蛋白质组学技术在小儿肾母细胞瘤临床中的应用研究
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摘要
研究背景:
     肾母细胞瘤(Nephroblastoma)是婴幼儿最常见的腹部肿瘤,亦称肾胚胎瘤或Wilms瘤,美国国家肾母细胞瘤研究组(NWTS)调查显示:手术切除加术后规范化疗,肾母细胞瘤Ⅰ期患儿8年存活率为90.5%~98.9%,Ⅱ~Ⅲ期为73.4%~88.7%,Ⅳ期仅为45.0%~57.1%。早期筛选及诊断并及时治疗对其预后有显著意义。但目前所使用的临床检查手段,尚不能满足对肾母细胞瘤早期筛选及诊断的要求。NWTS在其2001年的总结中提到:应用CT诊断直径小于3cm的肾母细胞瘤,仍有7%的漏诊率。因此,探索和建立一种简单、快速、敏感性高和特异性强的早期诊断技术已经成了临床医学上的迫切需要。
     任何疾病在出现病理变化之前,细胞内的蛋白质在成分和数量上都会有相应的改变,因此在理论上,通过对蛋白质的动态观察可以筛选出疾病早期的指标和征兆。当然实现这种诊断的前提是要找到各种疾病的特殊标志的分子,这种筛选是高通量的,用以前的常规方法很难做到。研究显示,肾母细胞瘤与一些基因的表达有关,比如WT1(11p13),WT2(11p15),在16p,1p and 17p发生基因突变。但是,由于WT1仅在15%的肾母细胞瘤患儿体内发现,而且目前其机理尚不清楚,这些基因不能被作为肾母细胞瘤早期诊断和判断预后的特异性标记物。蛋白质组学以细胞内全部蛋白质的存在及其活动方式为研究对象,开辟了生命科学研究进入后基因组时代的新天地,也为临床医学研究提供了全新的技术手段和思维模式。
     目前,对恶性肿瘤蛋白质标记物的检测已经成为肿瘤研究的热点。美国Ciphergen公司研制的表面增强激光解析电离飞行时间质谱SELDI-TOF-MS (Surface Enhanced Laser Desorption/Ionization Time of Flight-Mass Spectrometry)技术,可以非特异性地结合被测样本中的各种蛋白质,当在质谱仪中受激光轰击时,各种结合的蛋白质会被激发而形成气化离子,由于不同质荷比的离子在电场中飞行的时间长短不一,因此接收装置可根据蛋白质质荷比的不同及量的多寡,直观的用位置、强弱不同的峰表现出来,进而形成图谱用于分析。SELDI蛋白指纹技术在吸收传统质谱技术的基础上,克服了常规蛋白检测技术的缺点,增加了特异性蛋白芯片阅读系统和生物信息学分析软件,事实证明,血清、尿液等体液中含有许多疾病早期诊断的标志物,这是因为疾病早期就可在蛋白质水平出现未知的细微但重要的组合改变,蛋白质芯片技术使这种在复杂的背景中捕捉微小差异的研究成为可能。而且整个测定过程可以在几十分钟内完成,具有样品用量小、操作简便、灵敏度高、高通量等优点,目前已成功将其应用于卵巢癌、前列腺癌、肺癌、乳腺癌、甲状腺癌、肝癌等恶性肿瘤的诊断、肿瘤标志物的筛选及其他蛋白质组学研究中。
     本实验应用SELDI-TOF-MS技术结合支持向量机(SVM)的方法,对各期未经治疗肾母细胞瘤患儿、手术治疗后的肾母细胞瘤患儿和健康儿童血清血清蛋白质组进行检测,筛选出特异的蛋白质标记物,建立用于肾母细胞瘤早期诊断、临床分期及预后监测的血清蛋白质指纹图谱模型。在此基础上,确定待鉴定目标蛋白质;将待鉴定目标蛋白质经多维液相色谱分离纯化(HPLC)、酶解、运用串连液质联用质谱(LTQ)扫描后进行分析获得肽质量指纹图谱(PMF),通过SEQUEST软件在BIOWORK蛋白质数据库中检索,并运用生物信息学鉴定蛋白质,分析蛋白的生物学意义和生物学功能,作为肾母细胞瘤标志物的候选蛋白。
     研究目的:
     检测肾母细胞瘤患儿的血清蛋白质,筛选其特异性蛋白质标记物,构建用于肾母细胞瘤早期诊断、临床分期及预后监测的血清蛋白质指纹图谱模型。并对目标蛋白进行鉴定。
     材料与方法:
     1.材料:血清标本130例自2006年6月至2008年12月,在郑州大学第一附属医院小儿外科获得。其中,肾母细胞瘤术前标本30例(Ⅰ期6例,Ⅱ期10例,Ⅲ期10例,Ⅳ期4例)、术后2周24例(行根治术21例,行姑息切除术3例)、术后3月和6月各23例。30例术前肾母细胞瘤标本均经2位以上病理学医师证实。其中男21例,女9例。年龄24 d~10岁,平均2.8±1.6岁。门诊体检的健康儿童30例做为正常对照组。与患儿年龄、性别相匹配,男21例,女9例,平均年龄2.6±0.1岁。外周静脉血标本均于清晨空腹时抽取,静置1小时后3000rpm离心10min,收集血清样本,将样本进行编号,标记后分装储存于-80℃保存。
     2.主要试剂和仪器:来自中国科学研究院生物物理研究所:三氟乙酸(TFA瑞士Fluka公司)、乙腈(ACN美国Fisher Scientific公司)、细胞色素C(分子量12361.96)、胰岛素(分子量5734.51)、a-氰基-4-羟基肉桂酸(CHCA)、胰蛋白酶(美国Promega)、碘乙酰胺(IAM德国AppliChem公司)、二硫苏糖醇(DTT德国BIO-RAD)、Ziptip C18吸管尖(美国Millipore公司)、SPD SpeedVac真空离心浓缩系统(美国Thermo Electron公司)、液相色谱仪LC-lOAvp(日本—岛津SHIMADZU公司)、新型基质辅助激光电离飞行时间质量分析系统(MALDI-TOF MS) AXIMA-CFRTMplus(英国Kratos Analytical公司)、LTQ线性离子阱液质联用质谱仪(美国Thermo Finnigan公司)。
     3.质谱分析前样本的制备过程
     (1)确立目标蛋白质
     SELDI蛋白芯片操作路线:冰浴中解冻血清标本,4℃10000rpm(离心半径0.5 m)离心2 min。取96孔板置冰盒上,每孔加U9(9 mol/L尿素,2%CHAPS,1%DTT)10μl,血清5μl,4℃层析柜600 rpm振荡30 min。在震荡结束前15 min做芯片预处理,芯片装入Bioprocessor中,记下芯片号,每孔加NaAC (100 mmol/L, pH 4)200μl,层析柜中600 rpm振荡2 min,重复以上操作1次。U9处理后的96孔板置冰上,排枪加NaAC 185μl,层析柜4℃600 rpm震荡2 min。取已处理的样本100μl加到芯片上,置层析柜4℃600 rpm结合1h,甩去残液,快速拍干。加NaAC 200μl,600 rpm振荡5 min后,甩掉,拍干,重复3次。200μl去离子水冲洗各孔2次,快速甩干。每孔分两次加入50%饱和的SPA 1μl,干燥后上机待测。
     数据收集与处理:用已知分子量的蛋白芯片将SELDI-TOF-MS系统校正到分子量误差小于0.1%。将结合好蛋白质的WCX2蛋白芯片用质谱阅读仪分析。以质控血清作重复性检测。所有数据用Proteinchip Software 3.1做校正,使总离子的强度及分子量均一化。ZUCI-ProteinChip Data Analyze System软件包分析。质谱原始数据经过滤噪音,聚类分析处理后,对初步筛选出的质荷比峰数据做Wilconxon秩和检验,检验标准取a=0.01。使用线性的支持向量机(SVM)分类器,具体设置:采用径向基核函数,Gamma值设为0.6,罚分函数(c)设为19。特征向量的选取采用统计过滤结合模型依赖性筛选的方法,建立判别模型,用留一法交叉验证评估模型的判别效果。
     (2)纯化目标蛋白质
     于—80℃冰箱中取出血清样本,冰水浴中解冻。解冻后,取血清100μl,加入350μl超纯水,再加入700μl 1纯ACN。将上述混合液置入—20℃冰箱中,30 min后取出,离心10 min(13000 rpm)。取离心后的上清液移入新试管,置入SPDSpeedVac中冻干约20 min。将冻干后的样品上样至高效液相色谱仪(HPLC)中。收集不同时间的纯化液。将纯化出的蛋白液置入SPD SpeedVac中冻干,冻干至约20μl。将上述样品与CHCA各取1.5μl,两者混合后点样至MALDI靶板上,待干同时用Cytochrome C+CHCA和Insulin+CHCA校样。将靶板置入MALDI-TOF-MS中检测,找出SELDI-TOF-MS所筛质荷峰值的特异样本。
     (3)蛋白质酶解
     将蛋白液加超纯水至容积为40μl,再加入配好的浓度为0.1 mol的DTT溶液(11 mol的DTT溶液5μl+45μl的超纯水)4μl后,置于37℃温水中水浴1 h。向44μl的溶液中加入1.6μl的IAM后避光放置1 h。向45.6μl的溶液中依次加入1.6μl的1mol的DTT溶液,150μl的0.1 mol的NH4HCO3(碳酸氢氨)溶液和2μl的胰蛋白酶,然后置于37℃温水中过夜(6-8 h)。
     4.蛋白质鉴定
     取经酶解后蛋白液填柱上样。将样品全部上到毛细色谱柱上,经过液质联用质谱扫描后得到得肽质量指纹图谱,经过SEQUST检索程序在Bioworks数据库查询。运用生物信息学鉴定蛋白质,分析蛋白的生物学意义和生物学功能,作为肾母细胞瘤标志物的候选蛋白。
     5.统计学处理
     实验数据用x±s表示,根据数据类型采用不同的检验方法。患儿术前与正常对照组;肾母细胞瘤患儿各期;手术前后、术后与正常对照组的质谱数据分析使用t检验,各分期的质谱数据使用单因素方差分析,方差不齐使用秩和检验,以P(0.05做为差异有显著性的检验标准。全部统计计算均在SPSS13.0软件处理包下完成。
