胃癌发生发展不同阶段血清蛋白质指纹图谱及意义研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
我国胃癌发病率和死亡率占所有恶性肿瘤中的比率在农村为第一位,在城市为第二位,每年约有20万人死于胃癌,因此胃癌目前仍是我国严重的公共卫生问题和癌症防治研究工作的重点。长期以来我国一直是胃癌高发区,胃癌发病率和死亡率近年来在广大农村也未见明显下降趋势。迄今关于胃癌发病机制和早期诊断的研究仍没有重大突破,尚不能满足临床对胃癌进行有效控制的需要。胃癌患者不良预后主要是由于不能早期诊断,明确诊断时大多数已到进展期。另一方面,大多数得到手术治疗机会的患者在术后仍有相当数量的病人因重要脏器的转移而不可避免地导致死亡。目前依靠胃镜检查是发现早期胃癌的有效手段,但胃镜检查存在诸多不便,不适于作为筛查手段在大规模的人群中推广使用。寻找一种简单易行、灵敏度高、特异性强的早期胃癌诊断方法是当务之急。胃癌器官转移率国内报道为(64.2%),其中男性以肝转移最多(38.1%),女性则以卵巢转移多见(43.6%)。临床诊断肝转移的胃癌病例平均生存期仅为5.5个月;临床诊断卵巢转移的胃癌病例66%生存期仅为半年。因此,如何解决胃癌肝转移、卵巢转移早期诊断和防治的难题,已成为提高胃癌患者术后5年生存率所面对的最大挑战。
     表面增强激光解吸离子化-飞行时间质谱(SELDI-TOF)技术是新发展的蛋白质组学分析技术。根据定位于芯片表面物质的性质不同,可将所用芯片分为化学型芯片和生物型芯片。化学型芯片借芯片表面特定界质,通过疏水力、静电力、共价键等结合样品中的蛋白质。生物型芯片则是把生物活性分子(如抗体、受体、酶等)结合到芯片表面捕获相关蛋白质分子。近两年来,该技术在差异蛋白质组学、蛋白质分子相互作用、疾病诊断等方面得到了广泛应用,为差异蛋白质组学研究提供了强有力的技术平台。
     本研究采用SELDI-TOF蛋白质芯片技术,检测健康者、胃良性病患者、胃癌不同临床病理分期及不同分化程度患者血清蛋白指纹图谱,通过比交分析各组蛋白质指纹图谱差异,寻找出与胃癌发生、发展不同阶段相关的血清蛋白质指纹图谱,为进一步探讨胃癌发生发展的机理、筛选与不同生物学特征及不同临床病理分期相关的蛋白质分子标志打下基础。
     实验材料和方法
     (一)临床病例资料及血清标本收集
     1.临床病例资料:收集2004-2005年中国医科大学、沈阳军区总医院收治并经术后系统病理诊断证实的胃癌患者57例,男:女39:18;年龄42~84岁,平均年龄60±15.3岁;组织学分型及分化程度(34例):高分化腺癌7例;中分化腺癌10例;低分化腺癌11例;印戒细胞癌(SRC)6例。临床病理分期:早期胃癌19例,进展期38例;进展期胃癌转移情况:3例不伴转移,伴淋巴结转移35例;伴脏器转移12例(均同时有淋巴结转移):肝转移9例,卵巢转移2例,肾转移1例。将本组病例分为3组:早期胃癌组(19例)、不伴脏器转移之进展期胃癌组(25例)、伴脏器转移之进展期胃癌组(12例)。解放军306医院提供除胃以外的消化道癌肝转移患者9例,用于与本组胃癌肝转移做比较分析。收集沈阳军区总医院、中国医科大学附属第一医院经胃镜活检及病理确诊的胃良性病患者7例,其中不典型增生2例,慢性萎缩性胃炎2例,胃息肉1例,胃溃疡2例。解放军306医院提供健康者30例,其中男18例,女12例,年龄35~78岁,平均年龄49±18.2岁,来源于肝功、肾功、彩色超声等检查均正常且排除有其它系统性慢性疾病史的健康体检人群。
     2.血清标本收集:采集患者接收治疗前早晨空腹静脉血,静置凝结,于2500rpm离心5min,分装血清,冻存于-70℃冰箱中备用。
     (二)仪器、材料及蛋白质芯片检测方法
     1.仪器、芯片及主要试剂:表面增强激光解吸电离飞行时间质谱仪(SELDI-TOF,美国Ciphergen公司),化学修饰的WCX2芯片。所用尿素、乙腈、三氟乙酸、Tris-HCl pH9.0、CHAPS、DTT、NaAc、HPLC H_2O、Hepes等均购自Sigma公司。
     2.血清蛋白质质谱检测方法:血清样品前处理:取3μl血清加6μl U9处理液(pH9.0),充分混匀,冰浴振荡30min,加入108μl结合缓冲液(100mmol/LNaAc,pH4.0),立即混匀。上样及洗脱:将WCX2芯片装入bioprocessor中,每孔加入200μl结合缓冲液,室温振荡洗涤2次,5min/次,甩干。每孔分别加入100μl样品混合液,振荡孵育1h,甩去样品,用200μl洗脱缓冲液(100mmol/LNaAc,pH4.0)室温振荡洗涤2次,5min/次,甩干;再用HPLC H_2O洗涤1次,立即甩干。拆开bioprocessor,取出芯片,晾干后,每点加2次0.5μl SPA,晾干后上机检测。
     3.数据采集及统计学处理:采用蛋白飞行质谱仪(PBSⅡ-C型)对结合在WCX2芯片上的血清蛋白进行读取分析,设定最高检测分子量为50kD,优化范围为2kD-20kD,激光强度190,检测敏感度为8,将1kD以下的峰滤去。采用Ciphergen Proteinchip3.1版本的分析软件自动采集数据,用质谱峰下面积表示蛋白质丰度。采用Biomarker Wizard软件分析蛋白质指纹图谱,确定两组间蛋白峰值的差异。在对数据进行t检验时,P<0.05视为差异具有统计学意义。
