61.
[22] Gr(?)nborg, ML, Kristiansen, T. Z., Iwahori, A., Chang, R. et al., Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach. Mol. Cell Proteomics 2006, 5,157-171.
[23] Dupont, A., Tokarski, C., Dekeyzer, O., Guihot, A. L. et al., Two-dimensional maps and databases of the human macrophage proteome and secretome. Proteomics 2004, 4, 1761-1778.
[24] Shah, P., Atwood Ⅲ, J. A., Orlando, R., Mubarek, H. E. et al., Comparative Proteomic Analysis of Botrytis cinerea Secretome. J Proteome Res. 2009, 5,1123-1130.
[25] Yamashita, R., Fujiwara, Y., Ikari, K., Hamada, K. et al., Extracellular proteome of human hepatoma cell, HepG2 analyzed using two-dimensional liquid chromatography coupled with tandem mass spectrometry. Mol. Cell Biochem.2007, 295, 83-92.
[26] Kulasingam, V., Proteomics Analysis of Conditioned Media from Three Breast Cancer Cell Lines. Mol. Cell Proteomics 2007, 6, 1997-2011.
[27] Huang, M., Ananthaswamy, H. N., Barnes, S., Ma, Y. L. et al., Mass spectrometric proteomics profiles of in vivo tumor secretomes: Capillary ultrafiltration sampling of regressive tumor masses. Proteomics 2006, 6,6107-6116.
[28] Mbeunkui, F., Fodstad, O., Pannell, L. K., Secretory protein enrichment and analysis: An optimized approach applied on cancer cell lines using 2D LC-MS/MS. J Proteome Res. 2006, 5, 899-906.
[29] Chevallet, M., Diemer, H., Dorssealer, A.V., Villiers, C. et al., Toward a better analysis of secreted proteins: the example of the myeloid cells secretome.Proteomics 2007, 7, 1757-1770.
[30] Pellitteri-Hahn, M. C., Warren, M. C., Didier, D. N., Winkler, E. L. et al, J.Proteome. Res. 2006, 5, 2861-2864.
[31] Zwickl, H., Traxler, E., Staettner, S., Parzefall, W. et al., A novel technique to specifically analyze the secretome of cells and tissues. Electrophoresis 2005, 26,2779-2785.
[32] Sanderson, C. M., A new way to explore the world of extracellular protein interactions. Genome Res. 2008,18, 517-520.
[33] Welsh, J. B., Sapinoso, L. M., Kern, S. G., Brown, D. A. et al., Large-scale delineation of secreted protein biomarkers overexpressed in cancer tissue and serum. Proc. Natl Acad. Sci. U.S.A. 2003,100, 3410-3415.
[34] Hagglund, P., Bunkenborg, J., Elortza, F., Jensen, O. N. et al., A new strategy for identification of N-Glycosylated proteins and unambiguous assignment of their glycosylation sites using HILIC enrichment and partial deglycosylation. J.Proteome Res. 2004, 3, 556-566.
[35] Pauline, M. R., Tim, E., Peter, C., Ian, A. W. et al., Glycosylation and the immune system. Science 2001, 291, 2370-2376.
[36] Helenius, A. Markus, A., Intracellular functions of N-Linked glycans. Science 2001,297,2376-2378.
[37] Morelle, W. K(?)vin. C., Fr(?)d(?)ric, C., Valegh, F. et al., The use of mass spectrometry for the proteomic analysis of glycosylation. Proteomics 2006, 6,3993-4015.
[38] Sun, B. Y., Ranish, J. A., Utleg, A. G., White, J. T. et al., Shotgun glycopeptide-capture approach coupled with mass spectrometry for comprehensive glycoproteomics. Mol. Cell Proteomics 2007, 6, 141-149.
[39] Udiger, H. R., Gabius, H. J., Plant lectins: Occurrence, biochemistry, functions and applications. Glycoconjugate Journal 2001, 18, 589-613.
[40] Wang, L. J., Li, F. X., Sun, W., Wu, S. Z. et al., Concanavalin A-captured glycoproteins in healthy human urine. Mol Cell Proteomics 2006, 5, 560-562.
[41] Dai, Z., Fan, J., Liu, Y. K., Zhou, J. et al., Identification and analysis of α1,6-fucosylated proteins in human normal liver tissues by a target glycoproteomic approach. Electrophoresis 2007, 28,4382-4391.
[42] Dai, Z., Liu, Y. K., Cui, J. F., Yang, P. Y. et al., Identification and analysis of altered α1,6-fucosylated glycoproteins associated with hepatocellular carcinoma metastasis. Proteomics 2006, 6, 5857-5867.
[43] Vosseller, K., Trinidad, J. C, Chalkley, R. J., SpechtP, C. G. et al., O-Linked N-Acetylglucosamine proteomics of postsynaptic density preparations using cectin weak affinity chromatography and mass spectrometry. Mol. Cell Proteomics 2006,5, 923-934.
[44] Yang, Z. P., Hancock, W. S., Approach to the comprehensive analysis of glycoproteins isolated from human serum using a multi-lectin affinity column. J ChromatogrA 2004,1053, 79-88.
[45] Zhao, J., Simeone, D. M., Heidt, D., Anderson, M. A. et al., Comparative serum glycoproteomics using lectin selected sialic acid glycoproteins with mass spectrometric analysis: Application to pancreatic cancer serum. J. Proteome Res.2006,5,1792-1802.
[46] Drake, R. R., Schwegler, E. E., Malik, G. J., Diaz, J. et al., Lectin Capture Strategies Combined with Mass Spectrometry for the Discovery of Serum Glycoprotein Biomarkers. Mol Cell Proteomics 2006, 5,1957-1967.
[47] Kaji, H., Saito, H., Yamauchi, Y., Shinkawa, T. et al., Lectin affinity capture,isotope-coded tagging and mass spectrometry to identify N-linked glycoproteins. Nat. Biotechnol 2003, 21, 667-672.
[48] Schwientek, T., Mandel, U., Roth, U., Muller, S. et al., A serial lectin approach to the mucin-type O-glycoproteome of Drosophila melanogaster S2 cells. Proteomics 2007, 7, 3264-3277.
[49] Qiu, R. Q., Regnier, R E., Use of Multidimensional Lectin Affinity Chromatography in Differential Glycoproteomics. Anal. Chem. 2005, 77,2802-2809.
[50] Liu, X. C., Boronie Acids as Ligands for Affinity Chromatography. Chinese J Chromatogr. 2006, 24, 73-80.
[51] Sparbier, K., Wenzel, T., Kostrzewa, M., Exploring the binding profiles of ConA,boronic acid and WGA by MALDI-TOF/TOF MS and magnetic particles. J.Chromatogr. B 2006, 840, 29-36.
[52] Zhang, Q. B., Tang, N., Brock, J. W. C., Mottaz, H. M. et al., Enrichment and Analysis of Nonenzymatically Glycated Peptides: Boronate Affinity Chromatography Coupled with Electron-Transfer Dissociation Mass Spectrometry. J. Proteome Res. 2007, 6, 2323-2330.
[53] Zhang, H., Li, X. J., Martin, D. B., Aebersold, R., Identificatin and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat. Biotechnol. 2003, 21, 660-666.
[54] Liu, T., Qian, W. J., Gritsenko, M. A., Xiao, W. Z. et al., High Dynamic Range Characterization of the Trauma Patient Plasma Proteome. Mol. Cell Proteomics 2006,5,1899-1913.
[55] Liu, T., Qian, W. J., Gritsenko, M. A., CampⅡ, D. G. et al., Human Plasma N-Glycoproteome Analysis by Immunoaffinity Subtraction, Hydrazide Chemistry, and Mass Spectrometry. J. Proteome Res. 2005, 4, 2070-2080.
[56] Lewandrowski, U., Moebius, J., Walter, U., Sickmann, A., Elucidation of N-Glycosylation Sites on Human Platelet Proteins. Mol. Cell Proteomics 2006, 5,226-233.
[57] Ramachandran, P., Boontheung, P., Xie, Y. M., Sondej, M. et al., Identification of N-Linked Glycoproteins in Human Saliva by Glycoprotein Capture and Mass Spectrometry. J. Proteome Res. 2006, 5, 1493-1503.
[58] Zhang, H., Yi, E. C., Li, X. J., Mallick, P. et al., High Throughput Quantitative Analysis of Serum Proteins Using Glycopeptide Capture and Liquid Chromatography Mass Spectrometry. Mol Cell Proteomics 2005, 4, 144-155.
[59] Bernhard, O. K., Kapp, E. A., Simpson, R. J., Enhanced Analysis of the Mouse Plasma Proteome Using Cysteine-Containing Tryptic Glycopeptides. J. Proteome Res. 2007, 6, 987-995.
[60] Zhou, Y., Aebersold, R., Zhang, H., Isolation of N-Linked Glycopeptides from Plasma. Anal. Chem. 2007, 79, 5826-5837.
[61] Tian, Y., Zhou, Y., Elliott, S., Aebersold, R. et al., Solid-phase extraction of N-linked glycopeptides. Nat Protoc 2007, 2, 334-339.
[62] Wada, Y., Tajiri, M., Yoshida, S., Hydrophilic Affinity Isolation and MALDI Multiple-Stage Tandem Mass Spectrometry of Glycopeptides for Glycoproteomics. Anal. Chem. 2004, 76, 6560-6565.
[63] Wuhrer, M., Koeleman, C. A. M., Hokke, C. H., Deelder, A. M., Protein Glycosylation Analyzed by Normal-Phase Nano-Liquid Chromatography-Mass Spectrometry of Glycopeptides. Anal. Chem. 2005, 77, 886-894.
[64] Ding, W., Hill, J. J., Kelly, J., Selective Enrichment of Glycopeptides from Glycoprotein Digests Using Ion-Pairing Normal-Phase Liquid Chromatography. Anal. Chem. 2007, 79, 8891-8899.
[65] Zhang, Y., Go, E. P., Desaire, H., Maximizing Coverage of Glycosylation Heterogeneity in MALDI-MS Analysis of Glycoproteins with Up to 27 Glycosylation Sites. Anal. Chem. 2008, 80, 3144-3158.
[66] Hagglund, P., Bunkenborg, J. E. F., Jensen, O. N., Roepstorff, P., A New Strategy for Identification of N-Glycosylated Proteins and Unambiguous Assignment of Their Glycosylation Sites Using HILIC Enrichment and Partial Deglycosylation. J. Proteome Res. 2004, 3, 556-566.
[67] Tajiri, M., Yoshida, S., Wada, Y., Differential analysis of site-specific glycans on plasma and cellular fibronectins: application of a hydrophilic affinity method for glycopeptide enrichment. Glycobiology 2005,15,1332-1340.
[68] Gerardo, A. M., Guo, Y., Warren, N. L., Orlando, R. et al., Tools for Glycoproteomic Analysis: Size Exclusion Chromatography Facilitates Identification of Tryptic Glycopeptides with N-linked Glycosylation Sites. J.Proteome Res. 2006, 5, 701-708.
[69] Ghesquie're, B., Buyl, L., Demol, H., Damme, J. V. et al., A New Approach for Mapping Sialylated N-Glycosites in Serum Proteomes. J. Proteome Res. 2007, 6,4304-4312.
[70] Lewandrowski, U., Zahedi, R. P., Moebius, J., WalterP, U. et al., Enhanced N-Glycosylation Site Analysis of Sialoglycopeptides by Strong Cation Exchange Prefractionation Applied to Platelet Plasma Membranes. Mol. Cell Proteomics2007,6,1399-1941.
[71] Wells, L., Vosseller, K., Cole, R. N., Matunis, M. J. et al., Mapping Sites of O-GlcNAc Modification Using Affinity Tags for Serine and Threonine Post-translational Modifications. Mol Cell Proteomics 2002,1, 791-804.
[72] Comer, F. I., Vosseller, K., Wells, L., Accavitti, M. A. et al., Characterization of a Mouse Monoclonal Antibody Specific for O-Linked iV-Acetylglucosamine. Anal.Biochem. 2001, 293, 169-177.
[73] Boeggeman, E., Ramakrishnan, B., Kilgore, C., Khidekel, N. et al., Direct Identification of Nonreducing GlcNAc Residues on N-Glycans of Glycoproteins Using a Novel Chemoenzymatic Method. Bioconjugate Chem. 2007, 18,806-814.
[74] Ball, L. E., Berkaw, M. N., Buse, M. G., Identification of the Major Site of O-Linked-N-Acetylglucosamine Modification in the C Terminus of Insulin Receptor Substrate-1. Mol Cell Proteomics 2006, 5, 313-323.
[75] Pan, S., Wang, Y., Quinn, J. F., Peskind, E. R. et al., Identification of Glycoproteins in Human Cerebrospinal Fluid with a Complementary Proteomic Approach. J. Proteome Res. 2006, 5, 2769-2779.
[76] Lee, A., Kolarich, D., Haynes, P. A., Jensen, P. H. et al., Rat Liver Membrane Glycoproteome: Enrichment by Phase Partitioning and Glycoprotein Capture. J.Proteome Res. 2009, 770-781.
[77] Monzo, A., Bonn, G. K., Guttman, A., Boronic acid-lectin affinity chromatography. 1. Simultaneous glycoprotein binding with selective or combined elution. Anal Bioanal Chem. 2007, 389, 2097-2102.
[78] Cao, J., Shen C. P., Wang, H., Yang, P.Y. et al., Identification of N-Glycosylation Sites on Secreted Proteins of Human Hepatocellular Carcinoma Cells with a Complementary Proteomics Approach. J. Proteome Res. 2009, 8, 662-672.
[79] Kubota, K., Sato, Y., Suzuki, Y., Naoko, G. I. et al., Analysis of Glycopeptides Using Lectin Affinity Chromatography with MALDI-TOF Mass Spectrometry.Anal Chem. 2008, 80, 3693-3698.