     结果:
     1.应用SELDI-TOF-MS技术对肾母细胞瘤患儿血清蛋白标记物筛查
     1.1肾母细胞瘤血清蛋白质指纹图谱实验方法的建立及蛋白质芯片种类的筛选:本研究对血清样本处理时使用了0.5%CHAPS,应用Cibacron Blue 3G特异性地除去血清中的白蛋白,通过对4种不同的蛋白芯片WCX2蛋白芯片、SAX2蛋白芯片、IMAC3蛋白芯片及H4蛋白芯片研究发现,不同芯片所能结合的血清蛋白质的数量不同,H4芯片在本研究中证实能得到更多有意义的峰值,可更好地应用于肾母细胞瘤血清蛋白质指纹图谱的研究,而且具有很好的重复性。
     1.2本研究首次报道运用SELDI-TOF-MS技术结合SVM建立肾母细胞瘤血清蛋白质指纹图谱模型。60例血清标本的蛋白质指纹图谱经质谱仪收集数据,随机分为训练组和测试组。在收集每次实验数据前用己知分子量的标准蛋白芯片校正,误差小于0.1%。以质控血清作重复性检测,其峰值大小及其强度的变异系数(CVs)均控制在误差范围内(0.05%和15%以下),应用ProteinChip Software 3.1(Ciphergen Inc)软件同时将所有样本的质谱数据M/Z在2000到30000的峰值进行两次信噪比过滤,找出样本质荷峰,以10%为最小阈值对质荷峰进行聚类。采用Wilcoxon秩和检验分析,根据P值评价各个峰对区分两类样本的相对重要性。将差异显著的质荷峰随机组合输入SVM,筛选标志物,建立判别模型。用留一法交叉验证评估模型,进行盲法测试。按排列顺序依次增加M/Z峰,本研究发现,测试集的youden指数在M/Z的组合为5的时侯最佳,效果可达98.6%的准确率。这5个M/Z峰分别为6455,6984,3221,5639,9190。用30例样本做训练,30例样本做为盲法测试样本。建立模型的特异度为98.3%(95%的可信区间为85%~100%),敏感度为98.9%(95%的可信区间为89%~100%),Youden指数值为0.87551,表明该模型在鉴别肾母细胞瘤中有很好的诊断价值。
     1.3 M/Z峰分别为6455、6984、3221、5639、9190的质谱图显示,它们在肾母细胞瘤中低表达,在健康儿童中高表达。其中,M/Z位于6455,9190的峰值在健康儿童、肾母细胞瘤患儿中表达依次降低,设想M/Z 6455,M/Z 9190的蛋白质可能在肾母细胞瘤的发生发展中起重要作用。
     2.血清蛋白标记物在肾母细胞瘤患儿临床分期及预后中的应用
     2.1经过初步筛选,得到肾母细胞瘤Ⅰ期组和肾母细胞瘤Ⅱ期组质谱483个M/Z峰,筛选出3个M/Z峰5022.4、7965.4和8469.6,它们在肾母细胞瘤Ⅰ期组中高表达,在肾母细胞瘤Ⅱ期组中低表达。在测试集上判别模型的敏感性为80.00%,特异性为100.00%。肾母细胞瘤Ⅰ期组和肾母细胞瘤Ⅲ期组质谱496个M/Z峰,筛选出4个M/Z峰4330.7、4303.7、4263.2和4122.8,它们在肾母细胞瘤Ⅰ期组中高表达,在肾母细胞瘤Ⅲ期组中低表达。在测试集上判别模型的敏感性为100.00%,特异性为100.00%。肾母细胞瘤Ⅰ期组和肾母细胞瘤Ⅳ期组质谱565个M/Z峰,筛选出2个M/Z峰8179.1和10836.6,它们在肾母细胞瘤Ⅰ期组中高表达,在肾母细胞瘤Ⅳ期组中低表达。在测试集上判别模型的敏感性为88.89%,特异性为100.00%。肾母细胞瘤Ⅱ期组和肾母细胞瘤Ⅲ期组质谱490个M/Z峰,筛选出2个M/Z峰4143.2和5019.2,它们在肾母细胞瘤Ⅱ期组中高表达,在肾母细胞瘤Ⅲ期组中低表达。在测试集上判别模型的敏感性为88.89%,特异性为100.00%。肾母细胞瘤Ⅱ期组和肾母细胞瘤Ⅳ期组质谱508个M/Z峰,筛选出M/Z峰为7976.5在肾母细胞瘤Ⅱ期组中高表达,在肾母细胞瘤Ⅳ期组中低表达。在测试集上判别模型的敏感性为100.00%,特异性为100.00%。肾母细胞瘤、Ⅲ期组和肾母细胞瘤Ⅳ期组质谱504个M/Z峰,筛选出M/Z峰为8194.4在肾母细胞瘤Ⅲ期组中高表达,在肾母细胞瘤Ⅳ期组中低表达。在测试集上判别模型的敏感性为93.75%,特异性为100.00%。肾母细胞瘤Ⅰ+Ⅱ期组和肾母细胞瘤Ⅲ+Ⅳ期组质谱519个M/Z峰,筛选出2个M/Z峰3257.6和4153.9,它们在肾母细胞瘤Ⅰ+Ⅱ期组中高表达,在肾母细胞瘤Ⅲ+Ⅳ期组中低表达。在测试集上判别模型的敏感性为83.33%,特异性为93.75%。
     2.2随着患儿临床分期的增高,M/Z位于6455,9190的峰值在肾母细胞瘤患儿血清中表达依次降低。
     2.3肾母细胞瘤患儿SELDI分期:Ⅰ期6例,Ⅱ期10例,Ⅲ期10例,Ⅳ期4例,30例肾母细胞瘤分期均与病理结果相一致,各期符合率为100.00%。通过病理及手术证实,CT各期符合率依次为100.00%,85.00%,85.00%,75.00%。
     2.4肾母细胞瘤手术前、后组比较筛选到差异最显著的m/z位于6455.5、9190.8处的蛋白质标志物,在术前组血清中低表达,术后组及健康儿童组中呈高表达,差异有统计学意义(P<0.01);术后组和正常小儿组间差异无统计学意义(P>0.01)。表明瘤体切除后,这种抑制作用被削弱,蛋白质表达升高。
     2.5对肾母细胞瘤患儿手术后血清蛋白质谱及健康儿童组比较,发现将21例根治术后与术前患儿标本比较,M/Z峰值位于6455.5表达在术后2周、3个月和6个月,根治术组分别为2087.21±658.33、2189.67±856.42和2232.16±598.32(与对照组比较:P>0.05;与术前组比较:P<0.01),姑息术组分别为1044.86±533.21、677.86±435.26和431.65±158.31(与对照组比较:P<0.01;与术前组比较:P>0.05);M/Z峰值位于9190.8表达在术后2周、3个月和6个月,根治术组分别为1101.20±219.69、1078.21±322.43和1066.06±110.45(与对照组比较:P>0.05;与术前组比较:P<0.01),姑息术组分别为328.11±210.26、289.10±108.09和276.49±92.15(与对照组比较:P<0.01;与术前组比较:P>0.05)。姑息术组在术后2周、3月、6月血清中2个蛋白标记物呈持续低表达;根治术患儿术后3月、6月跟踪复查发现血清蛋白质在此两个峰值表达无明显改变。分期愈低,预后愈好,M/Z强度愈高。考虑3例姑息性切除术由于仍有瘤体存在,被抑制的蛋白质持续受到抑制,蛋白质标记物呈持续低表达,除1例术后2月死亡外,2例术后3及6个月表达继续下降,可能是该蛋白质标记物被抑制加深的结果。3.肾母细胞瘤血清蛋白质标记物的鉴定
     在本实验中,我们将质荷峰为6455.5、9190.8的血清样本确立为目标蛋白质,通过鉴定二者的氨基酸序列,蛋白质数据库搜索,与这2种差异蛋白质分子量相对应的蛋白质分别为:载脂蛋白C-Ⅰ和触珠蛋白。我们推测,载脂蛋白C-
     Ⅰ、触珠蛋白可能是判定肾母细胞瘤恶性程度鉴别、治疗转归、预后发展的候选蛋白。
     结论:
     用蛋白组学技术筛选出的特异性生物标志物载脂蛋白C-Ⅰ、触珠蛋白是肾母细胞瘤早期诊断、临床分期及预后监测的候选蛋白。
Backgroud:
     Nephroblastoma or Wilms'tumor is the most common pediatric tumor of the kidney. A study led by National Wilms' Tumor Study Group (NWTS) shows that by combining the nephrectomy with postnephrectomy chemotherapy, the 8-year relapse-free survival rate is within the range of 90.5% to 98.9% for stage I Wilms' tumor and is 73.4% to 88.7% for stageⅡandⅢWilms' tumor and decreases to 45.0% to 57.1% for stageⅣWilms'tumor, demonstrating the great significance of early detection for the treatment of Wilms'tumor. Early diagnosis, treatment and regular follow-up are important measures that should be taken to prevent recurrence and improve long-term survival rate. In another study conducted by NWTS in 2001, it is reported that the diagnosis of a noteworthy 7 percent cases is still missed mainly by CT scan, the majority of which were those having a tumor with a diameter less than 3 cm.So an effective, sensitive, specific diagnostic method is eagerly needed.