     (三)早期胃癌树状分类模型建立及盲筛评估方法
     1.早期胃癌预警决策树分类器模型初步建立
     1.1参与训练、建模的病例资料:在本组病例中随机挑选30例胃癌患者(早期10例、进展期中不伴脏器转移14例、伴远隔脏器转移6例)及30例正常对照作为训练组参加建模。
     1.2早期胃癌预警决策树分类器模型建立方法与主要步骤:
     建模方法:采用Ciphergen公司Biomarker Patterns Software初步建立早期胃癌预警决策树分类器模型。建模中采用Gini分布索引方法形成CART决策树(分类和回归树),同时选用决策树袋法,以克服向前可变选择过程中(如决策树)的不稳定性,并提高总体分类性能。
     建模主要步骤:选择论文一中筛选的早期胃癌与正常对照组之间的49个差异蛋白质峰信号簇构成决策树(树状分类器),按49个质点的高维特征将其划分为小的矩形,并将每个矩形划分为“癌”、“非癌”。以30例胃癌、30例正常对照作为训练组,以其差异蛋白质波峰强度为基础进行学习训练,确定判断癌与非癌的蛋白质丰度阈值(界值)。按照49个差异蛋白质在训练组中癌与非癌鉴别过程中的权重打分并排序,按权重排序原则首先选用单一蛋白质,然后再试用多个蛋白质相互组合进行树状分类器模型构建。通过学习和自我训练,以能达到对训练组标本最佳分类的蛋白质或蛋白质组合作为初步构建的早期胃癌预警树状分类器模型。
     2.采用盲筛实验评估早期胃癌预警决策树分类器模型
     2.1参与盲筛实验的病例资料:本组病例中未参加建模的27例胃癌患者(早期胃癌9例、进展期中不伴脏器转移12例、伴远隔脏器转移6例)、7例良性患者及19例健康对照。
     2.2盲筛实验方法:采用Ciphergen公司蛋白质芯片质谱分析仪配置的Biomarker Patterns软件初步建立早期胃癌预警决策树分类器模型。分别用上述建立的树状分类模型对盲筛组标本蛋白质指纹图谱数据做出相应的“癌”与“非癌”的判定,判定结果与标本实际病理诊断相符为判别正确,否则为错误。
     2.3对初步建立的早期胃癌预警决策树分类器模型进行评估:根据盲筛实验结果,采用敏感性、特异性、阳性预测值、阴性预测值、总有效率等指标对模型分类效果进行评估。
     结果
     1.健康者、胃良性病变及早期胃癌患者血清蛋白质指纹图谱变化
     胃良性病变与健康者相比较其差异蛋白质达26种之多,但与早期胃癌患者间的差异蛋白质仅6种,早期胃癌与健康对照差异蛋白质数量达42种,其中27种蛋白质在早期胃癌中低表达,15种在早期胃癌中高表达。健康者、胃良性病变、早期胃癌不同阶段,8367、31851 M/Z两种差异蛋白在血清中的含量呈进行性增高,而4799、6196 M/Z等蛋白质则呈进行性降低。早期胃癌组血清中6196、6366 M/Z蛋白质表达较胃粘膜良性病变组明显降低。
     2.不同分化程度胃癌患者血清蛋白质指纹图谱变化
     发现高、低分化胃癌血清蛋白质谱有不同特征,其中M/Z为2091、2197、2281等蛋白在低分化胃癌组呈高表达,而M/Z为34927蛋白在高分化胃癌组呈高表达。
     3.不同临床病理分期胃癌患者血清蛋白质指纹图谱变化
     进展期与早期胃癌组之间检出19种显著差异表达蛋白质;伴脏器转移组与早期胃癌组之间检出13种显著差异表达蛋白质;进展期胃癌中,伴脏器转移组与不伴脏器转移组之间检出7种显著差异表达蛋白质。初步分析表明,5种差异蛋白质在胃癌发生发展的不同阶段呈现明显的趋势性变化,其中M/Z为4078、3895、4289的三种蛋白质呈逐渐下降趋势,11687、36997两种蛋白质则呈现表达升高的趋势。
     4.胃癌肝转移与其它消化道癌肝转移蛋白质指纹图谱变化
     在9例胃癌肝转移与9例其它消化道癌肝转移患者的血清差异蛋白质指纹图谱比对分析中,共获得16种差异蛋白质峰谱,其中9种差异蛋白质在胃癌肝转移组显著高于其它消化道癌肝转移组,分别是N/Z为2923、2024、10260、3887、3102、9288、2637、2952、8149的蛋白质;另外7种M/Z为4329、5836、5085、4887、5067、6196、5705的蛋白质则显著低于非胃消化道癌肝转移组。更有意义的是,与非胃消化道癌肝转移组相比,胃癌肝转移组显著差异表达的4887、6196、10260、9288 M/Z四种蛋白质与正常对照组比较也有显著性差异
     5.早期胃癌决策树分类器模型构建与盲筛验证
     本实验共建立3个早期胃癌决策树分类器模型。
     模型Ⅰ由6196 M/Z单一蛋白质构成,用模型Ⅰ对未参加建模的27例胃癌、7例胃良性病患者及19例健康者进行识别试验(盲筛)。盲筛结果:27例胃癌诊断正确率为96.3%(26/27),仅有1例被误判为非癌;19例健康者诊断正确率为89.5%(17/19),仅有2例被误判为胃癌;7例胃良性病变诊断正确率为42.9%(3/7),有4例被误判为胃癌。模型Ⅰ对盲筛组胃癌与非胃癌的鉴别试验,其敏感性为96.3%,特异性76.9%,阴性预测值95.2%,阳性预测值81.3%,总有效率86.8%。