[80] Chen, R., Zou, H. F., Sun, D. G., Han, G. H. et al., Glycoproteomics Analysis of Human Liver Tissue by Combination of Multiple Enzyme Digestion and Hydrazide Chemistry. J. Proteome Res. 2009, 651-661.
[81] Andre, M., Morelle, W., Planchon, S., Milhiet, P. E. et al., Glycosylation status of the membrane protein CD9P-1. Proteomics 2007, 7, 3880-3895.
[82] Bleckmann, C., Glycomic Analysis of N-Linked Carbohydrate Epitopes from CD24 of Mouse Brain. J. Proteome Res. 2009, 8, 567-582.
[83] Montesino, R., N-Glycosylation Pattern of E2 Glycoprotein from Classical Swine Fever Virus. J. Proteome Res. 2009, 8, 546-555.
[84] Patwa, T. H., Screening of Glycosylation Patterns in Serum Using Natural Glycoprotein Microarrays and Multi-Lectin Fluorescence Detection.Anal Chem.2006,78,6411-6421.
[85]Zhao,J.,Glycoprotein Microarrays with Multi-Lectin Detection:Unique Lectin Binding Patterns as a Tool for Classifying Normal,Chronic Pancreatitis and Pancreatic Cancer Sera.J.Proteome Res.2007,6,1864-1874.
[86]Li,C.,Pancreatic Cancer Serum Detection Using a Lectin/Glyco-Antibody Array Method.J.Proteome Res.2009,8,483-492.
[87]Tang,Z.Y.,Ye,S.L.,Liu,Y.K.et al.,A decade's studies on metastasis of hepatocellular carcinoma.J.Cancer Res.Clin.Oncol.2004,130,187-196.
[88]汤钊猷.概述.见:汤钊猷,主编.肿瘤转移复发的基础与临床.第一版.上海:上海科技出版社,2003,1-24.
[89]汤钊猷.肝癌转移复发的早期发现与早期诊断.见汤钊猷,主编.肿瘤转移复发的基础与临床.第一版.上海:上海科技出版社,2003,289-293.
[90]Li,C.,Hong,Y.,Tan,Y.X.,Zhou,H.et al.,Accurate qualitative and quantitative proteomic analysis of clinical hepatocellular carcinoma using laser capture microdissection coupled with isotope-coded affinity tag and two-dimensional liquid chromatography mass spectrometry.Mol.Cell Proteomics 2004,3,399-409.
[91]Sun,B.S.,Dong,Q.Z.,Ye,Q.H.,Sun,H.J.et al.,Lentiviral-mediated miRNA against osteopontin suppresses tumor growth and metastasis of human hepatocellular carcinoma.Hepatology 2008,48,1834-1842.
[92]Srisomsap,C.,Sawangareetrakul,P,Subhasitanont,P,Panichakul,T.et al.,Proteomic analysis of cholangiocarcinoma cell line.Proteomics 2004,4,1135-1144.
[93]Fujii,K.,Kondo,T.,Yokoo,H.,Yamada,T.et al.,Proteomic study of human hepatocellular carcinoma using two-dimensional difference gel electrophoresis with saturation cysteine dye.Proteomics 2005,5,1411-1422.
[94]Zhai,Y.,Zhou,G,Deng,G,Xie,W.et al.,Estrogen Receptor Alpha Polymorphisms Associated with Susceptibility to Hepatocellular Carcinoma in Hepatitis B Virus Carriers.Gastroenterology 2006,130,2001-2009.
[95]Yokoo,H.,Kondo,T.,Fujii,K.,Yamada,T.et al.,Proteomic signature corresponding to alpha fetoprotein expression in liver cancer cells.Hepatology 2004,40,609-617.
[96] Song, P. M, Zhang, Y., He, Y. F., Bao, H. M. et al., Bioinformatics analysis of metastasis-related proteins in hepatocellular carcinoma. World J. Gastroenterol.2008,74,5816-5822.
[97] Ding, S. J., Li, Y., Shao, X. X. et al., Proteome analysis of hepatocellular carcinoma cell strains, MHCC97-H and MHCC97-L, with different metastasis potentials. Proteomics 2004, 4, 982-994.
[98] Ding, S. J., Li, Y., Tan, Y. X., Jiang, M. R. et al., From proteomic analysis to clinical significance: overexpression of cytokeratin 19 correlates with hepatocellular carcinoma metastasis. Mol. Cell Proteomics 2004, 3, 73-81.
[99] Cui, J. F., Liu, Y. K., Pan, B. S., Song, H. Y. et al., Differential proteomic analysis of human hepatocellular carcinoma cell line metastasis-associated proteins. J.Cancer Res. Clin. Oncol. 2004,130, 615-622.
[100] Li, C., Tan, Y. X., Zhou, H., Ding, S. J. et al., Proteomic analysis of hepatitis B virus-associated hepatocellular carcinoma: Identification of potential tumor markers. Proteomics 2005, 5, 1125-1139.
[101] Yang, G. H., Fan, J., Xu, Y., Qiu, S. J. et al., Osteopontin Combined with CD44,a Novel Prognostic Biomarker for Patients with Hepatocellular Carcinoma Undergoing Curative Resection. Oncologist 2008,13, 1155-1165.
[102] Yang, X. R., Xu, Y., Shi, G. M., Fan, J. et al., Cytokeratin 10 and cytokeratin 19: Predictive markers for poor prognosis in hepatocellular carcinoma patients after curative resection. Clin. Cancer Res. 2008,14, 3850-3859.
[103] Zeindl-Eberhart, E., Haraida, S., Liebmann, S., Jungblut, P. R. et al, Detection and identification of tumor-associated protein variants in human hepatocellular carcinomas. Hepatology 2004, 39, 540-549.
[104] Muramatsu, T., Muramatsu, H., Glycosaminoglycan-binding cytokines as tumor markers. Proteomics 2008, 8, 3350-3359.
[105] Zhu, X. D., Zhang, J. B., Zhuang, P. Y., Zhu, H. G. et al., High expression of macrophage colony-stimulating factor in peritumoral liver tissue is associated with poor survival after curative resection of hepatocellular carcinoma. J. Clin. Oncol. 2008, 26, 2707-2716.
[106] Lau, S. H., Sham, J. S. T., Xie, D., Tzang, C. H. et al., Clusterin plays an important role in hepatocellular carcinoma metastasis. Oncogene 2006, 25,1242-1250.
[107] Yang, Z. F., Ho, D. W., Ng, M. N, Lau, C. K. et al, Significance of CD90(+) cancer stem cells in human liver cancer. Cancer Cell 2008,13,153-166.
[108] Yang, Z. F., Ho, D. W., Lam, C. T., Luk, J. M. et al, Identification of brain-derived neurotrophic factor as a novel functional protein in hepatocellular carcinoma. Cancer Res. 2005, 65, 219-225.
[109] Lane, C. S., Nisar, S., Griffiths, W. J., Fuller, B. J. et al, Identification of cytochrome P450 enzymes in human colorectal metastases and the surrounding liver: a proteomic approach. Eur. J. Cancer 2004, 40, 2127-2134.
[110] Melle, C, Kaufinann, R., Hommann, M., Bleul, A. et al, Proteomic profiling in microdissected hepatocellular carcinoma tissue using ProteinChip technology. Int. J. Oncol. 2004, 24, 885-891.
[111] Kim, W., Lim, S.O., Kim, J. S., Ryu, Y H. et al, Comparison of proteome between hepatitis B virus- and hepatitis C virus-associated hepatocellular carcinoma. Clin. Cancer Res. 2003, 9, 5493-5500.
[112] Yokoyama, Y., Kuramitsu, Y., Takashima, M. et al, Proteomic profiling of proteins decreased in hepatocellular carcinoma from patients infected with hepatitis C virus. Proteomics 2004, 4, 2111-2116.
[113] Fan, H. Z., Liu, H., Zhang, C, Liu, Y. K. et al, Comparative proteomics and molecular mechanical analysis in CDA-Ⅱ induced therapy of LCI-D20 hepatocellular carcinoma model. J. Cancer Res. Clin. Oncol. 2009, 135,591-602.
[114] Steel, L. F., Shumpert, D., Trotter, M., Seeholzer, S. H. et al., A strategy for the comparative analysis of serum proteomes for the discovery of biomarkers for hepatocellular carcinoma. Proteomics 2003, 3, 601-609.
[115] Qiu, J. G., Fan, J., Liu, Y. K., Gao, D. M. et al., Screening and detection of portal vein tumor thrombi-associated serum low molecular weight protein biomarkers in human hepatocellular carcinoma. J. Cancer Res. Clin. Oncol. 2008,134, 299-305.
[116] Li, Y., Tang, Z. Y., Tian, B., Ye, S. L. et al Serum CYFRA21-1 level reflects hepatocellular carcinoma metastasis: study in nude mice model and clinical patients. J. Cancer Res. Clin. Oncol. 2006,132, 515-520.
[117] Paradis, V., Degos, F., Dargere, D., Pham, N. et al., Identification of a new marker of hepatocellular carcinoma by serum protein profiling of patients with chronic liver diseases. Hepatology 2005, 41, 40-47.
[118] Schwegler, E. E., Cazares, L., Steel, L. R, Adam B. L. et al, SELDI-TOF MS profiling of serum for detection of the progression of chronic hepatitis C to hepatocellular carcinoma. Hepatology 2005, 41, 634-642.
[119] Block, T. M, Use of targeted glycoproteomics to identify serum glycoproteins that correlate with liver cancer in woodchucks and humans. Proc. Natl. Acad.Sci. U.S.A. 2005,102, 779-784.
[120] Zheng, J. J., He, R C., Report of the 9th HLPP Workshop. Proteomics 2008, 8,3420-3423.
[1]Gaorav,P.G.,Massague,J.,Cancer Metastasis:Building a Framework.Cell 2006,127,679-695.
[2]Sun,F.X.,Tang,Z.Y.,Liu,K.D.,Ye,S.L.et al.,Establishment of a metastatic model of human hepatocellular carcinoma in nude mice via orthotopic implantation of histologically intact tissues.Int.J.Cancer 1996,66,239-243.
[3]Tian,J.,Tang,Z.Y.,Ye,S.L.,Liu,Y.K.et al.,New human hepatocellular carcinoma(HCC) cell line with highly metastatic potential(MHCC97) and its expressions of the factors associated with metastasis.Br.J.Cancer 1999,81,814-821.
[4]Tang,Z.Y.,Zhou,X.D.,Lin,Z.Y.,Yang,B.H.et al.,Surgical treatment of hepatocellular carcinoma and related basic research with special reference to recurrence and metastasis.Chinese Med.J.1999,112,887-891.
[5]Li,Y.,Tang,Z.Y.,Ye,S.L.,Liu,Y.K.et al.,Establishment of cell clones with different metastatic potential from the metastatic hepatocellular carcinoma cell line MHCC97.World J.Gastroenterol 2001,7,630-636.
[6]Qin,L.X.,Tang,Z.Y.,Ye,S.L.,Liu,Y.K.et al.,Chromosome 8p deletion is associated with metastasis of human hepatocellular carcinoma when high and low metastatic models are compared.J.Cancer Res.Clin.Oncol.2001,127,482-488.
[7]李雁,汤钊猷,叶胜龙,刘银坤等,体内连续筛选法建立自发性肺转移人肝癌细胞系.中华医学杂志2002,82,601-605.
[8]Kazuyasu,F.J.,Tadashi,K.,Hideki,Y.,Tesshi,Y.et al.,Proteomic study of human hepatocellular carcinoma using two-dimensional difference gel electrophoresis with saturation cysteine dye.Proteomics 2005,5,1411-1422.
[9]Song,P.M.,Zhang,Y.,He,Y.F.,Bao,H.M.et al.,Bioinformatics analysis of metastasis-related proteins in hepatocellular carcinoma.World J.Gastroenterol.2008,14,5816-5822.
[10]Yuan,Q.,An,J.,Liu,D.G.,Sun,L.et al.,Proteomic analysis of differential protein expression in a human hepatoma revertant cell line by using an improved two-dimensional electrophoresis procedure combined with matrix assisted laser desorption/ionization-time of flight-mass spectrometry.Electrophoresis 2004,25,1160-1168.
[11]Ding,S.J.,Li,Y.,Shao,X.X.Zhou;H.et al.,Proteome analysis of hepatocellular carcinoma cell strains,MHCC97-H and MHCC97-L,with different metastasis potentials.Proteomics 2004,4,982-994.
[12]Shen,H.L.,Cheng,G.,Fan H.Z.,Liu,Y.K.et al.,Expressed proteome analysis of human hepatocellular carcinoma in nude mice(LCI-D20) with high metastasis potential.Proteomics 2006,6,528-537.
[13]钦伦秀,主编,肝癌的分子诊断与预测.第一版.上海:上海科技教育出版社,2004.163-172.
[14]McClelland,C.M.,Gullick,W.J.,Proteomic identification of secreted proteins as surrogate markers for signal transduction inhibitor activity.Br.J.Cancer 2007,96,284-289.
[15] Zvonic, S., Lefevre, M., Kilroy, G, Floyd, Z. E. et al, Secretome of primary cultures of human adipose-derived stem cells-Modulation of serpins by adipogenesis. Mol. Cell Proteomics 2007, 6,18-28.
[16] Pardo, M., Garcia, A., Antrobus, R., Blanco, M. J. et al, Biomarker discovery from uveal melanoma secretomes: Identification of gp100 and cathepsin D in patient serum. J. Proteome Res. 2007, 6, 2802-2811.