     The component and quantity of proteins in the cells will change prior to the occurrence of the pathological changes in each disease. So in theory, it is possible that the index and signs of diseases will be screened in early stage by dynamic observation of the protein. The precondition is to find the specific molecule for certain disease. The screening technology is high-throughput and can not be completed with the traditional techniques.Several genes have been implicated in the development of Wilms'tumour, including WT1 (11p13), WT2 (11p15) and abnormalities in 16p, 1p and 17p. However, these genes are not suitable as biomarkers for early screening and detection. For example, WT1 mutations are only found in 15% of WTs and therefore cannot be used alone either as a marker or a predictor of therapy or can not explain WT development in the other 85% of children.In recent years, the research and application of proteomics along with its related high technology make this large scale screening come true.the rapid development of proteomics has provided new technology platforms to find new tumor-markers and opens a fresh new stage of the molecular diagnosis technology for the clinical application.
     To date, the search for malignant tumor makers has attracted high attention. The SELDI-TOF-MS (Surface Enhanced Laser Desorption/Ionization Time of Flight-Mass Spectrometry) system developed by Ciphergen is a novel proteomic analysis tool. Based on the method,protein chips can non-specifically combine with various proteins, which will be converted to ions when bombarded by laser in the mass spectrometer. Because of their different flying time in the electric field, ions with different mass/charge ratio may be produced and detected by the detector, then different kurtoses are diplayed, thereby generating characteristic protein fingerprints. This technique has the advantages of small sample size, easy operation, high sensitivity and high throughput and has been successfully applied to the diagnosis, tumor marker screening and other proteomic researches of a variety of malignant tumors such as ovarian cancer, prostate cancer, breast cancer, lung cancer, liver cancer and thyroid carcinoma etc.
     In our study, SELDI-TOF-MS and support vector machine(SVM) were used to screen specific protein markers in serum taken from children with nephroblastoma. we examined serum protein in children patients with Wilms'tumor at various stages, healthy controls and post-nephrectomy patients and construct a serum protein fingerprint model for the early diagnosis, clinical staging and outcome monitoring of Wilms tumor.After that, the target proteins were separated and purified by multidimensional high performance liquid chromatography (HPLC). The peptide mass fingerprints (PMFs) were obtained after scanning with 2D-LC-LTQ-MS. Then the potential biomarkers were identified by searching PMFs in Bioworks database using the SEQUEST program.
     Objective:
     To identify specific serum protein markers in children with Wilms'tumor, to establish a serum protein fingerprint model for the early diagnosis, clinical staging and prognosis monitoring of Wilms'tumor. And identify the pecific target protein markers.
     Subjects and Methods:
     1.Subjects:130 serum samples were collected from the Department of Pediatric Surgery at the First Affiliated Hospital of Zhengzhou University during May 2006 to Dec.2007. The 130 samples included 30 samples from nephroblastoma patients before surgery (stage 1:6, stage II:10, stage III:10, stage IV:4),70 samples from . nephroblastoma patients after surgery, and 30 samples from healthy individuals. Thirty samples in the pre-surgical group were taken from 21 males and 9 females who were confirmed to have nephroblastoma by more than two pathologists. The age of patients in the pre-surgical group was 24 days to10 years, average 2.8±0.1 years. Among the 70 postsurgical samples,24 samples were taken two weeks after surgery (radical nephrectomy:21 cases, partial nephrectomy:3 cases),23 samples were taken three months after surgery and 23 samples were taken six months after surgery. In the pre-surgical group and post-surgical group,12 patients from each group (24 patients in total:16 males and 8 females,2.6±0.1years old) were enrolled in the comparison study. None of the 24 patients received radiotherapy or chemotherapy before surgery. Thirty samples from age and gender-matched children who were confirmed healthy in clinic physical examinations were used as control, including 21 males and 9 females (average age:2.6±0.1 years). Samples were taken from peripheral veins in the early morning on an empty stomach. Samples were incubated 1 hour at room temperature and then centrifuged at 3000rpm for 10 minutes. Serums were collected and stored at -80℃before use.
     2.Reagents and equipments:Trifluoroacetic acid (TFA) was purchased from Fluka (Stockholm, Sweden). Acetonitrile was purchased from Fisher Scientific Inc (New Jersey, USA). Cytochrome c (molecular weight:12361.96), insulin (molecular weight:5734.51), a-cyanoacrylate-4-hydroxy-cinnamic acid (CHCA) and trypsin were purchased from Promega Corporation (Wisconsin, USA). Iodine acetamide (IAM) was purchased from AppliChem Inc (Darmstadt, Germany).Dithiothreitol (DTT) was purchased from BIO-RAD (Germany). Ziptip C18 pipette tips were purchased from Millipore Inc (USA), SPD SpeedVac vacuum centrifuge enrichment system was from Thermo Electron Corporation (USA), liquid chromatography LC-10Avp was from Shimadzu Corporation (Japan), the new enhanced laser desorption/ionization time of flight mass spectroscopy (MALDI-TOF MS) AXIMA-CFRTMplus was from Kratos Analytical Inc (UK), and the LTQ linear ion trap LC-MS mass spectrometer was from Thermo Finnigan Corporation (USA).
     3.Preparing of serum samples before the PMFs were obtained
     3.1 Determination of the target protein
     Analysis of serum samples using SELDI protein chip was performed as follows: serum samples from -80℃were thawed on ice, then centrifuged at 10,000 rpm (centrifugal radius 0.5 m) at 4℃for 2 min. Chips were put on a 96-well plate and the plate was placed on ice, and 10μl U9 (9 mol/L urea,2% CHAPS,1% DTT) and 5μl serum were added into each well of the SELDI chip. The plate was oscillated in a chromatography cabinet at 600 rpm for 30 min. When the oscillation was begun for 15 minutes, a protein chip should be pre-processed:put the chip into the bioprocessor, record the chip number, add 200μl NaAC (100mmol/L, pH4) to each well, and oscillate in a chromatography cabinet at 600 rpm for 2 min, then the procedures were repeated once. After pre-processing the chip and the 96-well U9 plate was finished, the 96-well plate was placed on ice and 185μl of NaAC was added to each well. The plate was then placed on the oscillator in a chromatography cabinet at 600rpm for 2 min at 4℃.A 100μl serum sample that had been balanced with CHAPS and serum albumin was removed and added to the chip, and then oscillated in the chromatography cabinet at 600rpm for 1h at 4℃. After discarding the residue and drying the chip,200μl NaAC was added to the chip and the chip was oscillated at 600rpm for 5 min. Repeat the procedures of discarding residue, drying chips, addition of NaAC and oscillation three times. Each well was rinsed with 200μl deionized water twice, and then the chip was quickly dried. Finally,1μl 50% saturated Sinapinic acid (SPA) was added to each well and chips were dried before analysis.
     A protein chip with a known molecular weight was used to rectify SELDI-TOF-MS and make sure the error of the molecular weight was less than 0.1%. The WCX2 protein chip with bound proteins was analyzed by a mass spectrometer. High quality serum was used as control to check the repeatability. All data were rectified by Proteinchip Software 3.1, ensuring that the total ionic intensity and molecular weight were homogenized. Data were analyzed using the ZUCI-Protein Chip Data Analyze System software package. MS raw data were noise filtered and examined by cluster analysis. Results of m/z peaks obtained after preliminarily screening were analyzed using Wilconxon rank sum test (a=0.01). The linear support vector machine (SVM) classifier was also used in the study:RBF kernel function was adopted, with the gamma value at 0.6 and penalty function (c) at 19. Support vectors were selected by statistical filtering in combination with model-dependent screening. An evaluation model was established, evaluated and validated by leave-one-out cross-validation.
     3.2 Purification of the target proteins
     After potential biomarker proteins were found by SELDI-TOF-MS, the proteins were purified. Serum samples stored at -80℃were thawed in ice water. After thawing,100μl serum was mixed with 350μl ultra-pure water and 700μl pure CAN, and then incubated at -20℃for 30min, centrifuged at 13,000rpm for 10min. The supernatant was transferred into a new tube, and the tube was placed freeze-dried in an SPD SpeedVac for about 20min. Then the freeze-dried samples were analyzed by high-performance liquid chromatography (HPLC:1mm×100mm column), and the purified solution of different time courses was collected. The purified protein solution was freeze-dried in the SPD SpeedVac until the volume was about 20μl.1.5μl of the prepared sample was mixed with 1.5μl a-cyanoacrylate -4-hydroxy-cinnamic acid (CHCA), and then spotted onto the MALDI target plate. Before analysis by MALDI-TOF-MS, controls (cytochrome c+CHCA and Insulin+CHCA) were used to rectify the machine accuracy. Then the target plate was put onto MALDI-TOF-MS to identify purified samples whose m/z peak values were screened by SELDI-TOF-MS.
     3.3 Enzyme hydrolysis
     Proteins were hydrolyzed before identification, which was performed as follows: ultra-pure water was added to the purified protein sample until the volume was 40μl. Then 4μl O.lmol DTT solution (5μl 11 mol DTT solution+45μl ultra-pure water) was added and incubated for at 37℃1 hour. Then 1.6μl Iodine acetamide (IAM) was added to the solution and the mixture was incubated in the dark for 1h. After that, 1.6μl 1mol DTT solution,150μl 0.1mol NH4HCO3 and 2μl parenzyme was subsequently add to the solution, then the mixture was incubated at 37℃overnight (6-8 h).
     3.4.Identification of proteins
     After enzyme hydrolysis, purified protein samples were analyzed by 2D-LC-LTQ-MS using nitrogen laser with the following settings:laser wavelength-337nm, pulse duration-3ns, mass spectrum accumulation of signal from 50 single scan, accelerating voltage-30kv, absorbing voltage-9.3kv, detecting voltage-4.75kv, vacuum degree-1×10-6Pa, and detecting mode-positive ion. Peptide mass fingerprints (PMFs) were used as searching objects in Bioworks data based by SEQUEST. Potential biomarker proteins were identified, and their biological functions and characterization were analyzed using bioinformatics methods.