     模型Ⅱ主要由6366 M/Z蛋白质构成,用模型Ⅱ对未参加建模的27例胃癌、7例胃良性病患者及19例健康者进行识别试验(盲筛),结果27例胃癌诊断正确率为96.3%(26/27),仅有1例被误判为非癌;19例健康者诊断正确率为100%(19/19);7例胃良性病变诊断正确率为42.9%(3/7),有4例被误判为胃癌。模型Ⅱ用于盲筛组胃癌与胃良性病、健康者鉴别的敏感性为96.3%,特异性84.6%,阴性预测值95.7%,阳性预测值86.7%,总有效率90.6%。
     模型Ⅲ主要由M/Z为13738,8931,2048三种差异蛋白质构成。用模型Ⅲ对盲筛组患者盲筛,结果27例胃癌诊断正确率为96.3%(26/27),仅有1例被误判为非癌;19例健康者诊断正确率为94.7%(18/19),有1例被误判为胃癌;7例胃良性病变诊断正确率为14.3%(1/7),有6例被筛为胃癌。模型Ⅲ对盲筛组胃癌鉴别的敏感性为96.3%,特异性73.1%,阴性预测值94.7%,阳性预测值78.8%,总有效率84.9%。
     在上述盲筛试验中,9例早期胃癌患者均被准确识别鉴定。
     结论
     1.与健康对照组相比较,早期胃癌组血清蛋白质指纹图谱有显著差异,提示血清蛋白质指纹图谱对早期胃癌预警可能有潜在的应用前景。本研究初步筛出6366、6196M/Z等几种在早期胃癌患者血清中低表达的蛋白质,目前尚未见文献报道,有可能成为新的早期胃癌预警分子。
     2.不同分化程度的胃癌具有不同的血清蛋白质指纹图谱,其中低分化胃癌组血清蛋白质指纹图谱与其它分化程度胃癌组相比差异显著,提示低分化胃癌涉及的基因及蛋白表达异常可能更为复杂。
     3.本实验共获得16种胃癌肝转移与其它消化道癌肝转移血清差异蛋白,其中6196、4887、10260、9288M/Z等可能有望用于胃癌肝转移与其它消化道癌肝转移之间鉴别的指标。4.本实验建立了3个有助于早期胃癌预警的决策树分类器模型,最有效的模型Ⅱ主要由6366 M/Z蛋白质构成,对盲筛组的盲筛实验结果为:敏感性96.3%,特异性84.6%,阴性预测值95.7%,阳性预测值86.7%,总有效率90.6%。盲筛组9例早期胃癌盲筛结果符合率100%。其潜在的应用价值尚需进一步深入研究。但本实验结果也显示上述模型在胃癌与胃良性病变鉴别上有一定局限性。
Significance of serum protein fingerprints of different phases during the carcinogenesis and progression of gastric carcinoma
     Introduction
     Gastric cancer is one of the most common malignancy in China,The inci-dence is about 20/100 thousand and mortality is the first in the county and sec-ond in the city.The main reason of poor prognosis of gastric carcinoma is that gastric cancer can't be diagnosed in the early stage and most cases diagnosed in clinic are advanced gastric carcinoma with distant metastasis.At present,it is a effective way to discover gastric cancer by gastroscopy,but it also has deficit for spread examination.So it is important to find a simple,sensitive and specific method to diagnose early gastric cancer.As reported in domestic,the metastasis rate of gastric cancer is 64.2%,among these cases,the most in male are liver metastasis and ovary metastasis in female.The average survival time of these pa-tients are 5.5 months and 6 months,respectively.How to find a effective meth-od to early diagnose for liver and ovary metastasis from gastric cancer has be-come a big challenge to enhance the patients'five-year-survival rate.