[17] Zwickl, H., Traxler, E., Staettner, S., Parzefall, W. et al., A novel technique to specifically analyze the secretome of cells and tissues. Electrophoresis 2005, 26,2779-2785.
[18] Trost, M., Wehmhoner, D. D., K(?)rst, U., Dieterich, G. et al, Comparative proteome analysis of secretory proteins from pathogenic and nonpathogenic Listeria species. Proteomics 2005, 5,1544-1557.
[19] Thouvenot, E., Lafon-Cazal, M., Demettre, E., Jouin, P. et al, The proteomic analysis of mouse choroid plexus secretome reveals a high protein secretion capacity of choroidal epithelial cells. Proteomics 2006, 6, 5941-5952.
[20] Kulasingam, V., Diamandis, E. P., Proteomics analysis of conditioned media from three breast cancer cell lines. Mol. Cell Proteomics 2007, 6, 1997-2011.
[21] Sardana, G., Marshall, J., Diamandis, E.P., Discovery of candidate tumor markers for prostate cancer via proteomic analysis of cell culture-conditioned medium. Clin. Chem. 2007, 53, 429-437.
[22] Wu, C. C., Chien, K. Y., Tsang, N. M., Chang, K. P. et al, Cancer cell-secreted proteomes as a basis for searching potential tumor markers: Nasopharyngeal carcinoma as a model. Proteomics 2005, 5, 3173-3182.
[23] Chevallet, M., Diemer, H., Dorssealer, A.V., Villiers, C. et al, Toward a better analysis of secreted proteins: the example of the myeloid cells secretome. Proteomics 2007, 7,1757-1770.
[24] Bendtsen, J. D., Jensen, L. J., Blom, N., Heijne, G. V. et al, Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng. Des. Sel.2004,77,349-356.
[25] Krogh, A., Larsson, B., Heijne, G., Sonnhammer, E. L. L. et al, Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 2001, 305, 567-580.
[26] Ashburner M., Ball C. A., Blake J. A., Botstein, D. et al, Gene Ontology: tool for the unification of biology. Nat. Genet. 2000, 25, 25-29.
[27] Sun, B. S., Dong, Q. Z., Ye, Q. H., Sun, H. J. et al, Lentiviral-mediated miRNA against osteopontin suppresses tumor growth and metastasis of human hepatocellular carcinoma. Hepatology 2008, 48, 1834-42.
[28] Ye, Q. H., Qin, L. X., Forgues, M., He, P. et al, Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning. Nat. Med. 2003, 9, 416-423.
[29] Lau, S. H., Sham, J. S. T., Xie, D., Tzang, C. H. et al, Clusterin plays an important role in hepatocellular carcinoma metastasis. Oncogene 2006, 25,1242-1250.
[30] http://www.iscb.org/cms_addon/conferences/ismb2005/posters_list.html, Poster C-95
[31] Ding, S. J., Li, Y, Tan, Y. X., Jiang, M. R. et al, From proteomic analysis to clinical significance: overexpression of cytokeratin 19 correlates with hepatocellular carcinoma metastasis. Mol. Cell Proteomics 2004, 3, 73-81.
[32] Cui, J. F., Liu, Y K., Pan, B. S., Song, H. Y. et al, Differential proteomic analysis of human hepatocellular carcinoma cell line metastasis-associated proteins. J. Cancer Res. Clin. Oncol. 2004, 130, 615-622.
[33] Gr(?)nborg, M., Kristiansen, T. Z., Iwahori, A., Chang, R. et al, Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach. Mol. Cell Proteomics 2006, 5, 157-171.
[1]Mbeunkui,F.,Fodstad,O.,Pannell,L.K.,Secretory protein enrichment and analysis:An optimized approach applied on cancer cell lines using 2D LC-MS/MS.J.Proteome Res.2006,5,899-906.
[2]http://www.iscb.org/cms_addon/conferences/ismb2005/posters_list.html,Poster C-95
[3]Chen,Y.J.,Zhang,Y.,Yin,Y.,Gao,G.et al.,SPD--a web-based secreted protein database.Nucleic.Acids.Res.2005,33,169-173.
[4]Zhang,Y.H.,Liu,Y.,Kong,J.L.,Yang,P.Y.et al.,Efficient Proteolysis System:A Nanozeolite-Derived Microreactor.small 2006,2,1170-1173.
[5]Dahm,(?).,Eriksson,H.,Ultra-stable zeolites - a tool for in-cell chemistry.J.Biotechnol 2004,111,279-290.
[6]Andersson,L.I.M.,Eriksson,H.,De-aluminated Zeolite Y as a Tool to Study Endocytosis,A delivery system revealing differences between human peripheral dendritic cells.Stand.J.Immunol.2007,66,52-61.
[7]Zhang,Y.H.,Yu,X.J.,Wang,X.Y.,Shan,W.et al.,Zeolite nanoparticles with immobilized metal ions:isolation and MALDI-TOF-MS/MS identification of phosphopeptides.Chem.Commun.2004,2882-2883.
[8]Zhang,Y.H.,Wang,X.Y.,Shan,W.,Wu,B.Y.et al.,Enrichment of low-abundance peptides and proteins on zeolite nanocrystals for direct MALDI-TOF MS analysis.Angew.Chem.Int.Ed.2005,44,615-617.
[9]Hu,Y.Y.,Liu,C.,Zhang,Y.H.,Ren,N.et al.,Microwave assisted hydrothermal synthesis of nanozeolites with controllable size. Microporous Mesoporous Mater.2009,779,306-314.
[10] Sch(?)gger, H., Jagow, G. V., Tricine-sodium dodecyl sulfate-polyacrylamid gel electroporesis for the separation of proteins in the range from 1 to 100 kDa. Anal.Biochem. 1987,766,368-379.
[11] Sch(?)gger, H. et al, Tricine-SDS-PAGE. Nat Protoc. 2006, 7, 16-22.
[12] Bendtsen J. D., Jensen L. J., Blom N., Heijne G. V. et al, Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng. Des. Sel.2004,77,349-356.
[13] Krogh A., Larsson B., Heijne G., Sonnhammer E. L. L. et al., Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 2001, 305, 567-580.
[14] Volmer, M. W., St(?)hler, K., Zapatka, M., Schoneck, A. et al., Differential proteome analysis of conditioned media to detect Smad4 regulated secreted biomarkers in colon cancer. Proteomics 2005, 5, 2587-2601.
[15] Lou, X. M., Xiao, T., Zhao, K., Wang, H. et al., Cathepsin D is secreted from M-BE cells: Its potential role as a biomarker of lung cancer. J. Proteome Res. 2007, 6, 1083-1092.
[16] Dupont, A., Tokarski, C, Dekeyzer, O., Guihot, A. L. et al., Two-dimensional maps and databases of the human macrophage proteome and secretome.Proteomics 2004, 4, 1761-1778.
[17] Wu, C. C, Cheng, H. C, Chen, S. J., Liu, H. P. et al., Identification of collapsin response mediator protein-2 as a potential marker of colorectal carcinoma by comparative analysis of cancer cell secretomes. Proteomics 2008, 8, 316-332.
[18] Feng, J. T., Liu, Y. K., Song, H. Y., Dai, Z. et al., Heat-shock protein 27: A potential biomarker for hepatocellular carcinoma identified by serum proteome analysis. Proteomics 2005, 5, 4581-4588.
[19] Sun, W., Xing, B. C, Sun, Y., Du, X. J. et al., Proteome Analysis of Hepatocellular Carcinoma by Two-dimensional Difference Gel Electrophoresis: Novel Protein Markers in Hepatocellular Carcinoma Tissues. Mol. Cell Proteomics 2007, 6, 1798-1808.
[20] Yokoyama, Y., Kuramitsu, Y., Takashima, M., Iizuka, N. et al., Protein level of apolipoprotein E increased in human hepatocellular carcinoma. Int. J. Oncol.2006, 28, 625-631.
[21]Ye,Q.H.,Qin,L.X.,Forgues,M.,He,P.et al.,Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning.Nat.Med.2003,9,416-423.
[22]Donato,R.,S100:a multigenic family of calcium-modulated proteins of the EF-hand type with intracellular and extracellular functional roles.Int.J.Biochem.Cell Biol.2001,33,637-668.
[23]姬峻芳,刘芝华,赵立群,S100蛋白家族与肿瘤,国外医学肿瘤学分册2002,29,261-263
[24]Komatsu,K.,Kobune-Fujiwara,Y.,Andoh,A.,Ishiguro,S.et al.,Increased expression of S100A6 at the invading fronts of the primary lesion and liver metastasis in patients with colorectal adenocarcinoma.Br.J.Cancer 2000,83,769-774.
[25]Semov,A.,Moreno,M.J.,Onichtchenko,A.,Abulrob,A.et al.,Metastasis-associated protein S100A4 induces angiogenesis through interaction with Annexin Ⅱ and accelerated plasmin formation.J.Biol.Chem.2005,280,20833-20841.
[26]Hermani,A.,Hess,J.,DeServi,B.,Medunjanin,S.et al.,Calcium-binding proteins S100A8 and S100A9 as novel diagnostic markers in human prostate cancer.Clin.Cancer Res.2005,11,5146-5152.
[27]Hemandas,A.K.,Salto-Tellez,M.,Maricar,S.H.,Leong,A.et al.,Metastasis-Associated Protein S100A4 - A Potential Prognostic Marker for Colorectal Cancer.J.Surg.Oncol.2006,93,498-503.
[28]Cui,J.F.,Liu,Y.K.,Zhang,L.J.,Shen,H.L.et al.,Identification of metastasis candidate proteins among HCC cell lines by comparative proteome and biological function analysis of S100A4 in metastasis in vitro.Proteomics 2006,6,5953-5961.
[29]Oida,Y.,Yamazaki,H.,Tobita,K.,Mukai,M.et al.,Increased S100A4expression combined with decreased E-cadherin expression predicts a poor outcome of patients with pancreatic cancer.Oncol.Rep.2006,16,457-463.
[30]Yamashita,R.,Fujiwara,Y.,Ikari,K.,Hamada,K.et al.,Extracellular proteome of human hepatoma cell,HepG2 analyzed using two-dimensional liquid chromatography coupled with tandem mass spectrometry.Mol.Cell Biochem.2007,298,83-92.
[31]Zwickl,H.,Traxler,E.,Staettner,S.,Parzefall,W.et al.,A novel technique to specifically analyze the secretome of cells and tissues.Electrophoresis 2005,26,2779-2785.
[32]Ji,X.N.,Ye,S.L.,Li,Y.,Tian,B.et al.,Contributions of lung tissue extracts to invasion and migration of human hepatocellular carcinoma cells with various metastatic potentials.J.Cancer Res.Clin.Oncol.2003,129,556-564.
[33]Tang,Z.Y.,Ye,S.L.,Liu,Y.K.,Qin,L.X.et al.,A decade's studies on metastasis of hepatocellular carcinoma.J.Cancer Res.Clin.Oncol.2004,130,187-196.
[1]Lewandrowski,U.,Moebius,J.,Walter,U.,Sickmann,A.et al.,Elucidation of N-Glycosylation Sites on Human Platelet Proteins.Mol.Cell Proteomics 2006,5,226-233.
[2]Pan,S.,Wang,Y.,Quinn,J.F.,Peskind,E.R.et al.,Identification of Glycoproteins in Human Cerebrospinal Fluid with a Complementary Proteomic Approach.J.Proteome.Res.2006,5,2769-2779.
[3]Danielle,H.D.Bertozzi,C.R.,Glycans in Cancer and Inflammation Potential for Therapeutics and Diagnostics.Nat.Rev.Drug Discov.2005,4,477-488.
[4]Roth,J.,Protein N-glycosylation along the secretory pathway:relationship to organelle topography and function,protein quality control,and cell interactions.Chem.Rev.2002,102,285-303.
[5]Tretter,V.,Altmann,F.,Marz,L.et al.,Peptide-N4-(N-acetyl-_-glucosaminyl)asparagine amidase F cannot release glycans with fucose attached _133 to the asparagine-linked N-acetylglucosamine residue.Eur.J.Biochem.1991,199,647-652
[6]Liu,X.,Ma,L.,Li,J.J.,Recent Developments in the Enrichment of Glycopeptides for Glycoproteomics.Anal.Lett.2008,41,268-277.
[7]Sun,B.Y.,Ranish,J.A.,Utleg,A.G.,White,J.T.,Shotgun Glycopeptide-Capture Approach Coupled with Mass Spectrometry for Comprehensive Glycoproteomics.Mol.Cell Proteomics 2007,6,141 - 149.
[8]Zhou,Y.,Aebersold,R.,Zhang,H.,Isolation of N-Linked Glycopeptides from Plasma.Anal.Chem.2007,79,5826-5837.
[9]Wada,Y.,Tajiri,M.,Yoshida,S.et al.,Hydrophilic Affinity Isolation and MALDI Multiple-Stage Tandem Mass Spectrometry of Glycopeptides for Glycoproteomics. Anal. Chem. 2004, 76, 6560-6565.
[10 Tajiri, M., Yoshida, S., Wada, Y. et al, Differential analysis of site-specific glycans on plasma and cellular fibronectins: application of a hydrophilic affinity method for glycopeptide enrichment. Glycobiology 2005, 75, 1332-1340.
[11] Zhang, Q. B., Tang, N, Brock, J. W. C., Mottaz, H. M. et al, Enrichment and Analysis of Nonenzymatically Glycated Peptides: Boronate Affinity Chromatography Coupled with Electron-Transfer Dissociation Mass Spectrometry. J. Proteome Res. 2007, 6, 2323-2330.
[12] Kreunin, P., Zhao, J., Rosser, C., Urquidi, V. et al, Bladder Cancer Associated Glycoprotein Signatures Revealed by Urinary Proteomic Profiling. J. Proteome Res. 2007, 6, 2631-2639.