     3.5Statistical anaylsis
     Data were presented as x±s. All statistic analysis was performed using SPSS 13.0 software. The t test was applied in the MS data analyses between the pre-surgery group and the control group, the pre-surgery and the post-surgery group, the post-surgery group and the control group. One-way Anova analysis was used to analyze mass spectrometry data of different stage. Rank sum test was used to analyze nonhomogeneous data. Difference was regarded as significantly when P<0.05.
     Results:
     1. Screening of serum protein biomarkers in child with Wilms'tumor by SELDI-TOF-MS
     1.1 Establishing experimental methods concerned with a serum protein fingerprint in Wilms'tumor and screening proteinchip types:In this study, we processed the serum samples by using the 0.5% CHAPS, and applied to Cibacron Blue 3GA specifically in order to remove serum albumin. Through the study of four kinds of different proteinchip:WCX2 proteinchip, SAX2 proteinchip, IMAC3 proteinchip and H4 proteinchip, we found that different chip can combine different number of serum proteins, and H4 chip in this study proved to be able to get more meaningful peak, which not only can be better applied to the serum protein fingerprinting studies of Wilms'tumor, but also has very good repeatability.
     1.2 This study is the first reported to establish a serum protein fingerprint based on models of Wilms'tumor by use of SELDI-TOF-MS technology combined with SVM60 cases of serum samples protein fingerprinting were randomly divided into training group and test group, and the datas of which were collected by a mass spectrometer.efore each collection of experimental data we use known proteinchip with standard molecular weight to calibrate and make sure the error is less than 0.1%.Testing the quality control serum repetitively to be sure its peak size and intensity of the coefficient of variation (CVs) are controlled within the error range (0.05% and 15% or less), and applying ProteinChip Software 3.1 (Ciphergen Inc)software the same time as MS raw data were noise filtered and examined twice by cluster analysis ranging from 2000 to 30000. then, finding out M/Z peak of samples, defining 10% M/Z peak as minimum threshold to cluster. Using Wilcoxon rank sum test to analyze, and assess the relative importance of the various peaks to distinguish two types of samples according to P value. Combining the M/Z peak with significant difference and randomly inputting SVM, then screen markers to establish discriminant model. With the method of leave-one-out and cross-validation assessment model, we carried out blind test, increased the peak of M/Z successively according to the order. This study found that youden index of test set got best when the combination of M/Z is 5, and the results proved accuracy could reach up to 98.6%.This 5 M/Z peak is respectively 6455,6984,3221,5639,9190. With 30 cases of samples as the training group,30 cases of samples as a blind test group, the specificity of established model was 98.3%(95%confidence interval 85%~100%), a sensitivity of 98.9%(95% confidence interval 89%~100%), Youden index value of 0.87551, which all indicated that the model has a good diagnostic value in the aspect of differentiating Wilms'tumor.
     1.3 With M/Z peaks respectively 6455,6984,3221,5639,9190, the mass spectrum diagram showed that they were in low expression within Wilms' tumors,whereas in healthy children were highly expressed. At the same time, M/Z peak in 6455 and in 9190, their expression had a sequential decline manner followed healthy children and children with Wilma's tumor,so we supposed that M/Z 6455, M /Z 9190 protein may be playing an important role in the occurrence and development of Wilma's tumor.
     2. Application of serum protein markers in the clinical staging and prognosis monitoring of children with Wilms'tumor
     2.1483 M/Z peaks were obtained from mass spectral of Wilms'tumor stageⅠgroup andⅡgroup after a preliminary screening. Three M/Z peaks of 5022.4,7965.4,8469.6 were identified, whose expression levels were high in patients with Wilms'tumor stage I group but were low in Wilms'tumor stageⅡgroup.They have a sensitivity of 80.00% and a specificity of 100% for the discrimination model in the test set. And 496 M/Z peaks were obtained from mass spectral of Wilms'tumor stageⅠgroup andⅢgroup. Four M/Z peaks of 4330.7,4303.7, 4263.2 and 4122.8 were identified, whose expression levels were high in patients with Wilms'tumor stageⅠgroup but were low in Wilms' tumor stageⅢgroup. The sensitivity and specificity are both 100% for the discrimination model in the test set. Meanwhile 565 M/Z peaks were obtained from mass spectral of Wilms'tumor stageⅠgroup andⅣgroup. Two M/Z peaks of 8179.1 and 10836.6 were identified, whose expression levels were high in patients with Wilms'tumor stage I group but were low in Wilms'tumor stageⅣgroup. Discriminant model in the test set was 88.89% sensitivity and specificity of 100.00%.And 490 M/Z peaks were obtained from mass spectral of Wilms'tumor stageⅡgroup andⅢgroup. Two M/Z peaks of 4143.2 and 5019.2 were identified, whose expression levels were high in patients with Wilms'tumor stageⅡgroup but were low in Wilms'tumor stageⅢgroup. They have a sensitivity of 88.89% and a specificity of 100% for the discrimination model in the test set. And 508 M/Z peaks were obtained from mass spectral of Wilms'tumor stageⅡgroup and IV group. The selected M/Z peak 7976.5 has a high expression in Wilms tumor stageⅡgroup but low expression in stageⅣgroup. The sensitivity and specificity are both 100% for the discrimination model in the test set.And 504 M/Z peaks were obtained from mass spectral of Wilms'tumor stageⅢgroup andⅣgroup. The selected M/Z peak 8194.4 has a high expression in Wilms tumor stageⅢgroup but low expression in stageⅣgroup. Discriminant model in the test set was 93.75% sensitivity and specificity of 100.00%.And 519 M/Z peaks were obtained from mass spectral of Wilms'tumor stageⅠ+Ⅱgroup andⅢ+Ⅳgroup. Two M/Z peaks of 3257.6 and 4153.9 were identified, whose expression levels were high in patients with Wilms'tumor stageⅠ+Ⅱgroup but were low in Wilms' tumor stageⅢ+Ⅳgroup. They have a sensitivity of 83.33% and a specificity of 93.75% for the discrimination model in the test set.
     2.2 With the clinical staging in children increasing, M/Z peaks at 6455,9190 expressed in serum decreased in order of the staging.
     2.3 Wilms tumor in children with SELDI stages:I period of 6 cases,Ⅱperiod of 10 cases,Ⅲperiod of 10 cases,Ⅳperiod of 4 cases,30 cases of Wilms tumor staging and the pathological findings were consistent with the coincidence rate was 100.00%. Confirmed by pathology and surgery, CT diagnostic accordance rates followed by periods were 100.00%,85.00%,85.00%,75.00%.
     2.4 We screened protein markers to the pre-operative and post-operative in child with Wilms tumor.We found the difference was the most significant when m/z was at 6455.5,9190.8 department, which expressed lowly in the serum of pre-operative group, in the post-operative group and showed high expression in healthy children group.The difference was statistically significant (P<0.01); Between the two groups of post-operative and normal children,the result showed no significant difference (P> 0.01). The above all indicated that protein expression increased as inhibitory effect was weakened after tumor resection.
     2.5 Compared serum protein spectrum with post-operative children with Wilms tumor and healthy children group, and compared with the pre-operative and post-operative specimens in 21 cases of radical surgery children at the different time of 2 weeks,3 months and 6 months after operation,we found M/Z peaks at 6455.5, the protein expression in radical surgery group were respectively 2087.21±658.33,2189.67±856.42 and 2232.16±598.32 (compared with control group:P> 0.05; compared with preoperative group:P<0.0.1), in palliative surgery group were respectively 1044.86±533.21,677.86±435.26 and 431.65±158.31 (compared with control group:P<0.01; compared with preoperative group:P> 0.05);While M/Z-peaks at 9190.8,at the time of 2 weeks,3 months and 6 months after operation,the protein expression were respectively 1101.20±219.69,1078.21±322.43 and 1066.06±110.45 (compared with control group:P> 0.05; compared with preoperative group: P<0.01) in radical surgery group,in palliative group were respectively 328.11±210.26,289.10±108.09 and 276.49±92.15 (compared with control group:P<0.01; compared with preoperative group:P> 005).There were two protein markers in serum, which was in low-expression consecutively in palliative at 2 weeks,3 months, 6 months after operation; While in radical surgery group, we found that serum protein was no significant change at the two peaks by tracking review 3 months,6 months after operation.The lower stages, the better prognosis is, and M/Z intensity is higher.Considering the tumor still exist in the three cases of palliative resection and the inhibited protein continued to be inhibited, protein markers was in sustained low expression. Except for 1 patient died in 2 months after operation, expression of 2 cases continued to decline at 3 months and 6 months after operation, the result may be that the protein markers have been suppressed deeper.
     3. Identification analysis of serum protein biomarkers in child with Wilms' tumor
     In this experiment, we established the serum samples for M/Z peaks at 6455.5,9190.8 as the target protein, By identifying their amino acid sequence, searching protein database, we found apolipoprotein C-Ⅰand haptoglobin was the two protein which corresponding to their protein molecular weight, so We speculated that apolipoprotein C-Ⅰand haptoglobin may be the candidate proteins to determine the extent of malignant treatment, prognosis, and development of Wilms tumor.
     Conclusion:
     The combined use of SELDI-TOF-MS and SVM to establish a serum protein fingerprint model for Wilms'tumor offers a novel, highly specific and sensitive method for the early diagnosis, clinical staging and prognosis monitoring of this condition. Apolipoprotein C-Ⅰand haptoglobin may be used as potential biomarkers to predict the degree of malignancy, therapeutic outcomes, and prognosis of nephroblastoma in children.
引文
1. P.F.Ehrlich. Wilms tumor:Progress and considerations for the surgon. Surgical Oncology,2007,16:157-171.
    2. Green DM.The treatment of stages I-IV favorable histology Wilms'tumor.J Clin Oncol,2004, 22:1366-1372.
    3. Thomson G,Blair G.Multislice helical CT depiction of Wilms' tumor.J Pediatr SURG,2001,27:912-915.
    4. Wright GL Jr. SELDI proteinchip MS:a platform for biomarker discovery and cancer diagnosis. Expert Rev Mol Diagn,2002,2:549-563.