     Very recently,a new technique,surface-enhanced laser desorption ioni-zation/time-of-flight mass spectrometry(SELDI/TOF-MS),has been de-veloped,whereby small amounts of proteins are bound to a biochip carrying spots with different types of chromatographic material,i.e,hydrophobic,hydro-philic,cation-exchanging,and anion-exchanging characteristics.SELDI/ TOF-MS can be apphed to analyze differential peptide and protein expression patterns in cancer and noncancer patients in various biological fluids.
     Objective
     To investigate the identification of serum protein fingerprints between the healthy,patients with gastric cancer in different clinico-pathological stages and histological differentiation using SELDI/TOF-MS and Protein-Chip technolo-gy,and find out the specific serum protein biomarkers related to the carcinogen-esis and progression of gastric carcinoma.
     Materials and Methods
     1.Clinical Materials
     Fifteen patients with gastric cancer were involved in this study,male;fe-male 39;18,ages 42-84,average age 60 15.3.Clinico-pathological staging; ealy 19 cases,advanced 38,among the advanced group,3 without any metasta-sis;11 cases with distant metastasis;9 metastasizing to liver;1 to the ovary;1 to the kidney.There were 7 cases with benign lesion of gastric mucosa diagnosed through gastroscope.Thirty cases of the healthy from the normal people perform-ing health examination,male;female 18;12,age 35-78,average age 49 18.2.
     Each sample of limosis blood was collected from patients before accepting any treatments,centrifuged for 5mins in 2500 rpm after congealed.The sera were stored in refrigerator at-70℃for later use.
     2.Detection of the serum protein fingerprints
     In present study,Surface-enhanced laser desorption ionization/time-of -flight mass spectrometry(SELDI/TOF-MS,Ciphergen Com,USA)and protein-chip(WCX2)were used to detect the serum protein fingerprints.
     Time-of-flight spectra were generated by laser shots collected in the pos-itive mode of WCX2.The laser intensity was set to 190,detector sensitivity to 8,and 60 laser shots per average spectrum were performed.A mixture of mass standard calibrant proteins was used for calibration of mass accuracy.Peak de-tection was performed using the Ciphergen Protein-Chip Software 3.0.Spectra ranging from 1000 to 50 000 m/z were selected for the analysis.Smaller masses were not used,since artifacts with EAMS and other contaminants could not be excluded.The spectra were normalized according to the intensity of total ion cur-rent.Automatic peak detection was performed in the range of 1000 to 20000 M/ Z,following baseline subtraction.
     3.Statistical analysis
     Statistical analysis was automatically performed using Biomarker Wizar soft-ware.P value less than 0.05 was considered as significant.
     4.The construction of decision-tree classification for screening gastric cancer
     Thirty patients with gastric cancer(10 in early stage,14 advanced,6 with distant metastasis)and 30 normal persons were included to construct the deci-sion-tree classifier model.The specificity of model was tested with the blind set including 27 gastric cancer,7 gastric benign lesion and 19 healthy cases.
     Results
     1.The changes.of serum protein fingerprints among the healthy,patients with gastric benign lesion and gastric caner
     There were 26 different proteins between gastric benign lesion patients and the healthy,while only 6 different proteins between gastric benign lesion and early gastric cancer.Between early gastric cancer and the healthy,there were 42 different proteins,among them 27 proteins lowly expressed and the others highly expressed in early gastric carcinoma.Proteins 8367M/Z and 31851M/Z expressed higher in early gastric cancer than in gastric benign lesion group, while proteins 4799M/Z and 6196M/Z lower in early gastric cancer than in gas-tric benign lesion group.
     2.The changes of serum protein fingerprints among the groups of different histological differentiation of gastric cancers
     There were 10 proteins,which were 2091M/Z,2197M/Z,2281M/Z, 2298M/Z,2310M/Z,2520M/Z,3105M/Z,4162M/Z,4479M/Z,8147M/Z., highly expressed in poorly differentiated gastric cancer,while in well differentia-ted gastric cancer only one protein 34927M/Z highly expressed.
     3.The changes of serum protein fingerprints among the groups of gastric cancer in different clinico-pathological stages.
     The expression of five different proteins showed a different changing tenden-cy with the progressing of gastric cancer,proteins 4078 M/Z,3895 M/Z and 4289M/Z expressed gradually lower,while proteins 11687M/Z and 36997M/Z gradually higher with progression of the cancers.