[13] Liu, T., Qian, W. J., Gritsenko, M. A., Xiao, W. Z. et al, High Dynamic Range Characterization of the Trauma Patient Plasma Proteome. Mol. Cell Proteomics 2006,5,1899-1913.
[14] Chen, M., Ying, W. T., Song, Y. P., Liu, X. et al, Analysis of human liver proteome using replicate shotgun strategy. Proteomics 2007, 7, 2479-2488.
[15] Atwood Ⅲ, J. A., Sahoo, S. S., Alvarez-Manilla, G., Alvarez-Manilla, G. et al,Simple modification of a protein database for mass spectral identification of N-linked glycopeptides. Rapid Commun. Mass Spectro. 2005,19, 3002-3006.
[16] Pellitteri-Hahn, M. C., Warren, M. C., Didier, D. N., Winkler, E. L. et al,Improved Mass Spectrometric Proteomic Profiling of the Secretome of Rat Vascular Endothelial Cells. J. Proteome. Res. 2006, 5, 2861-2864.
[17] Chevallet, M., Diemer, H., Dorssealer, A.V., Villiers, C. et al, Toward a better analysis of secreted proteins: the example of the myeloid cells secretome.Proteomics 2007', 7, 1757-1770.
[18] Krueger, K. E., Srivastava, S., Posttranslational Protein Modifications: Current Implications for Cancer Detection, Prevention, and Therapeutics. Mol Cell Proteomics 2006, 5, 1799-1810.
[19] Gonzalez-Moles, M. A., Esteban, F., Bravo-Perez, J. J., Bravo-Perez, M. et al,Adhesion molecule CD44 expression in non-tumor epithelium adjacent to laryngeal cancer. Oncology 2006, 29, 9-13.
[20]Qin,L.X.,Tang,Z.Y.et al.,World J.Gastroenterol 2002,8,385-392.
[21]Qin,L.X.,Tang,Z.Y.Liu,Y.K.,Qin,L.X.et al.,A decade's studies on metastasis of hepatocellular carcinoma.J.Cancer Res.Clin.Oncol.2004,130,497-513.
[22]Fedorowski,A.,Steciwko,A.,Rabczynski,J.et al.,Med.Sci.Monit.2004,10,BR144-BR150.
[23]Liu,X.C.,Boronic Acids as Ligands for Affinity Chromatography.Chinese J Chromatogr 2006,24,73-80.
[24]王山,李钰,蛋白质的糖组学研究进展.细胞生物学杂志2006,28,127-131.
[25]International Human Genome Sequencing Consortium.Nature 2004,431,915-916.
[26]Varki,A.,Cummings,R.,Esko,J.,Freeze,H.et al.,Essentials of Glycobiology.Cold Spring Harbor Laboratory Press,Cold Spring Harbor,New York 1999.
[27]Candiano,G.,Bruschi,M.,Musante,L.,Santucci,L.et al.,Blue silver:A very sensitive colloidal Coomassie G-250 staining for proteome analysis.Electrophoresis 2004,25,1327-1333.
[28]de Albuquerque Garcia Redondo,P.,Nakamura,C.V.,de Souza,W.,Morgado-Diaz,J.,A.,Differential expression of sialic acid and N-acetylgalactosamine residues on the cell surface of intestinal epithelial cells according to normal or metastatic potential.J.Histochem.Cytochem.2004,52,629-640.
[29]Pilobello,K.T.,Krishnamoorthy,L.,Slawek,D.,Mahal,L.K.et al.,Development of a lectin microarray for the rapid analysis of protein glycopatterns mahal.Chem.Bio.Chem.2005,6,985-989.
[30]Kuno,A.,Uchiyama,N.,Koseki-Kuno,S.,Ebe,Y.,Evanescent-field fluorescence-assisted lectin microarray:a new strategy for glycan profiling.Nature Methods 2005,2,851-856.
[31]Angeloni,S.,Ridet,J.L.,Kusy,N.,Gao,H.et al.,Glycoprofiling with micro-arrays of glycoconjugates and lectins.Glycobiology 2005,15,31-41.
[1]Kremer,A.,Schneider,R.,Terstappen,G.C.,A bioinformatics perspective on proteomics:data storage,analysis,and integration.Biosci Rep.2005,25,95-106.
[2]Englbrecht,C.C.,Facius,A.,Bioinformatics challenges in proteomics.Comb Chem High Throughput Screen 2005,8,705-715.
[3]Thorgeirsson,S.S.,Lee,J.S.,Grisham,J.W.et al.,Functional genomics of hepatocellular carcinoma. Hepatology 2006, 43, S145-150.
[4] Chagoyen, M., Carmona-Saez, P., Shatkay, H. et al, Discovering semantic features in the literature: a foundation for building functional associations. BMC Bioinformatics 2006, 7, 41.
[5] Armano, G., Mancosu, G., Milanesi, L. et al, A hybrid genetic-neural system for predicting protein secondary structure. BMC Bioinformatics 2005, 6 Suppl 4, S3.
[6] Fung, E. T., Weinberger, S. R., Gavin, E. et al, Bioinformatics approaches in clinical proteomics. Expert Rev. Proteomics 2005, 2, 847-862.
[7] Cai, Y. D., Chou, K. C, Predicting enzyme subclass by functional domain composition and pseudo amino acid composition. J Proteome Res. 2005, 4,967-971.
[8] Uren, A. G., Kool, I, Matentzoglu, K., de Ridder, J. et al, Large-Scale Mutagenesis in pl9(ARF)- and p53-Deficient Mice Identifies Cancer Genes and Their Collaborative Networks. Cell 2008,133, 727-741.
[9] Ray, S., Britschgi, M., Herbert, C, Takeda-Uchimura Y. et ai, Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins.Nat. Med. 2007,13, 1359-1362.
[10] Cloonan, N., Forrest, A. R., Kolle, G., Gardiner, B. B. et al, Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat. Methods 2008,5,613-619.
[11] Liu, X. Y. et al, Comparative proteomics and correlated signaling network of rat hippocampus in the pilocarpine model of temporal lobe epilepsy. Proteomics 2008,8, 582-603.
[12] Liu, H., Sadygov, R. G., Yates, J. R. et al, A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem. 2004,75,4193-4201.
[13] Kersey, P. J., Duarte, J., Williams, A., Karavidopoulou, Y. et al, The International Protein Index: An integrated database for proteomics experiments.2004, 4, 1985-1988.
[14] Liu, T., Qian, W. J., Gritsenko, M. A., Xiao, W. Z. et al, High Dynamic Range Characterization of the Trauma Patient Plasma Proteome. Mol. Cell Proteomics 2006, 5, 1899-1913.
[15]Ho,E.,Hayen,A.,Wilkins,M.R.,Characterisation of organellar proteomes:A guide to subcellular proteomic fractionation and analysis.Proteomics 2006,6,5746-5757.
[16]Qin,L.X.,Tang,Z.Y.Liu,Y.K.,Qin,L.X.et al.,A decade's studies on metastasis of hepatocellular carcinoma.J.Cancer Res.Clin.Oncol.2004,130,497-513.
[17]石作珍,赵春娥,成年肝癌与免疫球蛋白检测水平分析.实用医技杂志2006,13,3782.
[18]Wang,N.,Lin,K.K.,Lu,Z.,Lam,K.S.et al.,The LIM-only factor LMO4regulates expression of the BMP7.gene through an HDAC2-dependent mechanism,and controls cell proliferation and apoptosis of mammary epithelial cells.Oncogene 2007,26,6431-41.
[19]Yang,G.H.,Fan,J.,Xu,Y.,Qiu,S.J.et al.,Osteopontin Combined with CD44,a Novel Prognostic Biomarker for Patients with Hepatocellular Carcinoma Undergoing Curative Resection.Oncologist 2008,13,1155-1165.
[20]Bedossa,P.,Peltier,E.,Terris,B.,Franco,D.et al.,Transforming growth factor-beta 1(TGF-beta 1) and TGF beta 1 receptor in normal cirrhotic,and neoplastic human livers.Hepatology 1995,21,760-766.
[21]Tang,Z.Y.,Ye,S.L.,Liu,Y.K.,Qin,L.X.et al.,A decade's studies on metastasis of hepatocellular carcinoma.J.Cancer Res.Clin.Oncol.2004,130,187-196.
[22]Sjoeblom,T.,Jones,S.,Wood,L.D.et al.,The consensus coding sequences of human breast and colorectal cancers.Science,2006,314,268-274.
[23]卜文,黄晓武,汤钊猷,MMP-2在肝细胞癌侵袭转移中的作用.中华医学杂志1997,79,661-664.
[24]Wilson,C.L.,Heppner,K.J.,Labosky,P.A.et al.,Intestinal tumorigenesis is suppressed in mice lacking the metalloproteinase matrilysin.PNAS 1997,94,1402-1407.
[25]Gu,J.G.,Sato,Yu,Y.,Kariya,Y.,A Mutual Regulation between Cell-Cell Adhesion and N-Glycosylation:Implication of the Bisecting GlcNAc for Biological Functions.Journal of Proteome Research 2009,8,431-435.
[26]汤钊猷.粘附分子与肝癌转移.见汤钊猷,主编.肿瘤转移复发的基础与临 床.第一版.上海:上海科技出版社,2003,113-124.
[27]Giancotti,F.G.,Mainiero,F.et al.,Integrin-mediated adhesion and signaling in tumorgenesis.Biochem Biophys Acta.1994,1198,47-64.
[1]Liu,T.,Qian,W.J.,Gritsenko,M.A.,CampⅡ,D.G.et al.,Human plasma N-glycoproteome analysis by immunoaffinity subtraction,hydrazide chemistry, and mass spectrometry. J. Proteome Res. 2005, 4, 2070-2080.
[2] Liu, T., Qian, W. J., Gritsenko, M. A., Xiao, W. Z. et al, high dynamic range characterization of the trauma patient plasma proteome. Mol Cell Proteomics 2006,5,1899-1913.
[3] Lee, A., Kolarich, D., Haynes, P. A., Jensen, P. H. et al, Rat liver membrane glycoproteome: enrichment by phase partitioning and glycoprotein capture. J.Proteome Res. 2009, 2, 770-781.
[4] Chen, R., Zou, H. F., Sun, D. G., Han, G. H. et al, glycoproteomics analysis of human liver tissue by combination of multiple enzyme digestion and hydrazide chemistry. J. Proteome Res. 2009, 2, 651-661.
[5] Robinson, N. E., Protein deamidation. Proc. Natl Acad. Sci. U.S.A. 2002, 99,5283-5288.
[6] Li X.J., Cournoyer, J. J., Lin, C, O'Connora, P. B., Use of ~(18)O labels to monitor deamidation during protein and peptide sample processing. J. Am. Soc. Mass Spectrom. 2008,19, 855-864.
[7] Halgglund, P., Matthiesen, R., Elortza, F., H(?)jrup, P. et al, An enzymatic deglycosylation scheme enabling identification of core fucosylated N-glycans and O-glycosylation site mapping of human plasma proteins. J. Proteome Res. 2007,6,3021-3031.
[8] Kaji, H., Saito, H., Yamauchi, Y., Shinkawa, T. et al, Lectin affinity capture,isotope-coded tagging and mass spectrometry to identify N-linked glycoproteins. Nat. Biotechnol 2003, 21, 667-672.
[9] Zhou, Y., Aebersold, R., Zhang, H., Isolation of N-Linked Glycopeptides from Plasma. Anal. Chem. 2007, 79, 5826-5837.
[10] Sun, B. Y, Ranish, J. A., Utleg, A. G, White, J. T. et al, Shotgun glycopeptide-capture approach coupled with mass spectrometry for comprehensive glycoproteomics. Mol Cell Proteomics 2007, 6, 141-149.
[11] Zhang, H., Li, X. J., Martin, D. B., Aebersold, R., Identificatin and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat. Biotechnol. 2003, 21, 660-666.
[2]Xue,H.,Lu,B.J.,Lai,M.D.,The cancer secretome:a reservoir of biomarkers.J.Transl.Med.2008,6,1-12.
[3]Kulasingam,V.,Tissue culture-based breast cancer biomarker discovery platform.Int.J.Cancer 2008,123,2007-2012.
[4]Nickel,W.,Unconventional secretory routes:Direct protein export across the plasma membrane of mammalian cells.Traffic 2005,6,607-614.
[5]Seelenmeyer,C.,Stegrnayer,C.,Nickel,W.,Unconventional secretion of fibroblast growth factor 2 and galectin-1 does not require shedding of plasma membrane-derived vesicles.FEBS Lett.2008,582,1362-1368.
[6] Revest, J. M., DeMoerlooze, L., Dickson, C., Fibroblast growth factor 9 secretion is mediated by a non-cleaved amino-terminal signal sequence. J. Biol. Chem. 2000,275, 8083-8090.
[7] Wolters, D. A., Washburn, M. P., Yates, J. R. 3rd., An automated multidimensional protein identification technology for shotgun proteomics. Anal. Chem. 2001, 73,5683-5690.
[8] Gorg, A., Current two-dimensional electrophoresis technology for proteomics.Proteomics 2004, 4, 3665-3685.
[9] Wang, Y. et al., Nano-flow multidimensional liquid chromatography with electrospray ionization time-of-flight mass spectrometry for proteome analysis of hepatocellular carcinoma. Anal. Chim. Acta 2005, 530, 227-235.
[10] Han, D. K., Eng, J., Zhou, H., Aebersold, R. et al., Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry. Nat. Biotechnol. 2001,19, 946-951.
[11] Julka, S., Quantification in Proteomics through Stable Isotope Coding: A Review.J. Proteome Res. 2004, 3, 350-363.