    5. Weinberger SR, Boschetti E, Santambien P, et al. Surface-enhanced laser desorption-ionization retentate chromatography mass spectrometry (SELDI-TOF-MS):a new method for rapid development of process chromatography conditions. J ChromatogrB Analyt Technol Biomed Life Sci,2002,782:307-316.
    6. Vlahou A, Schorge JO, Gregory BW, et al. Diagnosis of ovarian cancer using decision tree classification of mass spectral data. J Biomed Biotechnol,2003,2003:308-314.
    7. Wagner M, Naik DN, Pothen A, et al. Computational protein biomarker prediction:a case study for prostate cancer. BMC Bioinformatics,2004,11:5-26.
    8. Vlahou A, Laronga C, Wilson L, et al. A novel approach toward development of a rapid blood test for breast cancer. Clin Breast Cancer,2003,4:203-209.
    9. Xiao XY, Tang Y, Wei XP, et al. A preliminary analysis of non-small cell lung cancer biomarkers in serum. Biomed Environ Sci,2003,16:140-148.
    10. Wang JX, Zhang B, Yu JK, et al. Using ANN and serum protein pattern models in liver cancer diagnosis. Zhorighua Yi Xue Za Zhi,2005,85:189-192.
    11. Wang JX, Wang L, Fan YZ,et al. Application of serum protein fingerprint model and support vector machine in diagnosis of thyroid cancer. Zhonghua Yi Xue Za Zhi,2006,86:979-982.
    12. Yu JK, Chen YD, Zheng S. An integrated approach to the detection of colorectal cancer utilizing proteomics and bioinformatics. World J Gastroenterol,2004,10:3127-3131.
    13. Mele C, Emst G, Schimmel B, et al. Different expression of calgizzarin(S100A11) in normal colonic epithelium, adenoma and colorectal carcinoma. International J of Oncology,2006,28(1):195-200.
    14. Yu Y, Chen S, Wang LS, et al. Prediction of pancreatic cancer by serum biomarkers using surface-enhanced laser desorption/ionization-based decision tree classification. Oncology, 2005,68:79-86.
    15. Mueller J, von Eggeling F, Driesch D, et al. ProteinChip technology reveals distinctive protein expression profiles in the urine of bladder cancer patients. Eur Urol,2005,47:885-893.
    16. Liu Y.Active learning with support vector machine app lied to gene expression data for cancer classification. Chem Inf Comput Sci,2004,44:1936-1941.
    17. Hyeran B, Lee SW. Application of support vector machines for pattern recognition:a survey//Lee SW, Verri A,eds. Pattern recognition with support vector machines. Heidelberg. Berlin:Springer verlag,2002:213-236.
    18. Miniati D, Gay AN, Parks KV,et al. Imaging accuracy and incidence of Wilms' and non-Wilms' renal tumors in children[J].J Pediatr Surg.2008,43(7):1301-1307.
    19. Lindor NM, McMaster ML, Lindor CJ,et al. Concise handbook of familial cancer susceptibility syndromes-second edition.J Natl Cancer Inst Monogr.2008, (38):3-93.
    20. Meyer JS, HartyMP, Khademian Z. Imaging of neuroblastoma and Wilms'tumor. Magn Reson Imaging Clin N Am,2002,10:275-302.
    21. Yang S, Xu L, Wu HM. Rapid multiplexed genotyping for hereditary thrombo-philia by SELDI-TOF mass spectrometry. Diagn Mol Pathol.2010;19(1):54-61.
    22. Guo JH, Wang WJ, Liao P,et al. Application of serum protein profiling in diagnosis, prognosis and evaluation of curative effect of pancreatic adenocarcinoma. Zhonghua Zhong Liu Za Zhi.2010;32(1):33-6.
    23. Okamoto A, Yamamoto H, Imai A,et al. Protein profiling of post-prostatic massage urine specimens by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to discriminate between prostate cancer and benign lesions. Oncol Rep.2009;21(1):73-9.
    24. Tang K, Li T, Xiong W,et al. Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data. BMC Bioinformatics.2010; 27;11(1):109.
    25. Ueda K, Fukase Y, Katagiri T,et al. Targeted serum glycoproteomics for the discovery of lung cancer-associated glycosylation disorders using lectin-coupled ProteinChip arrays. Proteomics.2009;9(8):2182-92.
    26. Gast MC, van Dulken EJ, van Loenen TK, et al. Detection of breast cancer by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry tissue and serum protein profiling. Int J Biol Markers.2009;24(3):130-41.
    27. Fan Y, Shi L, Liu Q,et al. Discovery and identification of potential biomarkers of papillary thyroid carcinoma. Mol Cancer.2009;28;8:79.
    28. Wu FX, Wang Q, Zhang ZM.et al.Identifying serological biomarkers of hepatocellular carcinoma using surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy.Cancer Lett.2009;8;279(2):163-70.
    29. Ben-Hur A, Weston J. A User's Guide to Support Vector Machines. Methods Mol Biol. 2010;609:223-39.
    30. Habib MS, Kalita J. Scalable biomedical Named Entity Recognition:investigation of a database-supported SVM approach. Int J Bioinform Res Appl.2010;6(2):191-208.
    31. Comin M, Verzotto D. Classification of protein sequences by means of irredundant patterns. BMC Bioinformatics.2010;18;11.
    32. Opiyo SO, Moriyama EN. Mining Cytochrome b561 proteins from plant genomes. Int J Bioinform Res Appl.2010;6(2):209-21.
    33. Fechner N, Jahn A, Hinselmann G, Zell A. Estimation of the applicability domain of kernel-based machine learning models for virtual screening. J Cheminform.2010;11;2(1):2.
    1. Shin DH, Lee JH, Kang HJ,et al. Novel epitheliomesenchymal biphasic stomach tumour(gastroblastoma) in a 9-year-old:morphological, usltrastructural and immuno-histochemical findings. J Clin Pathol.2010;63(3):270-4.
    2. [Primitive neuroectodermal tumor of the kidney in children; its differential diagnosis with Wilms tumor]. Arkh Patol.2009;71(6):41-3.
    3. Tamura H, Dan K, Yokose N, Iwakiri R,et al. Prognostic significance of WT1 mRNA and anti-WTl antibody levels in peripheral blood in patients with myelodysplastic syndromes. Leuk Res.2009 Dec 21.
    4. De-Nully-Brown P, et al. Trend sinsurvival after childhood cancer in Denmark,1943.1987:a population based study. Acta Paediatr 1995,84(3):316-324.
    5. Oskarsson T, Jonsson OG, Kristinsson JR, et al. [Childhood cancer in Iceland 1981-2006]. Laeknabladid.2010;96(1):21-6.
    6. Shamberger RC, Anderson JR, Breslow NE,et al. Long-term outcomes for infants with very low risk Wilms tumor treated with surgery alone in National Wilms Tumor Study-5. Ann Surg. 2010;251(3):555-8.
    7. Oue T, Yoneda A, Uehara S, Yamanaka H,et al. Increased expression of the hedgehog signaling pathway in pediatric solid malignancies. J Pediatr Surg.2010;45 (2):387-92.
    8. Burnei G, Burnei A, Hodorogea D,et al.Diagnosis and complications of renovascular hypertension in children:literature data and clinical observations. J Med Life. 2009;2(1):18-28.
    9. Raval MV, Bilimoria KY, Bentrem DJ,et al. Nodal evaluation in Wilms' tumors:analysis of the national cancer data base. Ann Surg.2010;251(3):559-65..
    10. Yang L, Wei J, He S. Integrative genomic analyses on interferon-lambdas and their roles in cancer prediction. Int J Mol Med.2010 Feb;25(2):299-304.
    11. Syse A, Loge JH, Lyngstad TH. Does childhood cancer affect parental divorce rates? A population-based study. J Clin Oncol.2010;28(5):872-7.
    12. Thomson GD, Blair GK. Multislice helical CT depiction of Wilms'tumor. J Pediatr Surg.2001;36(8):1313-1314.
    13.Grabois MF, Mendonca GA. Prognosis for patients with unilateral Wilms' tumor in Rio de Janeiro, Brazil,1990-2000. Rev Saude Publica.2005,39(5):731-737.
    14.Broecker B. Non-Wilms'renal tumors in children. Urol Clin North Am,2000,27:463-469.
    15.Meyer js, Harty MP, Khademian Z. Imaging of neuroblastoma and Wilms'tumor.Magn.Reson Imaging Clin N Am,2002,10:275-302.
    16.王家祥.肾神经母细胞瘤组织中trail, caspase-3的表达.郑州大学学报,2004;39(2):209-211.
    17.左连富,刘洪祥,郭建文等.流式细胞术检测石蜡包埋肿瘤细胞DNA含量的样品制备方法.中华病理学杂志,1988,17(3):232.
    18. Collin F, Chassevent A, Bonichon FC, et al.Flow cytonetric DNA content analysis of 185 soft tissue neoplasms indicate that S-phase fraction is a prognostic factor for sarcoma,Cancer,1997, 79 (12):2371.
    19. Chodyniciki S, Chyczewski L, Olazewska E. Immunihistochemical investigations of cathepsin D activity in the structures of cholesteatoma. Med Sci Monit,2002,8(5):BR184.
    20.王家祥.肾母细胞瘤组织中蛋白酶D,尿激酶型纤溶酶原激活剂及其受体的表达.郑州大学学报,2004,39(2):217-219.
    21.王家祥.肾神经母细胞瘤组织中Survivin的表达.郑州大学学报,2004,39(2):211-214.
    22. Montuori N, Mattiello A, Mancini A, et al. Urokinase-mediated posttranscriptional regulation of uroki nase-receptor expression in non small cell lung carcinoma.Int J Cancer,2003,105(3): 353.