     4.The changes of serum protein fingerprints btween liver metastases from gastric cancer and from other digestive tract cancer
     Between the liver metastases derived from gastric cancers and from other di-gestive tract cancers,16 different proteins were obtained.Nine proteins 2923M/ Z,2024M/Z,10260M/Z,3887M/Z,3102M/Z,9288M/Z,2637M/Z, 2952M/Z,8149M/Z expressed higher in liver metastasis from gastric cancer than from the other digestive tract cancers;Proteins 4329M/Z,5836M/Z, 5085M/Z,4887M/Z,5067M/Z,6196M/Z,5705M/Z expressed lower in liver metastasis from gastric cancer than from the other digestive tract cancers.Inter-estingly,expression of proteins 4887M/Z,6196M/Z,10260M/Z,9288M/Z were significantly different between gastric cancer and the healthy cases.
     5 The construction and evaluation of decision-tree classifier for screening early gastric cancer
     Three decision tree classifier models had been constructed by our study.
     The modelⅠwas consisted of 6196M/Z protein.The results of blind screen test for modelⅠshowed that 26(96.3%)patients with gastric cancers were cor-rectly classified,only one was falsely recognized as non-cancer.Seventeen (89.5%)healthy caseses were classified correctly,only two were recognized as cancer falsely.Three(42.9%)patients with gastric benign lesion were correct-ly classified.The overall sensitivity of modelⅠwas 96.3%,specificity 76.9%, negative predicting value 95.2%,positive predicting value 81.3%,ffective rate 86.8%.
     The modelⅡwas consisted of 6366M/Z protein.The results of blind screen test for modelⅡshowed that 26(96.3%)patients with gastric cancers were correctly classified,only one was falsely recognized as non-cancer.All 19(100%)healthy cases were correctly classified.Three(42.9%)patients with gastric benign lesion correctly classified.The overall sensitivity of the mod-elⅡwas 96.3%,specificity 84.6%,negative predicting value 95.7%,posi-tive predicting value 86.7%,total effective rate 90.6%.
     The modelⅢwas consisted of 3 different proteins,13738M/Z,8931 M/Z and 2048M/Z.The results of blind screen test for modelⅢⅠshowed that 26 (96.3%)patients with gastric cancers were correctly classified,only one was falsely recognized as non-cancer.Eighteen(94.7%)healthy cases were clas-sifted correctly,only one was recognized as cancer falsely.One(14.3%)pa-tients with gastric benign lesion was correctly classified.The overall sensitivity of modelⅢwas 96.3%,specificity 73.1%,negative predicting value 94.7%, positive predicting value 78.8%,total effective rate 84.9%.
     Nine patients with early stage gastric cancer of the blind set were accurately recognized as cancer by these three decision tree classifier models.
     Conclusions
     1.Compared with the healthy,the serum protein fingerprint of early gastric cancer was significantly different.This finding suggested that the serum protein fingerprinting has potential value for screening patients at high risk for gastric cancer.The proteins 6366 M/Z and 6196M/Z first found in this study have not been presents in the relevant literatures so far,and may be the new biomarkers for predicting and the early diagnosis of gastric cancer.
     2.There were different serum protein fingerprints between different grades of histological differentiation of gastric cancers.That poorly diffrentiated adeno-carcinoma possessed most distinct protein fingerprint pattern suggests that the abnormality of genes and proteins related to poorly diffrentiated cancer may be much more complicated than usually understood.
     3.Between the liver metastases derived from gastric cancers and from other digestive tract cancers,16 different proteins were obtained.The proteins 6196 M/Z,4887 M/Z,10260 M/Z and 9288M/Z are more likely to be the new bio-markers identifying the liver metastasis derived from gastric cancer.
     4.Among 3 decision-tree classification models constructed in the study, the most efficient one(modelⅡ)was consist of single protein 6366M/Z.the re-sult of test with blind set display that its sensitivity is 96.3%,specificity 84. 6%,negative predicting value 95.7%,positive predicting value 86.7%,total effective rate 90.6%.Further study is needed to explore the potential value of clinical application in predicting occurrence of gastric cancer.However,there is still some extent limitation for thses three decision-tree classification models to identifying the gastric cancer from gastric benign lesion.
引文
1.Hofler H,Becker KF.Molecular mechanisms of carcinogenesis in gastric cancer.Recent Results Cancer Res,2003,162:65-72.
    2.Richard J S,Donna S D.Cancer proteomics:from signaling networks to tumor markers.Trends in Biotechnology,2001,19(10):40-45.
    3.Juri R,Matthias M.What does it mean to identify a protein in proteomics ? Trends in Biochemical Sciences,2002,27(2):74-79.
    4.Terence C W,Philip J J.Proteome analysis and its impact on the discovery of serological tumor markers.Clinica Chimica Acta,2001,313:213-218.
    5.Streckfus CF,Bigler LR,Zwick M,et al.The use of surface -enhanced la-ser desorption/ionization time-of-flight mass spectrometry to detect puta-tive breast cancer markers in saliva:a feasibility study.J Oral Pathol Med,2006,35(5):292-300.