[12] Wells, W. W., Wang, G. H., Baek, S. J., Shen, R. F., Comparative study of three proteomic quantitative methods, DIGE, cICAT, and iTRAQ, Using 2D Gel- or LC-MALDI TOF/TOF. J. Proteome Res. 2006, 5, 651-658.
[13] Huang, C. M., In vivo secretome sampling technology for proteomics. Proteomics Clin. Appl. 2007,1, 953-962.
[14] Bendtsen, J. D., Nielsen, H., Heijne, G. V., Brunak, S., Improved Prediction of Signal Peptides: SignalP 3.0. J. Mol. Biol. 2004, 340, 783-795.
[15] Hiller, K., Andreas, G., Maurice, S., Richard, M., PrediSi: prediction of signal peptides and their cleavage positions. Nucleic Acids Res. 2004, 32, W375-W379.
[16] Bendtsen, J. D., Jensen, L. J., Blom, N., Heijne, G. V. et al., Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng. Des. Sel. 2004,77,349-356.
[17] Klee, E. W., Daniel F. C., Scott C. F., Stephen C. E. et al., Identifying secretomes in people, pufferfish and pigs. Nucleic Acids Res. 2004, 32,1414-1421.
[18] Chen, Y. J., Zhang, Y., Yin, Y., Gao, G. et al., SPD--a web-based secreted protein database. Nucleic. Acids. Res. 2005, 33, 169-173.
[19] Grimmond, S. M., The Mouse Secretome: Functional Classification of the Proteins Secreted Into the Extracellular Environment. Genome Res. 2003, 13,1350-1359.
[20] Bushell, K. M., Large-scale screening for novel low-affinity extracellular protein interactions. Genome Res. 2008, 75, 622-630.
[21] Diehl, H. C, Stuhler, K., Scory, S. K., Volmer, M. W. et al., A catalogue of proteins released by colorectal cancer cells in vitro as an alternative source for biomarker discovery. Proteomics Clin. Appl. 2007,1, 47-61.
[22] Gr(?)nborg, ML, Kristiansen, T. Z., Iwahori, A., Chang, R. et al., Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach. Mol. Cell Proteomics 2006, 5,157-171.
[23] Dupont, A., Tokarski, C., Dekeyzer, O., Guihot, A. L. et al., Two-dimensional maps and databases of the human macrophage proteome and secretome. Proteomics 2004, 4, 1761-1778.
[24] Shah, P., Atwood Ⅲ, J. A., Orlando, R., Mubarek, H. E. et al., Comparative Proteomic Analysis of Botrytis cinerea Secretome. J Proteome Res. 2009, 5,1123-1130.
[25] Yamashita, R., Fujiwara, Y., Ikari, K., Hamada, K. et al., Extracellular proteome of human hepatoma cell, HepG2 analyzed using two-dimensional liquid chromatography coupled with tandem mass spectrometry. Mol. Cell Biochem.2007, 295, 83-92.
[26] Kulasingam, V., Proteomics Analysis of Conditioned Media from Three Breast Cancer Cell Lines. Mol. Cell Proteomics 2007, 6, 1997-2011.
[27] Huang, M., Ananthaswamy, H. N., Barnes, S., Ma, Y. L. et al., Mass spectrometric proteomics profiles of in vivo tumor secretomes: Capillary ultrafiltration sampling of regressive tumor masses. Proteomics 2006, 6,6107-6116.
[28] Mbeunkui, F., Fodstad, O., Pannell, L. K., Secretory protein enrichment and analysis: An optimized approach applied on cancer cell lines using 2D LC-MS/MS. J Proteome Res. 2006, 5, 899-906.
[29] Chevallet, M., Diemer, H., Dorssealer, A.V., Villiers, C. et al., Toward a better analysis of secreted proteins: the example of the myeloid cells secretome.Proteomics 2007, 7, 1757-1770.
[30] Pellitteri-Hahn, M. C., Warren, M. C., Didier, D. N., Winkler, E. L. et al, J.Proteome. Res. 2006, 5, 2861-2864.
[31] Zwickl, H., Traxler, E., Staettner, S., Parzefall, W. et al., A novel technique to specifically analyze the secretome of cells and tissues. Electrophoresis 2005, 26,2779-2785.
[32] Sanderson, C. M., A new way to explore the world of extracellular protein interactions. Genome Res. 2008,18, 517-520.
[33] Welsh, J. B., Sapinoso, L. M., Kern, S. G., Brown, D. A. et al., Large-scale delineation of secreted protein biomarkers overexpressed in cancer tissue and serum. Proc. Natl Acad. Sci. U.S.A. 2003,100, 3410-3415.
[34] Hagglund, P., Bunkenborg, J., Elortza, F., Jensen, O. N. et al., A new strategy for identification of N-Glycosylated proteins and unambiguous assignment of their glycosylation sites using HILIC enrichment and partial deglycosylation. J.Proteome Res. 2004, 3, 556-566.
[35] Pauline, M. R., Tim, E., Peter, C., Ian, A. W. et al., Glycosylation and the immune system. Science 2001, 291, 2370-2376.
[36] Helenius, A. Markus, A., Intracellular functions of N-Linked glycans. Science 2001,297,2376-2378.
[37] Morelle, W. K(?)vin. C., Fr(?)d(?)ric, C., Valegh, F. et al., The use of mass spectrometry for the proteomic analysis of glycosylation. Proteomics 2006, 6,3993-4015.
[38] Sun, B. Y., Ranish, J. A., Utleg, A. G., White, J. T. et al., Shotgun glycopeptide-capture approach coupled with mass spectrometry for comprehensive glycoproteomics. Mol. Cell Proteomics 2007, 6, 141-149.
[39] Udiger, H. R., Gabius, H. J., Plant lectins: Occurrence, biochemistry, functions and applications. Glycoconjugate Journal 2001, 18, 589-613.
[40] Wang, L. J., Li, F. X., Sun, W., Wu, S. Z. et al., Concanavalin A-captured glycoproteins in healthy human urine. Mol Cell Proteomics 2006, 5, 560-562.
[41] Dai, Z., Fan, J., Liu, Y. K., Zhou, J. et al., Identification and analysis of α1,6-fucosylated proteins in human normal liver tissues by a target glycoproteomic approach. Electrophoresis 2007, 28,4382-4391.
[42] Dai, Z., Liu, Y. K., Cui, J. F., Yang, P. Y. et al., Identification and analysis of altered α1,6-fucosylated glycoproteins associated with hepatocellular carcinoma metastasis. Proteomics 2006, 6, 5857-5867.
[43] Vosseller, K., Trinidad, J. C, Chalkley, R. J., SpechtP, C. G. et al., O-Linked N-Acetylglucosamine proteomics of postsynaptic density preparations using cectin weak affinity chromatography and mass spectrometry. Mol. Cell Proteomics 2006,5, 923-934.
[44] Yang, Z. P., Hancock, W. S., Approach to the comprehensive analysis of glycoproteins isolated from human serum using a multi-lectin affinity column. J ChromatogrA 2004,1053, 79-88.
[45] Zhao, J., Simeone, D. M., Heidt, D., Anderson, M. A. et al., Comparative serum glycoproteomics using lectin selected sialic acid glycoproteins with mass spectrometric analysis: Application to pancreatic cancer serum. J. Proteome Res.2006,5,1792-1802.
[46] Drake, R. R., Schwegler, E. E., Malik, G. J., Diaz, J. et al., Lectin Capture Strategies Combined with Mass Spectrometry for the Discovery of Serum Glycoprotein Biomarkers. Mol Cell Proteomics 2006, 5,1957-1967.
[47] Kaji, H., Saito, H., Yamauchi, Y., Shinkawa, T. et al., Lectin affinity capture,isotope-coded tagging and mass spectrometry to identify N-linked glycoproteins. Nat. Biotechnol 2003, 21, 667-672.
[48] Schwientek, T., Mandel, U., Roth, U., Muller, S. et al., A serial lectin approach to the mucin-type O-glycoproteome of Drosophila melanogaster S2 cells. Proteomics 2007, 7, 3264-3277.
[49] Qiu, R. Q., Regnier, R E., Use of Multidimensional Lectin Affinity Chromatography in Differential Glycoproteomics. Anal. Chem. 2005, 77,2802-2809.
[50] Liu, X. C., Boronie Acids as Ligands for Affinity Chromatography. Chinese J Chromatogr. 2006, 24, 73-80.
[51] Sparbier, K., Wenzel, T., Kostrzewa, M., Exploring the binding profiles of ConA,boronic acid and WGA by MALDI-TOF/TOF MS and magnetic particles. J.Chromatogr. B 2006, 840, 29-36.
[52] Zhang, Q. B., Tang, N., Brock, J. W. C., Mottaz, H. M. et al., Enrichment and Analysis of Nonenzymatically Glycated Peptides: Boronate Affinity Chromatography Coupled with Electron-Transfer Dissociation Mass Spectrometry. J. Proteome Res. 2007, 6, 2323-2330.
[53] Zhang, H., Li, X. J., Martin, D. B., Aebersold, R., Identificatin and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat. Biotechnol. 2003, 21, 660-666.
[54] Liu, T., Qian, W. J., Gritsenko, M. A., Xiao, W. Z. et al., High Dynamic Range Characterization of the Trauma Patient Plasma Proteome. Mol. Cell Proteomics 2006,5,1899-1913.
[55] Liu, T., Qian, W. J., Gritsenko, M. A., CampⅡ, D. G. et al., Human Plasma N-Glycoproteome Analysis by Immunoaffinity Subtraction, Hydrazide Chemistry, and Mass Spectrometry. J. Proteome Res. 2005, 4, 2070-2080.
[56] Lewandrowski, U., Moebius, J., Walter, U., Sickmann, A., Elucidation of N-Glycosylation Sites on Human Platelet Proteins. Mol. Cell Proteomics 2006, 5,226-233.
[57] Ramachandran, P., Boontheung, P., Xie, Y. M., Sondej, M. et al., Identification of N-Linked Glycoproteins in Human Saliva by Glycoprotein Capture and Mass Spectrometry. J. Proteome Res. 2006, 5, 1493-1503.
[58] Zhang, H., Yi, E. C., Li, X. J., Mallick, P. et al., High Throughput Quantitative Analysis of Serum Proteins Using Glycopeptide Capture and Liquid Chromatography Mass Spectrometry. Mol Cell Proteomics 2005, 4, 144-155.
[59] Bernhard, O. K., Kapp, E. A., Simpson, R. J., Enhanced Analysis of the Mouse Plasma Proteome Using Cysteine-Containing Tryptic Glycopeptides. J. Proteome Res. 2007, 6, 987-995.
[60] Zhou, Y., Aebersold, R., Zhang, H., Isolation of N-Linked Glycopeptides from Plasma. Anal. Chem. 2007, 79, 5826-5837.
[61] Tian, Y., Zhou, Y., Elliott, S., Aebersold, R. et al., Solid-phase extraction of N-linked glycopeptides. Nat Protoc 2007, 2, 334-339.
[62] Wada, Y., Tajiri, M., Yoshida, S., Hydrophilic Affinity Isolation and MALDI Multiple-Stage Tandem Mass Spectrometry of Glycopeptides for Glycoproteomics. Anal. Chem. 2004, 76, 6560-6565.
[63] Wuhrer, M., Koeleman, C. A. M., Hokke, C. H., Deelder, A. M., Protein Glycosylation Analyzed by Normal-Phase Nano-Liquid Chromatography-Mass Spectrometry of Glycopeptides. Anal. Chem. 2005, 77, 886-894.
[64] Ding, W., Hill, J. J., Kelly, J., Selective Enrichment of Glycopeptides from Glycoprotein Digests Using Ion-Pairing Normal-Phase Liquid Chromatography. Anal. Chem. 2007, 79, 8891-8899.
[65] Zhang, Y., Go, E. P., Desaire, H., Maximizing Coverage of Glycosylation Heterogeneity in MALDI-MS Analysis of Glycoproteins with Up to 27 Glycosylation Sites. Anal. Chem. 2008, 80, 3144-3158.
[66] Hagglund, P., Bunkenborg, J. E. F., Jensen, O. N., Roepstorff, P., A New Strategy for Identification of N-Glycosylated Proteins and Unambiguous Assignment of Their Glycosylation Sites Using HILIC Enrichment and Partial Deglycosylation. J. Proteome Res. 2004, 3, 556-566.
[67] Tajiri, M., Yoshida, S., Wada, Y., Differential analysis of site-specific glycans on plasma and cellular fibronectins: application of a hydrophilic affinity method for glycopeptide enrichment. Glycobiology 2005,15,1332-1340.
[68] Gerardo, A. M., Guo, Y., Warren, N. L., Orlando, R. et al., Tools for Glycoproteomic Analysis: Size Exclusion Chromatography Facilitates Identification of Tryptic Glycopeptides with N-linked Glycosylation Sites. J.Proteome Res. 2006, 5, 701-708.
[69] Ghesquie're, B., Buyl, L., Demol, H., Damme, J. V. et al., A New Approach for Mapping Sialylated N-Glycosites in Serum Proteomes. J. Proteome Res. 2007, 6,4304-4312.
[70] Lewandrowski, U., Zahedi, R. P., Moebius, J., WalterP, U. et al., Enhanced N-Glycosylation Site Analysis of Sialoglycopeptides by Strong Cation Exchange Prefractionation Applied to Platelet Plasma Membranes. Mol. Cell Proteomics2007,6,1399-1941.
[71] Wells, L., Vosseller, K., Cole, R. N., Matunis, M. J. et al., Mapping Sites of O-GlcNAc Modification Using Affinity Tags for Serine and Threonine Post-translational Modifications. Mol Cell Proteomics 2002,1, 791-804.