    23.王家祥.肾母细胞瘤组织中β-连环素和MMP-7的表达.郑州大学学报,2004,39(2):214-216.
    24.邹高德,葛根,孙锡林.肾细胞癌bcl-2及Ki-67的免疫组化研究,江西医学院学报,2002,42(1):33.
    25. Ougolkov AV, Yamashita K, Mai M. et al. Oncogenic betacatenin and MMP-7 (mat-rilysin) cosegregate in late-stage clinical colon cancer.Gastroenterology,2002,122(l):60.
    26.王家祥.肾母细胞瘤组织中PTEN,NF-KB和Ki-67的表达.郑州大学学报,2004,39(2):219-222.
    27.王家祥.肾母细胞瘤组织中DNA倍体,SPF,增殖细胞指数的测定.郑州大学学报,2004,39(2):21.
    28.陈尧,李瑞祥,王若菡.人直肠癌血管内皮细胞NFκ Bp65表达的意义.华西医科大学学报,2001,32(2):196.
    29.Wright GL Jr. SELDI proteinchip MS:a platform for biomarker discovery and cancer diagnosis. Expert Rev Mol Diagn,2002,2:549-563.
    30.Weinberger SR, Boschetti E, Santambien P, et al. Surface-enhanced laser desorption-ionization retentate chromatography mass spectrometry (SELDI-TOF-MS):a new method for rapid development of process chromatography conditions. J ChromatogrB Analyt Technol Biomed Life Sci,2002,782:307-316.
    31.Vlahou A, Schorge JO, Gregory BW, et al. Diagnosis of ovarian cancer using decision tree classification of mass spectral data. J Biomed Biotechnol,2003,2003:308-314.
    32.Wagner M, Naik DN, Pothen A, et al. Computational protein biomarker prediction:a case study for prostate cancer. BMC Bioinformatics,2004,11:5-26.
    33.Vlahou A, Laronga C, Wilson L, et al. A novel approach toward development of a rapid blood test for breast cancer. Clin Breast Cancer,2003,4:203-209.
    34.Xiao XY, Tang Y, Wei XP, et al. A preliminary analysis of non-small cell lung cancer biomarkers in serum. Biomed Environ Sci,2003,16:140-148.
    35.Wang JX, Zhang B, Yu JK,et al. Using ANN and serum protein pattern models in liver cancer diagnosis. Zhonghua Yi Xue Za Zhi,2005,85:189-192.
    36.Wang JX, Wang L, Fan YZ,et al. Application of serum protein fingerprint model and support vector machine in diagnosis of thyroid cancer. Zhonghua Yi Xue Za Zhi,2006,86:979-982.
    37.Yu JK, Chen YD, Zheng S. An integrated approach to the detection of colorectal cancer utilizing proteomics and bioinformatics. World J Gastroenterol,2004,10:3127-3131.
    38.Mele C, Emst G, Schimmel B, et al. Different expression of calgizzarin(S100A11) in normal colonic epithelium, adenoma and colorectal carcinoma. International J of Oncology,2006,28(1):195-200.
    39.Yu Y, Chen S, Wang LS, et al. Prediction of pancreatic cancer by serum biomarkers using surface-enhanced laser desorption/ionization-based decision tree classification. Oncology, 2005,68:79-86.
    40.Mueller J, von Eggeling F, Driesch D, et al. ProteinChip technology reveals distinctive protein expression profiles in the urine of bladder cancer patients. Eur Urol,2005,47:885-893.
    41.Huang HL, Stasyk T, Morandell S,et al.Biomarker discovery in breast cancer serum using 2-D differential gel electrophoresis/MALDI-TOF/TOF and data validation by routine clinical assays.Electrophoresis.2006,27(8):1641-1650.
    42.Hoagland LF 4th, Campa MJ, Gottlin EB,et al.Haptoglobin and posttranslati-onal glycan-modified derivatives as serum biomarkers for the diagnosis of nonsmall cell lung cancer.Cancer.2007,110(10):2260-2268.
    43.Hyeran B,Lee SW. Application of support vector machines for pattern recogni-tion:a survey.In Lee SW, Verri A, eds. Pattern recognition with support vector machines.Heidelberg. Berlin; Springer verlag 2002,213-236.
    44.周康荣,主编.腹部CT.上海:上海医科大学出版社,1993:174.
    45.Ng YY, Hall-Craggs MA, Dicks-Mireaux C, et al. Wilms' tumour:pre-and post-chemotherapy CT appearances. Clin Radiol,1991,43:255-259.
    46.Lidongling, Lifuyue, Duyanshang, et al. The value of CT diagnosis for nephro-blastoma in children. Chinese Remedies & Clinics,2004,12(4),12:952.
    47.Wang JX, Zhang J, Liu QL,et al.Detecting biomarkers from serum in nephro-blastoma patients with support vector machine[J].Zhonghua Yi Xue Za Zhi,2006,86(42):2982-2985. Chinese.
    48.Miniati D, Gay AN, Parks KV,et al.Imaging accuracy and incidence of Wilms' and non-Wilms' renal tumors in children[J].J Pediatr Surg.2008,43(7):1301-1307.
    49.Lindor NM, McMaster ML, Lindor CJ,et al. Concise handbook of familial cancer susceptibility syndromes-second edition.J Natl Cancer Inst Monogr.2008,(38):3-93.
    50.Fan YZ, Li GH, Wang YH,et al. [Effects of genistein on colon cancer cells in vitro and in vivo and its mechanism of action.]. Zhonghua Zhong Liu Za Zhi.2010;32(1):4-9.
    51.Eppenberger M, Zlobec I, Baumhoer D,et al. Role of the VEGF ligand to receptor ratio in the progression of mismatch repair-proficient colorectal cancer. BMC Cancer.2010;10(1):93.
    52.Guo QS, Jia H, Han MY,et al. [Expression and significance of integrin alpha5, betal and E-CD in patients with non-small cell lung carcinoma]. Zhonghua Zhong Liu Za Zhi. 2006;28(10):746-9.
    53.Yoo NJ, Kim S, Lee SH. Mutational analysis of WTX gene in Wnt/beta-catenin pathway in gastric, colorectal, and hepatocellular carcinomas. Dig Dis Sci.2009;54(5):1011-4.
    54.Su MC, Huang WC, Lien HC. Beta-catenin expression and mutation in adult and pediatric Wilms' tumors. APMIS.2008;116(9):771-8.
    55.E1-Kares R, Hueber PA, Blumenkrantz M,et al. Wilms tumor arising in a child with X-linked nephrogenic diabetes insipidus. Pediatr Nephrol.2009;24(7):1313-9.
    56.Madore J, Ren F, Filali-Mouhim A,et al. Characterization of the molecular differences between ovarian endometrioid carcinoma and ovarian serous carcinoma. J Pathol.2010;220(3):392-400.
    57. Lidongling, Lifuyue, Duyanshang, et al. The value of CT diagnosis for nephro-blastoma in children. Chinese Remedies & Clinics,2004,12(4),12:952.
    58. Corbin M, de Reynies A, Rickman DS,et al. WNT/beta-catenin pathway activation in Wilms tumors:a unifying mechanism with multiple entries? Genes Chromosomes Cancer.2009;48(9):816-27.
    1.Arndt V, Lacour B, Steliarova-Foucher E.Up-to-date monitoring of childhood cancer long-term survival in Europe:tumours of the sympathetic nervous system, retinoblastoma, renal and bone tumours, and soft tissue sarcomas. Ann Oncol.2007,18(10):1722-1733.
    2.Ekenze SO, Agugua-Obianyo NE, Odetunde OA.The challenge of nephroblastoma in a developing country.Ann Oncol.2006,17(10):1598-1600.
    3.Patterson SD. Proteomics:evolution of the technology. Biotechniques,2003,35(3):440-444.
    4.Karas M, Hillenkamp F. Laser desorption ionization of proteins with molecular masses exceeding 10000 daltons. Anal Chem,1988,60(8):2299-2301.
    5. Cedazo-Minguez A, Winblad B. Biomarkers for Alzheimer's disease and other forms of dementia:clinical needs, limitations and future aspects. Exp Gerontol.2010; 45(1):5-14.
    6. Arnesen T, Van Damme P, Polevoda B,et al. Proteomics analyses reveal the evolutionary conservation and divergence of N-terminal acetyltransferases from yeast and humans. Proc Natl Acad Sci U S A.2009; 106(20):8157-62.
    7.Aebersold R, Mann M. Mass spectrometry-based proteommies.Nature,2003.422:198-207.
    8.Chien KL, Fang WH, Wen HC, et al. APOA1/C3/A5 haplotype and risk of hypertriglyceridemia in Taiwanese. Clin Chim Acta,2008,390:56-62.
    9.Blankenhorn DH, Alaupovic P, Wickham E, et al. Prediction of angiographic change in native human coronary arteries and aortocoronary bypass grafts;Lipid and nonlipidfactors[J]. Circulation,1990,81:470-476.
    10.Qi L, Liu S, Rifai N, et al. Associations of the apolipoprotein A1/C3/A4/A5 gene cluster with triglyceride and HDL cholesterol levels in women with type 2 diabetes. Atherosclerosis,2007,192:204-210.
    11.Chen J, Anderson M, Misek DE,et al.Characterization of apolipoprotein and apolipoprotein precursors in pancreatic cancer serum samples via two-dimensional liquid chromatography and mass spectrometry.J Chromatogr A.2007,1162(2):117-125.
    12.Huang HL, Stasyk T, Morandell S, et al. Biomarker discovery in breast cancer serum using 2-D differential gel electrophoresis/MALDI-TOF/TOF and data validation by routine clinical assays. Electrophoresis,2006,27(8):1641-1650.
    13.Qiu JG, Fan J, Liu YK,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(3):299-305.