    6.Kozak KR,Su F,Whitelegge JP,et al.Characterization of serum biomark-ers for detection of early stage ovarian cancer.Proteomics,2005,5(17):4589-96.
    7.Junker K,yon Eggeling F,Muller J,et al.Identification of biomarkers and therapeutic targets for renal cell cancer using ProteinChip technology.Uro-loge A,2006,45(3):305-315.
    8.Langbein S,Lehmann J,Harder,et al.A Protein profiling of bladder canc-er using the 2D-PAGE and SELDI-TOF-MS technique,Technol Cancer Res Treat.2006,5(1):67-72.
    9.Bons JA,Wodzig WK,van Dieijen-Visser MP,et al.Protein profiling as a diagnostic tool in clinical chemistry:a review.Clin Chem Lab Med,2005,43(12):1281-1290.
    10.曾益新,主编.肿瘤学.广州,人民卫生出版社,1999,p80-99.
    11.Xie HL,Su Q,He XS,et al.Expression of p21(WAF1)and p53 and po1-ymorphism of p21(WAF1)gene in gastric carcinoma.World J Gastroen-terol,2004,10(8):1125-1131.
    12.Lauwers G Y,Scott Gv,Karpeh MS,et al.Immunohistoehenieal evaluation of bcl-2 protein expression in gastric adenocarcinomas.Cancer,1995,75:2209-2213.
    13.Tahara E.Genetic pathways of two types of gastric cancer.IARC Sei Publ,2004,15(7):327-349.
    14.Yoshida Y,Ytoh F,Endo T,et al.Decreased DCC mRNA expression in human gastric cancers is clinicopathologiclly significant.Int J Cancer,1998,79:634-639.
    15.Yoshida K,Toge T,Kuniyasu H Molecular mechanisms of carcinogenesis in human stomach cancer:K-sam gene.Nippon Rinsho,2001,59(14):53-59.
    16.Matthias P.A.Ebert,Jolrn Meuer,et al.Identification of Gastric Cancer Patients by Serum Protein Profiling.Journal of Proteo Res,2004,3:1261-1266.
    17.粱勇,潘春琴,杨林军,等.血清蛋白质指纹图谱模型在胃腺癌诊断中的应用.中华检验医学杂志,2004,27(9):576-578.
    18.Wen,Q S,Zhang,G Z,Kong,X T,et al.Modulation effect of cimetidine on the production of IL-2 and interferon-gamma in patients with gastric cancer.Zhonghua Zhong Liu Za Zhi,1994,16(4):299-301.
    19.Forones N M,Mandowsky S V,Lourenco L G,et al.Serum levels of inter-leukin-2 and tumor necrosis factor-alpha correlate to tumor progression in gastric cancer.Hepatogastroenterology,2001,48(40):1199-1201.
    20.Maeta M,Saito H,Katano K,et al.A progressive postoperative increase in the serum level of soluble receptors for interleukin-2 is an inclicator of a poor prognosis in patients with gastric cancer.Int J Mol Med,1998,1(1):113-116.
    1.Hofler H,Becker KF.Molecular mechanisms of carcinogenesis in gastric cancer.Recent Results Cancer Res,2003,162:65-72.
    2.Wiesner,A.Detection of Tumor Markers with ProteinChip? Technology.Current Pharmaceutical Biotechnology.2004,5:45-67.
    3.全国胃癌病理协作组.360例胃癌尸检材料病理学研究.中华病理学杂志,1983,12(2):124-128.
    4.Wang Y,Hanley R,Klemke RL.Computational methods for comparison of large genomic and proteomic datasets reveal protein markers of metastatic cancer.J Proteome Res,2006,5(4):907-915.
    5.Canelle L,Bousquet J,Pionneau C,et al.A proteomic approach to investi-gate potential biomarkers directed against membrane-associated breast cancer proteins.Electrophoresis,2006,27(8):1609-1616.
    6.Calvo A,Gonzalez-Moreno O,Yoon CY,et al.Prostate cancer and the genomic revolution:Advances using microarray analyses.Mutat Res,2005,25,576(1-2):66-79.
    7.Alrawi SJ,Schiff M,Carroll RE,et al.Aberrant crypt foci.Anticancer Res,2006,26(1A):107-119.
    8.Tomlinson AJ,Hincapie M,Morris GE,et al.Global proteome analysis of a human gastric carcinoma.Electrophoresis,2002,23(18):3233-4320.
    9.Riccardo A,Timothy C,Lance A.The cellular microenvironment.Sci of Clin Oncology,2004,3:1-12.
    10.Smyth MJ,Godferey DI,Trapani JA.A fresh look at tumor immunosurveil-lance and immunotherapy.Nature review Immunology,2001,1:41-49.
    11.Weiss L.Metastasis of cancer:a conceptual history from antiquity to the 1990s.Cancer Metastasis Rev,2000,19:193-200.
    12.Fidler I J.The organ microenvironment and cancer metastasis.Differentia-tion,2002,70:498 505.