[72] Comer, F. I., Vosseller, K., Wells, L., Accavitti, M. A. et al., Characterization of a Mouse Monoclonal Antibody Specific for O-Linked iV-Acetylglucosamine. Anal.Biochem. 2001, 293, 169-177.
[73] Boeggeman, E., Ramakrishnan, B., Kilgore, C., Khidekel, N. et al., Direct Identification of Nonreducing GlcNAc Residues on N-Glycans of Glycoproteins Using a Novel Chemoenzymatic Method. Bioconjugate Chem. 2007, 18,806-814.
[74] Ball, L. E., Berkaw, M. N., Buse, M. G., Identification of the Major Site of O-Linked-N-Acetylglucosamine Modification in the C Terminus of Insulin Receptor Substrate-1. Mol Cell Proteomics 2006, 5, 313-323.
[75] Pan, S., Wang, Y., Quinn, J. F., Peskind, E. R. et al., Identification of Glycoproteins in Human Cerebrospinal Fluid with a Complementary Proteomic Approach. J. Proteome Res. 2006, 5, 2769-2779.
[76] Lee, A., Kolarich, D., Haynes, P. A., Jensen, P. H. et al., Rat Liver Membrane Glycoproteome: Enrichment by Phase Partitioning and Glycoprotein Capture. J.Proteome Res. 2009, 770-781.
[77] Monzo, A., Bonn, G. K., Guttman, A., Boronic acid-lectin affinity chromatography. 1. Simultaneous glycoprotein binding with selective or combined elution. Anal Bioanal Chem. 2007, 389, 2097-2102.
[78] Cao, J., Shen C. P., Wang, H., Yang, P.Y. et al., Identification of N-Glycosylation Sites on Secreted Proteins of Human Hepatocellular Carcinoma Cells with a Complementary Proteomics Approach. J. Proteome Res. 2009, 8, 662-672.
[79] Kubota, K., Sato, Y., Suzuki, Y., Naoko, G. I. et al., Analysis of Glycopeptides Using Lectin Affinity Chromatography with MALDI-TOF Mass Spectrometry.Anal Chem. 2008, 80, 3693-3698.
[80] Chen, R., Zou, H. F., Sun, D. G., Han, G. H. et al., Glycoproteomics Analysis of Human Liver Tissue by Combination of Multiple Enzyme Digestion and Hydrazide Chemistry. J. Proteome Res. 2009, 651-661.
[81] Andre, M., Morelle, W., Planchon, S., Milhiet, P. E. et al., Glycosylation status of the membrane protein CD9P-1. Proteomics 2007, 7, 3880-3895.
[82] Bleckmann, C., Glycomic Analysis of N-Linked Carbohydrate Epitopes from CD24 of Mouse Brain. J. Proteome Res. 2009, 8, 567-582.
[83] Montesino, R., N-Glycosylation Pattern of E2 Glycoprotein from Classical Swine Fever Virus. J. Proteome Res. 2009, 8, 546-555.
[84] Patwa, T. H., Screening of Glycosylation Patterns in Serum Using Natural Glycoprotein Microarrays and Multi-Lectin Fluorescence Detection.Anal Chem.2006,78,6411-6421.
[85]Zhao,J.,Glycoprotein Microarrays with Multi-Lectin Detection:Unique Lectin Binding Patterns as a Tool for Classifying Normal,Chronic Pancreatitis and Pancreatic Cancer Sera.J.Proteome Res.2007,6,1864-1874.
[86]Li,C.,Pancreatic Cancer Serum Detection Using a Lectin/Glyco-Antibody Array Method.J.Proteome Res.2009,8,483-492.
[87]Tang,Z.Y.,Ye,S.L.,Liu,Y.K.et al.,A decade's studies on metastasis of hepatocellular carcinoma.J.Cancer Res.Clin.Oncol.2004,130,187-196.
[88]汤钊猷.概述.见:汤钊猷,主编.肿瘤转移复发的基础与临床.第一版.上海:上海科技出版社,2003,1-24.
[89]汤钊猷.肝癌转移复发的早期发现与早期诊断.见汤钊猷,主编.肿瘤转移复发的基础与临床.第一版.上海:上海科技出版社,2003,289-293.
[90]Li,C.,Hong,Y.,Tan,Y.X.,Zhou,H.et al.,Accurate qualitative and quantitative proteomic analysis of clinical hepatocellular carcinoma using laser capture microdissection coupled with isotope-coded affinity tag and two-dimensional liquid chromatography mass spectrometry.Mol.Cell Proteomics 2004,3,399-409.
[91]Sun,B.S.,Dong,Q.Z.,Ye,Q.H.,Sun,H.J.et al.,Lentiviral-mediated miRNA against osteopontin suppresses tumor growth and metastasis of human hepatocellular carcinoma.Hepatology 2008,48,1834-1842.
[92]Srisomsap,C.,Sawangareetrakul,P,Subhasitanont,P,Panichakul,T.et al.,Proteomic analysis of cholangiocarcinoma cell line.Proteomics 2004,4,1135-1144.
[93]Fujii,K.,Kondo,T.,Yokoo,H.,Yamada,T.et al.,Proteomic study of human hepatocellular carcinoma using two-dimensional difference gel electrophoresis with saturation cysteine dye.Proteomics 2005,5,1411-1422.
[94]Zhai,Y.,Zhou,G,Deng,G,Xie,W.et al.,Estrogen Receptor Alpha Polymorphisms Associated with Susceptibility to Hepatocellular Carcinoma in Hepatitis B Virus Carriers.Gastroenterology 2006,130,2001-2009.
[95]Yokoo,H.,Kondo,T.,Fujii,K.,Yamada,T.et al.,Proteomic signature corresponding to alpha fetoprotein expression in liver cancer cells.Hepatology 2004,40,609-617.
[96] Song, P. M, Zhang, Y., He, Y. F., Bao, H. M. et al., Bioinformatics analysis of metastasis-related proteins in hepatocellular carcinoma. World J. Gastroenterol.2008,74,5816-5822.
[97] Ding, S. J., Li, Y., Shao, X. X. et al., Proteome analysis of hepatocellular carcinoma cell strains, MHCC97-H and MHCC97-L, with different metastasis potentials. Proteomics 2004, 4, 982-994.
[98] Ding, S. J., Li, Y., Tan, Y. X., Jiang, M. R. et al., From proteomic analysis to clinical significance: overexpression of cytokeratin 19 correlates with hepatocellular carcinoma metastasis. Mol. Cell Proteomics 2004, 3, 73-81.
[99] Cui, J. F., Liu, Y. K., Pan, B. S., Song, H. Y. et al., Differential proteomic analysis of human hepatocellular carcinoma cell line metastasis-associated proteins. J.Cancer Res. Clin. Oncol. 2004,130, 615-622.
[100] Li, C., Tan, Y. X., Zhou, H., Ding, S. J. et al., Proteomic analysis of hepatitis B virus-associated hepatocellular carcinoma: Identification of potential tumor markers. Proteomics 2005, 5, 1125-1139.
[101] Yang, G. H., Fan, J., Xu, Y., Qiu, S. J. et al., Osteopontin Combined with CD44,a Novel Prognostic Biomarker for Patients with Hepatocellular Carcinoma Undergoing Curative Resection. Oncologist 2008,13, 1155-1165.
[102] Yang, X. R., Xu, Y., Shi, G. M., Fan, J. et al., Cytokeratin 10 and cytokeratin 19: Predictive markers for poor prognosis in hepatocellular carcinoma patients after curative resection. Clin. Cancer Res. 2008,14, 3850-3859.
[103] Zeindl-Eberhart, E., Haraida, S., Liebmann, S., Jungblut, P. R. et al, Detection and identification of tumor-associated protein variants in human hepatocellular carcinomas. Hepatology 2004, 39, 540-549.
[104] Muramatsu, T., Muramatsu, H., Glycosaminoglycan-binding cytokines as tumor markers. Proteomics 2008, 8, 3350-3359.
[105] Zhu, X. D., Zhang, J. B., Zhuang, P. Y., Zhu, H. G. et al., High expression of macrophage colony-stimulating factor in peritumoral liver tissue is associated with poor survival after curative resection of hepatocellular carcinoma. J. Clin. Oncol. 2008, 26, 2707-2716.
[106] Lau, S. H., Sham, J. S. T., Xie, D., Tzang, C. H. et al., Clusterin plays an important role in hepatocellular carcinoma metastasis. Oncogene 2006, 25,1242-1250.
[107] Yang, Z. F., Ho, D. W., Ng, M. N, Lau, C. K. et al, Significance of CD90(+) cancer stem cells in human liver cancer. Cancer Cell 2008,13,153-166.
[108] Yang, Z. F., Ho, D. W., Lam, C. T., Luk, J. M. et al, Identification of brain-derived neurotrophic factor as a novel functional protein in hepatocellular carcinoma. Cancer Res. 2005, 65, 219-225.
[109] Lane, C. S., Nisar, S., Griffiths, W. J., Fuller, B. J. et al, Identification of cytochrome P450 enzymes in human colorectal metastases and the surrounding liver: a proteomic approach. Eur. J. Cancer 2004, 40, 2127-2134.
[110] Melle, C, Kaufinann, R., Hommann, M., Bleul, A. et al, Proteomic profiling in microdissected hepatocellular carcinoma tissue using ProteinChip technology. Int. J. Oncol. 2004, 24, 885-891.
[111] Kim, W., Lim, S.O., Kim, J. S., Ryu, Y H. et al, Comparison of proteome between hepatitis B virus- and hepatitis C virus-associated hepatocellular carcinoma. Clin. Cancer Res. 2003, 9, 5493-5500.
[112] Yokoyama, Y., Kuramitsu, Y., Takashima, M. et al, Proteomic profiling of proteins decreased in hepatocellular carcinoma from patients infected with hepatitis C virus. Proteomics 2004, 4, 2111-2116.
[113] Fan, H. Z., Liu, H., Zhang, C, Liu, Y. K. et al, Comparative proteomics and molecular mechanical analysis in CDA-Ⅱ induced therapy of LCI-D20 hepatocellular carcinoma model. J. Cancer Res. Clin. Oncol. 2009, 135,591-602.
[114] Steel, L. F., Shumpert, D., Trotter, M., Seeholzer, S. H. et al., A strategy for the comparative analysis of serum proteomes for the discovery of biomarkers for hepatocellular carcinoma. Proteomics 2003, 3, 601-609.
[115] Qiu, J. G., Fan, J., Liu, Y. K., Gao, D. M. et al., Screening and detection of portal vein tumor thrombi-associated serum low molecular weight protein biomarkers in human hepatocellular carcinoma. J. Cancer Res. Clin. Oncol. 2008,134, 299-305.
[116] Li, Y., Tang, Z. Y., Tian, B., Ye, S. L. et al Serum CYFRA21-1 level reflects hepatocellular carcinoma metastasis: study in nude mice model and clinical patients. J. Cancer Res. Clin. Oncol. 2006,132, 515-520.
[117] Paradis, V., Degos, F., Dargere, D., Pham, N. et al., Identification of a new marker of hepatocellular carcinoma by serum protein profiling of patients with chronic liver diseases. Hepatology 2005, 41, 40-47.
[118] Schwegler, E. E., Cazares, L., Steel, L. R, Adam B. L. et al, SELDI-TOF MS profiling of serum for detection of the progression of chronic hepatitis C to hepatocellular carcinoma. Hepatology 2005, 41, 634-642.
[119] Block, T. M, Use of targeted glycoproteomics to identify serum glycoproteins that correlate with liver cancer in woodchucks and humans. Proc. Natl. Acad.Sci. U.S.A. 2005,102, 779-784.
[120] Zheng, J. J., He, R C., Report of the 9th HLPP Workshop. Proteomics 2008, 8,3420-3423.
[1]Gaorav,P.G.,Massague,J.,Cancer Metastasis:Building a Framework.Cell 2006,127,679-695.
[2]Sun,F.X.,Tang,Z.Y.,Liu,K.D.,Ye,S.L.et al.,Establishment of a metastatic model of human hepatocellular carcinoma in nude mice via orthotopic implantation of histologically intact tissues.Int.J.Cancer 1996,66,239-243.
[3]Tian,J.,Tang,Z.Y.,Ye,S.L.,Liu,Y.K.et al.,New human hepatocellular carcinoma(HCC) cell line with highly metastatic potential(MHCC97) and its expressions of the factors associated with metastasis.Br.J.Cancer 1999,81,814-821.
[4]Tang,Z.Y.,Zhou,X.D.,Lin,Z.Y.,Yang,B.H.et al.,Surgical treatment of hepatocellular carcinoma and related basic research with special reference to recurrence and metastasis.Chinese Med.J.1999,112,887-891.
[5]Li,Y.,Tang,Z.Y.,Ye,S.L.,Liu,Y.K.et al.,Establishment of cell clones with different metastatic potential from the metastatic hepatocellular carcinoma cell line MHCC97.World J.Gastroenterol 2001,7,630-636.
[6]Qin,L.X.,Tang,Z.Y.,Ye,S.L.,Liu,Y.K.et al.,Chromosome 8p deletion is associated with metastasis of human hepatocellular carcinoma when high and low metastatic models are compared.J.Cancer Res.Clin.Oncol.2001,127,482-488.
[7]李雁,汤钊猷,叶胜龙,刘银坤等,体内连续筛选法建立自发性肺转移人肝癌细胞系.中华医学杂志2002,82,601-605.
[8]Kazuyasu,F.J.,Tadashi,K.,Hideki,Y.,Tesshi,Y.et al.,Proteomic study of human hepatocellular carcinoma using two-dimensional difference gel electrophoresis with saturation cysteine dye.Proteomics 2005,5,1411-1422.
[9]Song,P.M.,Zhang,Y.,He,Y.F.,Bao,H.M.et al.,Bioinformatics analysis of metastasis-related proteins in hepatocellular carcinoma.World J.Gastroenterol.2008,14,5816-5822.