    14.Tyblaerg-Hansen A,Nordestgaard BG,Gerde LU,et al.Humphfies. [J]. Athero-sclerosist 1993,100:157-169.
    15. Vergnes L, Baroukh N, Ostos MA,et al. Expression of human apolipoprotein A-I/C-Ⅲ/A-IV gene cluster in mice induces hyperlipidemia but reduces atherogenesis. Arterioscler Thromb Vasc Biol.2000;20(10):2267-74.
    16.Wassell J. Haptoglobin:function and polymorphism. Clin lab,2000,46(11-12):547-552.
    17.Nakano M, Nakagawa T, Ito T,et al.Site-specific analysis of N-glycans on haptoglobin in sera of patients with pancreatic cancer:a novel approach for the development of tumor markers.Int J Cancer.2008,122 (10):2301-2309.
    18. Hoagland LF 4th, Campa MJ, Gottlin EB,et al.Haptoglobin and posttran-slational glycan-modified derivatives as serum biomarkers for the diagnosis of nonsmall cell lung cancer.Cancer.2007,110(10):2260-2268.
    19. Fujimura T, Shinohara Y, Tissot B,et al.Glycosylation status of haptoglobin in sera of patients with prostate cancer vs. benign prostate disease or normal subjects.Int J Cancer. 2008,122(1):39-49.
    20. Zhao C, Annamalai L, Guo C,et al.Circulating haptoglobin is an independent prognostic factor in the sera of patients with epithelial ovarian cancer.Neoplasia.2007,9(1):1-7.
    1. Srinivas PR,Verma M,Zhao Y, et al. Proteomics for cancer biomarker discovery. Clin Chem,2002,48(8):1160-1169.
    2. Mahon P,Dupree P.Quantitative and reproducible two-dimensional gel analysis using Phoretix 2D Full. Electrophoresis 2001,22:2075-2085.
    3. Kim JH, Stevens RC, Maccoss MJ, et al. Identification and characterization of biomarkers of organophosphorus exposures in humans. Adv Exp Med Biol.2010;660:61-71.
    4. Anderson NL,Anderson NQProteome and proteomics:new technologies,new conepts,and new words.Electrophoresis.1998,19(11):1853-1861.
    5. O'Farrell PH. High resolution two-dimensional electrophoresis of proteins. J Biol Chem.1975; 250(10):4007-21.
    6. Alban A,David SO,Bjorkesten L,et al.A novel experimental design for comparative two-dimensional gel analysis:two-dimensional difference gel electrophoresis incorporating a pooled internal standard.Proteomics 2003;3(1):36-44.
    7. W u SL,Hancock W S,Goodrich GG,et al.An approach to the proteomic analysis of a breast cancer cell line (SKBR-3).Proteomics 2003;3(6):1037-1046.
    8. Miura K,Imaging and detection technologies for image analysis in electrophoresis. Electrophoreses.2001;22(5):801-13.
    9. Ferrer Amate C, Unterluggauer H, Fischer RJ,et al.Development and validation of a LC-MS/MS method for the simultaneous determination of aflatoxins, dyes and pesticides in spices. Anal Bioanal Chem.2010 Mar 12.
    10. Srinivas P.R,Srivastava S,Hanash S,et al.Proteomics in early detection of cancer. Clin.Chem,2001,47:1901-1911.
    11. Wright GL JR.SELDI proteinchip MS:aplatform for biomarker discover and cancer dignosis.Expert Rev mol diagn.2002,2(6):549-563.
    12. Gygi SP, Rist B, Gether SA,et al.Quantitative analysis of complex Protein Protein Mixing using isotope-coded affinity tage.Nat Biotechnol,1999,17(10):994-999.
    13.Beavis RC,Fenyo D.Database searching with mass Pectrometrometrie Information. A Trends Gulde,2000,7:22-27.
    14. Read TD,Salzberg SL,Pop M, et al. Comparative genome sequencing for discovery of novel polymorphisms in Bacillus anthracis[J].Science,2002,296 (5575):2028-2033.
    15. Sehaub S, wilkins J, welfer T,et al.Urine Protein Profiling with Surface-enhanced Laser-desorption/ioulzation time-of-flight mass spectrometry [J]. Kidney Int 2004,65 (1):323-332.
    16. Ho DW, Yang ZF, Wong BY,et al. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry serum protein profiling to identify nasopharyngeal carcinoma. Cancer. 2006,1;107(1):99-107.
    17. Roesch Ely M, Nees M, Karsai S,et al. Transcript and proteome analysis reveals reduced expression of calgranulins in head and neck squamous cell carcinoma. Eur J Cell Biol. 2005,84(2-3):431-44.
    18. Belbin TJ, Singh B, Barber I, et al. Molecullar classification of head and neck squamous cell carcinoma using cDNA microarry[J].Cancer Res,2002,62 (4):1184-1190.
    19. Deng G, Li D, Xiao Z,et al. Comparative proteome analysis of laser capture microdissection for purified primary tumor and lymph node metastatic tumor in human lung squamous carcinoma.Zhong Nan Da Xue Xue Bao Yi Xue Ban.2009; 34(12):1182-8.
    20. Gao WM, Kuick R, Orchekowski RP, et al. Distinctive serum protein profiles involving abundant proteins in lung cancer patients based upon antibody microarray analysis[J]. BMC Cancer,2005,5:110.
    21. Lee S, Kang J, Cho M,et al. Profiling of transcripts and proteins modulated by K-ras oncogene in the lung tissues of K-ras transgenic mice by omics approaches. Int J Oncol. 2009;34(1):161-72.
    22. Dai S, Wang X, Liu L, et al. Discovery and identification of Serum Amyloid A protein elevated in lung cancer serum. Sci China C Life Sci,2007,50(3):305.
    23. Han KQ, Huang G, Gao CF, Wang XL, Ma B, Sun LQ, Wei ZJ. Identification of lung cancer patients by serum protein profiling using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Am J Clin Oncol.2008 Apr;31(2):133-9.
    24. An JY, Fan ZM, Zhuang ZH,et al. Proteomic analysis of blood level of proteins before and after operation in patients with esophageal squamous cell carcinoma at high-incidence area in Henan Province. World J Gastroenterol.2004,15;10(22):3365-8.
    25. Li W, Liu BY; Zhang XQ, et al.Analysis of human gastric cancer by transcriptome and proteome profiling.Zhonghua Wei Chang Wai Ke Za Zhi.2009; 12 (1):52-6.
    26. Borgia B, Roesli C, Fugmann T,et al. A proteomic approach for the identification of vascular markers of liver metastasis. Cancer Res.2010;70(1):309-18.
    27. Roesli C, Borgia B, Schliemann C,et al. Comparative analysis of the membrane proteome of closely related metastatic and nonmetastatic tumor cells. Cancer Res.2009;69(13):5406-14.
    28. Hu L, Zhou H, Li Y,et al. Profiling of endogenous serum phosphorylated peptides by titanium (IV) immobilized mesoporous silica particles enrichment and MALDI-TOFMS detection. Anal Chem.2009;81(1):94-104.
    29. Distler U, Hulsewig M, Souady J,et al. Matching IR-MALDI-o-TOF mass spectrometry with the TLC overlay binding assay and its clinical application for tracing tumor-associated glycosphingolipids in hepatocellular and pancreatic cancer. Anal Chem.2008;80(6):1835-46.
    30. Ehmann M, Felix K, Hartmann D,et al. Identification of potential markers for the detection of pancreatic cancer through comparative serum protein expression profiling.Pancreas. 2007;34(2):205-14.
    31. Eck W, Craig G, Sigdel A,et al. PEGylated gold nanoparticles conjugated to monoclonal F19 antibodies as targeted labeling agents for human pancreatic carcinoma tissue. ACS Nano. 2008;2(11):2263-72.
    32. Zheng GX, Wang CX, Qu X, et al. Establishment of serum protein pattern for screening colorectal cancer using SELDI-TOF-MS. Exp Oncol,2006,28(4):282.
    33. Xu W H, Chen Y D, Hu Y, et al. Preoperatively molecular staging with CM10 ProteinChip and SELDI-TOF-MS for colorectal cancer patients. J Zhejiang Univ Sci B,2006, 7(3):235-240.
    34. Ward DG, Nyangoma S, Joy H,et al. Proteomic profiling of urine for the detection of colon cancer. Proteome Sci.200816;6:19.
    35. Xu G, Xiang CQ, Lu Y, et al. Application of SELDI-TOF-MS to identify serum biomarkers for renal cell carcinoma. Cancer Lett.2009;282(2):205-13.
    36. Holcakova J, Hernychova L, Bouchal P,et al. Identification of alphaB-crystallin, a biomarker of renal cell carcinoma by SELDI-TOF MS. Int J Biol Markers.2008;23(1):48-53.
    37. Rossi L, Martin BM, Hortin GL, et al. Inflammatory protein profile during systemic high dose interleukin-2 administration. Proteomics.2006;6(2):709-20.
    38. Vlahou A, Schellhammer PF, Medrinos S, et al. Development of a novel proteomic approach for the detection of transitional cell carcinoma of the bladder in urine[J]. Am J Pathol,2001;158(4):1491-1502.
    39. Adam BL, Vlahou A, Senmes OJ, el al. Proteomic approaches to biomarker discovery in prostate and bladder cancers. Pmteomics,2001;1(10):1264.
    40. Okamoto A, Yamamoto H, Imai A,et al. Protein profiling of post-prostatic massage urine specimens by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to discriminate between prostate cancer and benign lesions. Oncol Rep.2009;21(1):73-9.
    41. Lai Y, Adam BL, Podolsky R,et al. A mixture model approach to the tests of concordance and discordance between two large-scale experiments with two-sample groups. Bioinformatics. 2007;23(10):1243-50.
    42. Pan YZ, Xiao XY, Zhao D, et al. Application of surface-enhanced laser desorption/ionization time-of-flight-based serum proteomic array technique for the early diagnosis of prostate cancer. Asian J Androl,2006,8(1):45-51.