    13.Simone N L.Laser capture microdissection:opening the microscopic fron-tier to molecular analysis.Trends Genet,1998,14:272-276.
    14. Fidler I J. Critical factors in the biology of humancancer metastasis; twenty - eighth G. H. A. Clowesmemorial award lecture. Cancer Res, 1990, 50: 6130-6138.
    
    15. Fidler I J, Talmadge J E. Evidence that intravenously derived murine pulmonary melanoma metastases can originate from the expansion of a single tumour cell. Cancer Res, 1986,46:5167 - 5171.
    1.Fujiwara K,Fujimoto N,Tabata M,et al.Identification of epigenetic aber-rant promoter methylation in serum DNA is useful for early detection of lung cancer.Clin Cancer Res,2005,11:1219-1225.
    2.Ichikawa D,Koike H,Ikoma H,et al.Detection of aberrant methylation as a tumor marker in serum of patients with gastric cancer.Anticancer Res,2004,24:2477-2481.
    3.Wang JY,Hsieh JS,Chen CC,et al.Alterations of APC,c-met,and p53 genes in tumor tissue and serum of patients with gastric cancers.J Surg Res,2004,120:242-248.
    4.郭成业,闫春,于文军,等.联合检测血清CA72-4、CA19-9和CEA对胃癌临床价值的探讨.中国肿瘤临床,2000,27(10):776-777.
    5.Wiesner,A.Detection of Tumor Markers with ProteinChip? Technology.Current Pharmaceutical Biotechnology,2004,5:45-67.
    6.Freire T,DAlayer J,Bay S.Efficient monitoring of enzymatic conjugation reaction by surface-enhanced laser desorption/ionization time of flight mass spectrometry for process optimization ioconjug Chem,2006,17(2):559-554.
    7.Moore LE,feiffer R,Warner,et al.Identifieation of biomarkers of arsenic exposure and metabolism in urine using SELDI technology.J Bioehem Mol Toxicol,2005,19(3):176-182.
    8.Ward DG,Cheng Y,Nkontehou G,et al.Changes in the serum proteome associated with the development of hepatoeellular carcinoma in hepatitis C -related cirrhosis.Br J Cancer,2006,30;94(2):287-292.
    9.Ilyin SE,Horowitz D,Belkowski SM,et al.Integrated expressional analy-sis:application to the drug discovery process.Methods,2005,37(3):280-288.
    10.ProteinChip? Applications Guide.seldi PUB-0073,2000,1:p58.
    11.Petrieoin E,Ardekani A M,Hitt B A,et al.Use of proteomie patterns in serum to identify ovarian cancer.The Lancet,2002,359:572-575.
    12.Gourin CG,Xia ZS,Hart Y,Serum protein profile analysis in patients with head and neck.squamous cell carcinoma.Arch Otolaryngol Head Neck Surg,2006,132(4):390-397.
    13.Suriano R,Lin Y,Ashok BT,Pilot study using SELDI-TOF-MS based proteomic profile for the identification of diagnostic biomarkers of thyroid proliferative diseases.J Proteome Res,2006,5(4):856-861.
    14.Engwegen JY,Gast MC,Schellens JH,Clinical proteomics:searching for better tumour markers with SELDI-TOF mass spectrometry.Trends Phar-macol Sci,2006,3:356-371.
    15.Ernst G,Melle C,Schimmel B,et al.Proteohistography-direct analysis of tissue with high sensitivity and high spatial resolution using ProteinChip technology.J Histochem Cytochem,2006,54(1):13-17.
    16.Melle C,Ernst G,Schimmel B,et al.Characterization of pepsinogen C as a potential biomarker for gastric cancer using a histo-proteomic approach.J Proteome Res,2005,4(5):1799-1804.
    17.Das S,Sierra JC,Soman KV,Differential protein expression profiles of gas-tric epithelial cells following Helicobacter pylori infection using Protein-Chips.J Proteome Res,2005,4(3):920-930.
    18.Matthias P.A.Ebert,Jolrn Meuer,et al.Identification of Gastric Cancer Patients by Serum Protein Profiling.Journal of Proteo Res,2004,3:1261-1266.
    19.粱勇,潘春琴,杨林军,等.血清蛋白质指纹图谱模型在胃腺癌诊断中的应用.中华检验医学杂志.2004.27(9):576-578.
    20.Hong-Gang Qian,Jing-Shen,Hong Ma,et al.Preliminary study on pro-teomics of gastric carcinoma and its clinical significance.World J Gastroen-terol,2005,11(40):6249-6258.
    1.Paget S.The distribution of Secondary growths in cancer of the breast.Lan-cet1,571-573(1889).
    2.Ewing J.Neoplastic Diseases edn 6(W.B.Saunders,Philadelphia,1928).
    3.Sugarbaker E V.Cancer.metastasis:a product of tumour host interactions.Curr.Probl.Cancer,1979,3:1 59.
    4.Weiss L.Metastasis of cancer:a conceptual history from antiquity to the 1990s.Cancer Metastasis Rev,2000,19:193 200.