[10]Yuan,Q.,An,J.,Liu,D.G.,Sun,L.et al.,Proteomic analysis of differential protein expression in a human hepatoma revertant cell line by using an improved two-dimensional electrophoresis procedure combined with matrix assisted laser desorption/ionization-time of flight-mass spectrometry.Electrophoresis 2004,25,1160-1168.
[11]Ding,S.J.,Li,Y.,Shao,X.X.Zhou;H.et al.,Proteome analysis of hepatocellular carcinoma cell strains,MHCC97-H and MHCC97-L,with different metastasis potentials.Proteomics 2004,4,982-994.
[12]Shen,H.L.,Cheng,G.,Fan H.Z.,Liu,Y.K.et al.,Expressed proteome analysis of human hepatocellular carcinoma in nude mice(LCI-D20) with high metastasis potential.Proteomics 2006,6,528-537.
[13]钦伦秀,主编,肝癌的分子诊断与预测.第一版.上海:上海科技教育出版社,2004.163-172.
[14]McClelland,C.M.,Gullick,W.J.,Proteomic identification of secreted proteins as surrogate markers for signal transduction inhibitor activity.Br.J.Cancer 2007,96,284-289.
[15] Zvonic, S., Lefevre, M., Kilroy, G, Floyd, Z. E. et al, Secretome of primary cultures of human adipose-derived stem cells-Modulation of serpins by adipogenesis. Mol. Cell Proteomics 2007, 6,18-28.
[16] Pardo, M., Garcia, A., Antrobus, R., Blanco, M. J. et al, Biomarker discovery from uveal melanoma secretomes: Identification of gp100 and cathepsin D in patient serum. J. Proteome Res. 2007, 6, 2802-2811.
[17] Zwickl, H., Traxler, E., Staettner, S., Parzefall, W. et al., A novel technique to specifically analyze the secretome of cells and tissues. Electrophoresis 2005, 26,2779-2785.
[18] Trost, M., Wehmhoner, D. D., K(?)rst, U., Dieterich, G. et al, Comparative proteome analysis of secretory proteins from pathogenic and nonpathogenic Listeria species. Proteomics 2005, 5,1544-1557.
[19] Thouvenot, E., Lafon-Cazal, M., Demettre, E., Jouin, P. et al, The proteomic analysis of mouse choroid plexus secretome reveals a high protein secretion capacity of choroidal epithelial cells. Proteomics 2006, 6, 5941-5952.
[20] Kulasingam, V., Diamandis, E. P., Proteomics analysis of conditioned media from three breast cancer cell lines. Mol. Cell Proteomics 2007, 6, 1997-2011.
[21] Sardana, G., Marshall, J., Diamandis, E.P., Discovery of candidate tumor markers for prostate cancer via proteomic analysis of cell culture-conditioned medium. Clin. Chem. 2007, 53, 429-437.
[22] Wu, C. C., Chien, K. Y., Tsang, N. M., Chang, K. P. et al, Cancer cell-secreted proteomes as a basis for searching potential tumor markers: Nasopharyngeal carcinoma as a model. Proteomics 2005, 5, 3173-3182.
[23] Chevallet, M., Diemer, H., Dorssealer, A.V., Villiers, C. et al, Toward a better analysis of secreted proteins: the example of the myeloid cells secretome. Proteomics 2007, 7,1757-1770.
[24] Bendtsen, J. D., Jensen, L. J., Blom, N., Heijne, G. V. et al, Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng. Des. Sel.2004,77,349-356.
[25] Krogh, A., Larsson, B., Heijne, G., Sonnhammer, E. L. L. et al, Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 2001, 305, 567-580.
[26] Ashburner M., Ball C. A., Blake J. A., Botstein, D. et al, Gene Ontology: tool for the unification of biology. Nat. Genet. 2000, 25, 25-29.
[27] Sun, B. S., Dong, Q. Z., Ye, Q. H., Sun, H. J. et al, Lentiviral-mediated miRNA against osteopontin suppresses tumor growth and metastasis of human hepatocellular carcinoma. Hepatology 2008, 48, 1834-42.
[28] Ye, Q. H., Qin, L. X., Forgues, M., He, P. et al, Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning. Nat. Med. 2003, 9, 416-423.
[29] Lau, S. H., Sham, J. S. T., Xie, D., Tzang, C. H. et al, Clusterin plays an important role in hepatocellular carcinoma metastasis. Oncogene 2006, 25,1242-1250.
[30] http://www.iscb.org/cms_addon/conferences/ismb2005/posters_list.html, Poster C-95
[31] Ding, S. J., Li, Y, Tan, Y. X., Jiang, M. R. et al, From proteomic analysis to clinical significance: overexpression of cytokeratin 19 correlates with hepatocellular carcinoma metastasis. Mol. Cell Proteomics 2004, 3, 73-81.
[32] Cui, J. F., Liu, Y K., Pan, B. S., Song, H. Y. et al, Differential proteomic analysis of human hepatocellular carcinoma cell line metastasis-associated proteins. J. Cancer Res. Clin. Oncol. 2004, 130, 615-622.
[33] Gr(?)nborg, M., Kristiansen, T. Z., Iwahori, A., Chang, R. et al, Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach. Mol. Cell Proteomics 2006, 5, 157-171.
[1]Mbeunkui,F.,Fodstad,O.,Pannell,L.K.,Secretory protein enrichment and analysis:An optimized approach applied on cancer cell lines using 2D LC-MS/MS.J.Proteome Res.2006,5,899-906.
[2]http://www.iscb.org/cms_addon/conferences/ismb2005/posters_list.html,Poster C-95
[3]Chen,Y.J.,Zhang,Y.,Yin,Y.,Gao,G.et al.,SPD--a web-based secreted protein database.Nucleic.Acids.Res.2005,33,169-173.
[4]Zhang,Y.H.,Liu,Y.,Kong,J.L.,Yang,P.Y.et al.,Efficient Proteolysis System:A Nanozeolite-Derived Microreactor.small 2006,2,1170-1173.
[5]Dahm,(?).,Eriksson,H.,Ultra-stable zeolites - a tool for in-cell chemistry.J.Biotechnol 2004,111,279-290.
[6]Andersson,L.I.M.,Eriksson,H.,De-aluminated Zeolite Y as a Tool to Study Endocytosis,A delivery system revealing differences between human peripheral dendritic cells.Stand.J.Immunol.2007,66,52-61.
[7]Zhang,Y.H.,Yu,X.J.,Wang,X.Y.,Shan,W.et al.,Zeolite nanoparticles with immobilized metal ions:isolation and MALDI-TOF-MS/MS identification of phosphopeptides.Chem.Commun.2004,2882-2883.
[8]Zhang,Y.H.,Wang,X.Y.,Shan,W.,Wu,B.Y.et al.,Enrichment of low-abundance peptides and proteins on zeolite nanocrystals for direct MALDI-TOF MS analysis.Angew.Chem.Int.Ed.2005,44,615-617.
[9]Hu,Y.Y.,Liu,C.,Zhang,Y.H.,Ren,N.et al.,Microwave assisted hydrothermal synthesis of nanozeolites with controllable size. Microporous Mesoporous Mater.2009,779,306-314.
[10] Sch(?)gger, H., Jagow, G. V., Tricine-sodium dodecyl sulfate-polyacrylamid gel electroporesis for the separation of proteins in the range from 1 to 100 kDa. Anal.Biochem. 1987,766,368-379.
[11] Sch(?)gger, H. et al, Tricine-SDS-PAGE. Nat Protoc. 2006, 7, 16-22.
[12] Bendtsen J. D., Jensen L. J., Blom N., Heijne G. V. et al, Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng. Des. Sel.2004,77,349-356.
[13] Krogh A., Larsson B., Heijne G., Sonnhammer E. L. L. et al., Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 2001, 305, 567-580.
[14] Volmer, M. W., St(?)hler, K., Zapatka, M., Schoneck, A. et al., Differential proteome analysis of conditioned media to detect Smad4 regulated secreted biomarkers in colon cancer. Proteomics 2005, 5, 2587-2601.
[15] Lou, X. M., Xiao, T., Zhao, K., Wang, H. et al., Cathepsin D is secreted from M-BE cells: Its potential role as a biomarker of lung cancer. J. Proteome Res. 2007, 6, 1083-1092.
[16] Dupont, A., Tokarski, C, Dekeyzer, O., Guihot, A. L. et al., Two-dimensional maps and databases of the human macrophage proteome and secretome.Proteomics 2004, 4, 1761-1778.
[17] Wu, C. C, Cheng, H. C, Chen, S. J., Liu, H. P. et al., Identification of collapsin response mediator protein-2 as a potential marker of colorectal carcinoma by comparative analysis of cancer cell secretomes. Proteomics 2008, 8, 316-332.
[18] Feng, J. T., Liu, Y. K., Song, H. Y., Dai, Z. et al., Heat-shock protein 27: A potential biomarker for hepatocellular carcinoma identified by serum proteome analysis. Proteomics 2005, 5, 4581-4588.
[19] Sun, W., Xing, B. C, Sun, Y., Du, X. J. et al., Proteome Analysis of Hepatocellular Carcinoma by Two-dimensional Difference Gel Electrophoresis: Novel Protein Markers in Hepatocellular Carcinoma Tissues. Mol. Cell Proteomics 2007, 6, 1798-1808.
[20] Yokoyama, Y., Kuramitsu, Y., Takashima, M., Iizuka, N. et al., Protein level of apolipoprotein E increased in human hepatocellular carcinoma. Int. J. Oncol.2006, 28, 625-631.
[21]Ye,Q.H.,Qin,L.X.,Forgues,M.,He,P.et al.,Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning.Nat.Med.2003,9,416-423.
[22]Donato,R.,S100:a multigenic family of calcium-modulated proteins of the EF-hand type with intracellular and extracellular functional roles.Int.J.Biochem.Cell Biol.2001,33,637-668.
[23]姬峻芳,刘芝华,赵立群,S100蛋白家族与肿瘤,国外医学肿瘤学分册2002,29,261-263
[24]Komatsu,K.,Kobune-Fujiwara,Y.,Andoh,A.,Ishiguro,S.et al.,Increased expression of S100A6 at the invading fronts of the primary lesion and liver metastasis in patients with colorectal adenocarcinoma.Br.J.Cancer 2000,83,769-774.
[25]Semov,A.,Moreno,M.J.,Onichtchenko,A.,Abulrob,A.et al.,Metastasis-associated protein S100A4 induces angiogenesis through interaction with Annexin Ⅱ and accelerated plasmin formation.J.Biol.Chem.2005,280,20833-20841.
[26]Hermani,A.,Hess,J.,DeServi,B.,Medunjanin,S.et al.,Calcium-binding proteins S100A8 and S100A9 as novel diagnostic markers in human prostate cancer.Clin.Cancer Res.2005,11,5146-5152.
[27]Hemandas,A.K.,Salto-Tellez,M.,Maricar,S.H.,Leong,A.et al.,Metastasis-Associated Protein S100A4 - A Potential Prognostic Marker for Colorectal Cancer.J.Surg.Oncol.2006,93,498-503.
[28]Cui,J.F.,Liu,Y.K.,Zhang,L.J.,Shen,H.L.et al.,Identification of metastasis candidate proteins among HCC cell lines by comparative proteome and biological function analysis of S100A4 in metastasis in vitro.Proteomics 2006,6,5953-5961.
[29]Oida,Y.,Yamazaki,H.,Tobita,K.,Mukai,M.et al.,Increased S100A4expression combined with decreased E-cadherin expression predicts a poor outcome of patients with pancreatic cancer.Oncol.Rep.2006,16,457-463.
[30]Yamashita,R.,Fujiwara,Y.,Ikari,K.,Hamada,K.et al.,Extracellular proteome of human hepatoma cell,HepG2 analyzed using two-dimensional liquid chromatography coupled with tandem mass spectrometry.Mol.Cell Biochem.2007,298,83-92.
[31]Zwickl,H.,Traxler,E.,Staettner,S.,Parzefall,W.et al.,A novel technique to specifically analyze the secretome of cells and tissues.Electrophoresis 2005,26,2779-2785.
[32]Ji,X.N.,Ye,S.L.,Li,Y.,Tian,B.et al.,Contributions of lung tissue extracts to invasion and migration of human hepatocellular carcinoma cells with various metastatic potentials.J.Cancer Res.Clin.Oncol.2003,129,556-564.
[33]Tang,Z.Y.,Ye,S.L.,Liu,Y.K.,Qin,L.X.et al.,A decade's studies on metastasis of hepatocellular carcinoma.J.Cancer Res.Clin.Oncol.2004,130,187-196.
[1]Lewandrowski,U.,Moebius,J.,Walter,U.,Sickmann,A.et al.,Elucidation of N-Glycosylation Sites on Human Platelet Proteins.Mol.Cell Proteomics 2006,5,226-233.
[2]Pan,S.,Wang,Y.,Quinn,J.F.,Peskind,E.R.et al.,Identification of Glycoproteins in Human Cerebrospinal Fluid with a Complementary Proteomic Approach.J.Proteome.Res.2006,5,2769-2779.
[3]Danielle,H.D.Bertozzi,C.R.,Glycans in Cancer and Inflammation Potential for Therapeutics and Diagnostics.Nat.Rev.Drug Discov.2005,4,477-488.
[4]Roth,J.,Protein N-glycosylation along the secretory pathway:relationship to organelle topography and function,protein quality control,and cell interactions.Chem.Rev.2002,102,285-303.