    43. Montazery-Kordy H, Miran-Baygi MH, Moradi MH. A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform. J Zhejiang Univ Sci B.2008; 9 (11):863-70.
    44. Ye B, Skates S, Mok SC, et al. Proteomic-based discovery and characterization of glycosylated eosinophil-derived neurotoxin and COOH-terminal osteopontin fragments for ovarian cancer in urine[J]. Clin Cancer Res,2006,12:432-441.
    45. Wu SP, Lin YW, Lai HC,et al. SELDI-TOF MS profiling of plasma proteins in ovarian cancer. Taiwan J Obstet Gynecol.2006;45(1):26-32.
    46. Zhang H, Kong B, Qu X, et al. Biomarker discovery for ovarian cancer using SELDI-TOF-MS. Gynecol Oncol,2006,102(1):61-66.
    47. Coombes KR, Tsavachidis S, Morris JS,et al.Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform. Proteomics. 2005;5(16):4107-17.
    48. Cazares LH, Diaz JI, Drake RR, Semmes OJ. MALDI/SELDI protein profiling ofserum for the identification of cancer biomarkers. Methods Mol Biol.2008;428:125-40.
    49. Sauler ERZh, u W, Fan XJ,et al. Proteomic analysis of nipple aspirate fluid to detect biologic markers of breast cancer. Br J Cancer,2002,86(9):1440.
    50. Vlahou A, Laronga C, Wilson L, et al. A novel approach toward development of a rapid blood test for breast cancer. Clin Breast Caneer,2003,4(3):203-209.
    51. Guo JH, Wang WJ, Liao P, Zhang CY, Jin DY, Lou WH, Zhang SC. Application of serum protein profiling in diagnosis, prognosis and evaluation of curative effect of pancreatic adenocarcinoma. Zhonghua Zhong Liu Za Zhi.2010;32(1):33-6.
    52. Vermeulen R, Lan Q, Zhang L, et al. Decreased levels of CXC-chemokines in serum of benzene-exposed workers identified by array-based proteomics. Proceedings of the National Academy of Sciences of the United States of America,.2005,102(47):17041-17046.
    1. Hartwig S, Ho J, Pandey P,et al. Genomic characterization of Wilms' tumor suppressor 1 targets in nephron progenitor cells during kidney development. Development.2010; 137 (7):1189-203.
    2.Shin DH, Lee JH, Kang HJ,et al. Novel epitheliomesenchymal biphasic stomach tumour(gastroblastoma) in a 9-year-old:morphological, usltrastructural and immuno-histochemical findings. J Clin Pathol.2010;63(3):270-4.
    3. Beckwith JB.Precursor of Wilms'tumor:clinical and biological implications.Med Pediatr Oncol,1993;21:158.
    4. Saatci C, Caglayan AO, Kocyigit I,et al. Expression of WT1 gene in multiple myeloma patients at diagnosis:is WT1 gene expression a useful marker in multiple myeloma? Hematology. 2010;15(1):39-42.
    5. Nowakowska-Kopera A, Sacha T, Florek I,et al.Wilms' tumor gene 1 expression analysis by real-time quantitative polymerase chain reaction for monitoring of minimal residual disease in acute leukemia. Leuk Lymphoma.2009;50(8):1326-32.
    6. Amel L,Leila BF,Lam ia K.H istologic and p rognostic study of nephro-blastoma in central Tunisia[J].Ann U rol (Paris),2003,37 (4):275-302.
    7. Barsela G,A rush MW.Reial metastasia detected by two-dimendi-onal echocardiography[J].Acta Oncol,2004,43(1):87-90.
    8. Gow KW,Roberts IF,Jam ieson DH,et al.Local staging of W ilm s'tumor-computerized tomography correlation with histological findings[J].Pediair Surg,2000,35(5):677-679.
    9. Li JH, Man YG Dual usages of single Wilms' tumor 1 immunohistochemistry in evaluation of breast tumors:a preliminary study of 30 cases. Cancer Biomark.2009; 5(3):109-16.
    10. Taran K, Kobos J, Sporny S. Examination of expression of WT1 gene product and CD44 adhesive molecule in nephroblastoma histologic types. Pol J Pathol.2008;59 (3):177-82.
    11. Dome Js,Cotton CA,Perlman EJ, et al.Treatment of anaplastic history Wilms' tumor;Results the fifth National Wilms'tumor Study. J Clin Oncol,2006,20; 24:2352-2358.
    12. Kang GH, Kim KM, Noh JH,et al. WT-1 expression in gastrointestinal stromal tumours. Pathology.2010;42(1):54-7.
    13. Wright KD, Green DM, Daw NC. Late effects of treatment for wilms tumor. Pediatr Hematol Oncol.2009;26(6):407-13.
    14. Al-Salam S, Hammad FT, Salman MA,et al. Expression of Wilms tumor-1 protein and CD 138 in malignant mesothelioma of the tunica vaginalis. Pathol Res Pract.2009;205(11):797-800.
    15. Lapillonne H, Llopis L, Auvrignon A, et al. Extensive mutational status of genes and clinical outcome in pediatric acute myeloid leukemia. Leukemia.2010;24(1):205-9.
    16. Canalis E, Smerdel-Ramoya A, Durant D,et al.Nephroblastoma overexpressed (Nov) inactivation sensitizes osteoblasts to bone morphogenetic protein-2, but nov is dispensable for skeletal homeostasis.Endocrinology.2010;151(1):221-33..
    17. Jones C, Pritchard-Jones K. MIB-1 and p27Kipl expression in nephroblastoma. Clin Cancer Res.2004; 15;10(22):7785-6.
    18. Sankhala KK, Chawla SP. Review:desmoplastic small round cell tumor:current treatment approach and role of targeted therapy. Clin Adv Hematol Oncol.2009;7(7):476-8.
    19. Routh JC, Ashley RA, Sebo TJ,et al. B7-H1 expression in Wilms tumor:correlation with tumor biology and disease recurrence.J Urol.2008; 179(5):1954-9.
    20. Subramaniam MM, Lazar N, Navarro S, et al. Expression of CCN3 protein in human Wilms' tumors:immunohistochemical detection of CCN3 variants using domain-specific antibodies. Virchows Arch.2008;452(1):33-9.
    21. Portugal R, Barroca H. Clear cell sarcoma, cellular mesoblastic nephroma and metanephric adenoma:cytological features and differential diagnosis with Wilms tumour. Cytopathology. 2008;19(2):80-5.
    22. Giordano G, Campanini N, Rocco A,et al. C-kit protein expression in Wilms' tumour:an immunohistochemical study. Eur J Surg Oncol.2009;35(6):629-35.
    23. Collins F S,Morgan M,Patrinos A.The Human Genome Project:lessons from large-scale biology[J].Science,2003,300(5617):286-290.
    24. Srinivas PR,Verma M,Zhao Y, et al. Proteomics for cancer biomarker discovery. Clin Chem,2002,48(8):1160-1169.
    25. Blackstock W,Mann M.A boundless future for proteomics[J].Trends Biotechnol,2001,19 (suppl):s1-s2.
    26. Hutchens TW,Yip T T.New desorption st rategies for t he mass spect ro met ric analysis of macro molecules.Rapid Commun Mass Spect rum,1993,7(7):576.
    27.吴西梅,朱杰民,朱炳辉.蛋白质组学研究方法进展及在卫生防疫中的应用前景[J].中国预防医学杂志,2006(2):159-152.
    28. DRYSDALE R A.Flybase:genes and gene models[J].Nucleic Acids Res Database Issue,2005, 33:390-395.
    29. CHEN N.Wormbase:a comprehensive data resource for Caenorhabditis biology and genomics[J].Nucleic Acids Res Database lssue,2005,33:383-389.
    30. KESELER I M.EcoCye:a comprehensive database resource for Escherichia coli[J].Nucleic Acids Res Database Issue,2005,33:334-337.
    31.Metzger M L,Do me J S1 Current t herapy for wilms t umor[J].Oncologist, 2005,10(10):815-826.
    32.叶惟靖,汤静燕,赵海腾,等.多学科协作模式对肾母细胞瘤治疗的影响[J].中华小儿外科杂志,2004,25(3):201-204.
    33. Graf N,Tournade M F,de Kraker J.The role of p reoperative chemot herapy in t he management of Wil m's t umor[J].Urol Clin North Am,2000,27 (3):443-454.
    34. Green D M.The t reat ment of stages Ⅰ-Ⅳfavorable histology Wilms't umor[J].J Clin Oncol,2004,22(8):1366-1372.
    35. Thomas P R,Tefft M,Co mpaan P J,et al.Result s of two radiation t herapy rand-omizations in the third national wilm's tumor study [J]. Cancer,1991,68 (8):1703-1707.
    36. Metzger ML,Dome JS.Current therapy for wilm s tumor [J]. Oncologist,2005, 10(10):815-826.
    37.Jenq RR, van den Brink MR. Allogeneic haematopoietic stem cell transplantation: individualized stem cell and immune therapy of cancer. Nat Rev Cancer.2010;10 (3):213-21.
    38. Fewkes NM, Krauss AC, Guimond M,et al.Pharmacologic modulation of niche accessibility via tyrosine kinase inhibition enhances marrow and thymic engraftment following hematopoietic stem cell transplantation. Blood.2010 Mar 15.
    39. Bons J A,de Boer D, et al.Standardi-zation of calibration and qualit y cont rol using surface enhanced desorption ionization-time of flight-mass spectrometry.Clinica Chimica Acta,2006,366:249-256
    40. Hong H,Dragan Y,Ep stein J,et al.Qualit y cont rol and quality assessment of data f rom surface-enhanced laser desorption/i-onization (SELDI)time2of flight(TO F)mass spect ro metry (MS).BMC Bioinformatics,2005,15:(6 Suppl 2):S5

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