[5]Tretter,V.,Altmann,F.,Marz,L.et al.,Peptide-N4-(N-acetyl-_-glucosaminyl)asparagine amidase F cannot release glycans with fucose attached _133 to the asparagine-linked N-acetylglucosamine residue.Eur.J.Biochem.1991,199,647-652
[6]Liu,X.,Ma,L.,Li,J.J.,Recent Developments in the Enrichment of Glycopeptides for Glycoproteomics.Anal.Lett.2008,41,268-277.
[7]Sun,B.Y.,Ranish,J.A.,Utleg,A.G.,White,J.T.,Shotgun Glycopeptide-Capture Approach Coupled with Mass Spectrometry for Comprehensive Glycoproteomics.Mol.Cell Proteomics 2007,6,141 - 149.
[8]Zhou,Y.,Aebersold,R.,Zhang,H.,Isolation of N-Linked Glycopeptides from Plasma.Anal.Chem.2007,79,5826-5837.
[9]Wada,Y.,Tajiri,M.,Yoshida,S.et al.,Hydrophilic Affinity Isolation and MALDI Multiple-Stage Tandem Mass Spectrometry of Glycopeptides for Glycoproteomics. Anal. Chem. 2004, 76, 6560-6565.
[10 Tajiri, M., Yoshida, S., Wada, Y. et al, Differential analysis of site-specific glycans on plasma and cellular fibronectins: application of a hydrophilic affinity method for glycopeptide enrichment. Glycobiology 2005, 75, 1332-1340.
[11] Zhang, Q. B., Tang, N, Brock, J. W. C., Mottaz, H. M. et al, Enrichment and Analysis of Nonenzymatically Glycated Peptides: Boronate Affinity Chromatography Coupled with Electron-Transfer Dissociation Mass Spectrometry. J. Proteome Res. 2007, 6, 2323-2330.
[12] Kreunin, P., Zhao, J., Rosser, C., Urquidi, V. et al, Bladder Cancer Associated Glycoprotein Signatures Revealed by Urinary Proteomic Profiling. J. Proteome Res. 2007, 6, 2631-2639.
[13] Liu, T., Qian, W. J., Gritsenko, M. A., Xiao, W. Z. et al, High Dynamic Range Characterization of the Trauma Patient Plasma Proteome. Mol. Cell Proteomics 2006,5,1899-1913.
[14] Chen, M., Ying, W. T., Song, Y. P., Liu, X. et al, Analysis of human liver proteome using replicate shotgun strategy. Proteomics 2007, 7, 2479-2488.
[15] Atwood Ⅲ, J. A., Sahoo, S. S., Alvarez-Manilla, G., Alvarez-Manilla, G. et al,Simple modification of a protein database for mass spectral identification of N-linked glycopeptides. Rapid Commun. Mass Spectro. 2005,19, 3002-3006.
[16] Pellitteri-Hahn, M. C., Warren, M. C., Didier, D. N., Winkler, E. L. et al,Improved Mass Spectrometric Proteomic Profiling of the Secretome of Rat Vascular Endothelial Cells. J. Proteome. Res. 2006, 5, 2861-2864.
[17] Chevallet, M., Diemer, H., Dorssealer, A.V., Villiers, C. et al, Toward a better analysis of secreted proteins: the example of the myeloid cells secretome.Proteomics 2007', 7, 1757-1770.
[18] Krueger, K. E., Srivastava, S., Posttranslational Protein Modifications: Current Implications for Cancer Detection, Prevention, and Therapeutics. Mol Cell Proteomics 2006, 5, 1799-1810.
[19] Gonzalez-Moles, M. A., Esteban, F., Bravo-Perez, J. J., Bravo-Perez, M. et al,Adhesion molecule CD44 expression in non-tumor epithelium adjacent to laryngeal cancer. Oncology 2006, 29, 9-13.
[20]Qin,L.X.,Tang,Z.Y.et al.,World J.Gastroenterol 2002,8,385-392.
[21]Qin,L.X.,Tang,Z.Y.Liu,Y.K.,Qin,L.X.et al.,A decade's studies on metastasis of hepatocellular carcinoma.J.Cancer Res.Clin.Oncol.2004,130,497-513.
[22]Fedorowski,A.,Steciwko,A.,Rabczynski,J.et al.,Med.Sci.Monit.2004,10,BR144-BR150.
[23]Liu,X.C.,Boronic Acids as Ligands for Affinity Chromatography.Chinese J Chromatogr 2006,24,73-80.
[24]王山,李钰,蛋白质的糖组学研究进展.细胞生物学杂志2006,28,127-131.
[25]International Human Genome Sequencing Consortium.Nature 2004,431,915-916.
[26]Varki,A.,Cummings,R.,Esko,J.,Freeze,H.et al.,Essentials of Glycobiology.Cold Spring Harbor Laboratory Press,Cold Spring Harbor,New York 1999.
[27]Candiano,G.,Bruschi,M.,Musante,L.,Santucci,L.et al.,Blue silver:A very sensitive colloidal Coomassie G-250 staining for proteome analysis.Electrophoresis 2004,25,1327-1333.
[28]de Albuquerque Garcia Redondo,P.,Nakamura,C.V.,de Souza,W.,Morgado-Diaz,J.,A.,Differential expression of sialic acid and N-acetylgalactosamine residues on the cell surface of intestinal epithelial cells according to normal or metastatic potential.J.Histochem.Cytochem.2004,52,629-640.
[29]Pilobello,K.T.,Krishnamoorthy,L.,Slawek,D.,Mahal,L.K.et al.,Development of a lectin microarray for the rapid analysis of protein glycopatterns mahal.Chem.Bio.Chem.2005,6,985-989.
[30]Kuno,A.,Uchiyama,N.,Koseki-Kuno,S.,Ebe,Y.,Evanescent-field fluorescence-assisted lectin microarray:a new strategy for glycan profiling.Nature Methods 2005,2,851-856.
[31]Angeloni,S.,Ridet,J.L.,Kusy,N.,Gao,H.et al.,Glycoprofiling with micro-arrays of glycoconjugates and lectins.Glycobiology 2005,15,31-41.
[1]Kremer,A.,Schneider,R.,Terstappen,G.C.,A bioinformatics perspective on proteomics:data storage,analysis,and integration.Biosci Rep.2005,25,95-106.
[2]Englbrecht,C.C.,Facius,A.,Bioinformatics challenges in proteomics.Comb Chem High Throughput Screen 2005,8,705-715.
[3]Thorgeirsson,S.S.,Lee,J.S.,Grisham,J.W.et al.,Functional genomics of hepatocellular carcinoma. Hepatology 2006, 43, S145-150.
[4] Chagoyen, M., Carmona-Saez, P., Shatkay, H. et al, Discovering semantic features in the literature: a foundation for building functional associations. BMC Bioinformatics 2006, 7, 41.
[5] Armano, G., Mancosu, G., Milanesi, L. et al, A hybrid genetic-neural system for predicting protein secondary structure. BMC Bioinformatics 2005, 6 Suppl 4, S3.
[6] Fung, E. T., Weinberger, S. R., Gavin, E. et al, Bioinformatics approaches in clinical proteomics. Expert Rev. Proteomics 2005, 2, 847-862.
[7] Cai, Y. D., Chou, K. C, Predicting enzyme subclass by functional domain composition and pseudo amino acid composition. J Proteome Res. 2005, 4,967-971.
[8] Uren, A. G., Kool, I, Matentzoglu, K., de Ridder, J. et al, Large-Scale Mutagenesis in pl9(ARF)- and p53-Deficient Mice Identifies Cancer Genes and Their Collaborative Networks. Cell 2008,133, 727-741.
[9] Ray, S., Britschgi, M., Herbert, C, Takeda-Uchimura Y. et ai, Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins.Nat. Med. 2007,13, 1359-1362.
[10] Cloonan, N., Forrest, A. R., Kolle, G., Gardiner, B. B. et al, Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat. Methods 2008,5,613-619.
[11] Liu, X. Y. et al, Comparative proteomics and correlated signaling network of rat hippocampus in the pilocarpine model of temporal lobe epilepsy. Proteomics 2008,8, 582-603.
[12] Liu, H., Sadygov, R. G., Yates, J. R. et al, A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem. 2004,75,4193-4201.
[13] Kersey, P. J., Duarte, J., Williams, A., Karavidopoulou, Y. et al, The International Protein Index: An integrated database for proteomics experiments.2004, 4, 1985-1988.
[14] Liu, T., Qian, W. J., Gritsenko, M. A., Xiao, W. Z. et al, High Dynamic Range Characterization of the Trauma Patient Plasma Proteome. Mol. Cell Proteomics 2006, 5, 1899-1913.
[15]Ho,E.,Hayen,A.,Wilkins,M.R.,Characterisation of organellar proteomes:A guide to subcellular proteomic fractionation and analysis.Proteomics 2006,6,5746-5757.
[16]Qin,L.X.,Tang,Z.Y.Liu,Y.K.,Qin,L.X.et al.,A decade's studies on metastasis of hepatocellular carcinoma.J.Cancer Res.Clin.Oncol.2004,130,497-513.
[17]石作珍,赵春娥,成年肝癌与免疫球蛋白检测水平分析.实用医技杂志2006,13,3782.
[18]Wang,N.,Lin,K.K.,Lu,Z.,Lam,K.S.et al.,The LIM-only factor LMO4regulates expression of the BMP7.gene through an HDAC2-dependent mechanism,and controls cell proliferation and apoptosis of mammary epithelial cells.Oncogene 2007,26,6431-41.
[19]Yang,G.H.,Fan,J.,Xu,Y.,Qiu,S.J.et al.,Osteopontin Combined with CD44,a Novel Prognostic Biomarker for Patients with Hepatocellular Carcinoma Undergoing Curative Resection.Oncologist 2008,13,1155-1165.
[20]Bedossa,P.,Peltier,E.,Terris,B.,Franco,D.et al.,Transforming growth factor-beta 1(TGF-beta 1) and TGF beta 1 receptor in normal cirrhotic,and neoplastic human livers.Hepatology 1995,21,760-766.
[21]Tang,Z.Y.,Ye,S.L.,Liu,Y.K.,Qin,L.X.et al.,A decade's studies on metastasis of hepatocellular carcinoma.J.Cancer Res.Clin.Oncol.2004,130,187-196.
[22]Sjoeblom,T.,Jones,S.,Wood,L.D.et al.,The consensus coding sequences of human breast and colorectal cancers.Science,2006,314,268-274.
[23]卜文,黄晓武,汤钊猷,MMP-2在肝细胞癌侵袭转移中的作用.中华医学杂志1997,79,661-664.
[24]Wilson,C.L.,Heppner,K.J.,Labosky,P.A.et al.,Intestinal tumorigenesis is suppressed in mice lacking the metalloproteinase matrilysin.PNAS 1997,94,1402-1407.
[25]Gu,J.G.,Sato,Yu,Y.,Kariya,Y.,A Mutual Regulation between Cell-Cell Adhesion and N-Glycosylation:Implication of the Bisecting GlcNAc for Biological Functions.Journal of Proteome Research 2009,8,431-435.
[26]汤钊猷.粘附分子与肝癌转移.见汤钊猷,主编.肿瘤转移复发的基础与临 床.第一版.上海:上海科技出版社,2003,113-124.
[27]Giancotti,F.G.,Mainiero,F.et al.,Integrin-mediated adhesion and signaling in tumorgenesis.Biochem Biophys Acta.1994,1198,47-64.
[1]Liu,T.,Qian,W.J.,Gritsenko,M.A.,CampⅡ,D.G.et al.,Human plasma N-glycoproteome analysis by immunoaffinity subtraction,hydrazide chemistry, and mass spectrometry. J. Proteome Res. 2005, 4, 2070-2080.
[2] Liu, T., Qian, W. J., Gritsenko, M. A., Xiao, W. Z. et al, high dynamic range characterization of the trauma patient plasma proteome. Mol Cell Proteomics 2006,5,1899-1913.
[3] Lee, A., Kolarich, D., Haynes, P. A., Jensen, P. H. et al, Rat liver membrane glycoproteome: enrichment by phase partitioning and glycoprotein capture. J.Proteome Res. 2009, 2, 770-781.
[4] Chen, R., Zou, H. F., Sun, D. G., Han, G. H. et al, glycoproteomics analysis of human liver tissue by combination of multiple enzyme digestion and hydrazide chemistry. J. Proteome Res. 2009, 2, 651-661.
[5] Robinson, N. E., Protein deamidation. Proc. Natl Acad. Sci. U.S.A. 2002, 99,5283-5288.
[6] Li X.J., Cournoyer, J. J., Lin, C, O'Connora, P. B., Use of ~(18)O labels to monitor deamidation during protein and peptide sample processing. J. Am. Soc. Mass Spectrom. 2008,19, 855-864.
[7] Halgglund, P., Matthiesen, R., Elortza, F., H(?)jrup, P. et al, An enzymatic deglycosylation scheme enabling identification of core fucosylated N-glycans and O-glycosylation site mapping of human plasma proteins. J. Proteome Res. 2007,6,3021-3031.
[8] Kaji, H., Saito, H., Yamauchi, Y., Shinkawa, T. et al, Lectin affinity capture,isotope-coded tagging and mass spectrometry to identify N-linked glycoproteins. Nat. Biotechnol 2003, 21, 667-672.
[9] Zhou, Y., Aebersold, R., Zhang, H., Isolation of N-Linked Glycopeptides from Plasma. Anal. Chem. 2007, 79, 5826-5837.
[10] Sun, B. Y, Ranish, J. A., Utleg, A. G, White, J. T. et al, Shotgun glycopeptide-capture approach coupled with mass spectrometry for comprehensive glycoproteomics. Mol Cell Proteomics 2007, 6, 141-149.
[11] Zhang, H., Li, X. J., Martin, D. B., Aebersold, R., Identificatin and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat. Biotechnol. 2003, 21, 660-666.
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