    5.Fidler I J.The organ microenvironment and cancer metastasis.Differentia-tion,2002,70:498 505.
    6.Simone N L.Laser capture microdissection:opening the microscopic frontier to molecular analysis.Trends Genet,1998,14:272 275.
    7.Fidler I J.Critical factors in the biology of humancancer metastasis:twenty -eighth G.H.A.Clowesmemorial award lecture.Cancer Res,1990,50:6130 6138.
    8.Fidler I J,Talmadge J E.Evidence that intravenously derived murine pul-monary melanoma metastases can originate from the expansion of a single tumour cell.Cancer Res,1985,46:5157 5171.
    9.Pasqualini R,Ruoslahti E.Organ targeting in vivo using phage display pep-tide libraries.Nature,1996,380:364-365.
    10.Uehara H.Effects of blocking platelet-derived growth factor receptor signa-ling in a mouse model of prostate cancer bone metastasis.J Natl Cancer Inst,2003,95:558-570.
    11.秦荣,张巧玉,陈伟,等.裸鼠高转移性胃癌细胞亚系的筛选及其肝细胞生长因子受体表达.第三军医大学学报,2003:25(11):952-955.
    12.Schackert G,Fidler I J.Site-specific metastasis of mouse melanomas and a fibrosarcoma in the brain or the meninges,of syngeneic animals.Cancer Res,1988,48:3478-3484.
    13.Goodison S,Kawai K,Hihara J,Jiang P,et al.Prolonged dormancy and site-specific growth potential of cancer cells spontaneously disseminated from nonmetastatic breast tumors as revealed by labeling with green fluores-cent protein.Clin Cancer Res,2003,9(10 Pt 1):3808-3814.
    14.Glinskii OV,Huxley VH,Turk JR,et al.Continuous real time ex vivo epif-luorescent video microscopy for the study of metastatic cancer cell interac-tions with microvascular endothelium.Clin Exp Metastasis,2003,20(5):451-458.
    15.Choudhury A,Moniaux N,Ulrich AB,et al.MUC4 mucin expression in human pancreatic tumours is affected by organ environment:the possible role of TGFbeta2.Br J Cancer,2004,90(3):657-664.
    16.Ji XN,Ye SL,Li Y,et al.Tian B,Contributions of lung tissue extracts to invasion and migration of human hepatoeellular carcinoma cells with various metastatic potentials.J Cancer Res Clin Oncol,2003,129(10):556-564.
    17.Vantyghem SA,Postenka CO,Chambers AF.Estrous cycle influences organ -specific metastasis of B16F10 melanoma cells.Cancer Res,2003,63(16):4763-4765.
    18.Muller A,Homey B,Soto H,Ge N,et al.Involvement of chemokine recep-tors in breast cancer metastasis.Nature,2001,410(6824):50-56.
    19.杨琳,陈颖,李锦毅,等.人胃肠道癌肝脏和卵巢及子宫颈转移的器官特异性分子病理学机制比较研究.中国肿瘤临床,2003,30(4):229-233.
    20.陈绍维,廖湘凌,潘剑,等.nm23-H1基因对口腔癌Ace-M细胞株的转移能力及化疗敏感性影响的体外研究.四川大学学报(医学版),2003,34(4):628-630.
    21.李静,陈刚,高庆蕾,等.重组腺相关病毒介导的nm23H1对转移性卵巢癌原位移植模型的逆转研究.中华医学杂志,2003,83(19):1671-1673.
    22.Staller P,Sulitkova J,Lisztwan J,et al.Chemokine receptor CXCR4 down-regulated by von Hippel-Lindau tumour suppressor pVHL.Nature,2003,4,25(6955):307-311.
    23.Lee H,Lin EC,Liu L,et al.Gene expression profiling of tumor xenografts:In vivo analysis of organ-specific metastasis.Int J Cancer,2003,107(4):528-534.
    24.Copeland S,Siddiqui J,Remick D.Direct comparison of traditional ELISAs and membrane protein arrays for detection and quantification of human cyto-kines.J Immunol Methods,2004,284(1-2):99-106.
    25.Rimner A,Wischhussen J,Naumann U,et al.Identification by suppression subtractive hybridization of p21 as a radioinduciblegen in human glioma-cells.Anticancer Res,2001,21(5):3505-3508.
    26.Wadsworth JT,Somers KD,Stack B C,et al.Identification of Patients With Head and Neck Cancer Using Serum Protein Profiles.Arch Otola-Head Neck Surg,2004,130(1):98-101.
    27.Ryu JW,Kim H J,Lee YS.The proteomics approach to find biomarkers in gastric cancer.J Korean Med Sci,2003,18(4):505-509.
    28.辛彦,李晓玲,王艳萍,等.胃癌细胞功能分化表型与侵袭转移的关系。中华肿瘤杂志,2001,23(4):320-323.
    29.Nakatani Y,Kiamura H,Inayama Y.Pulmonaryadenocar cinoma of the fetal lung type:a clinicopathologic study indicating difference in histology,epide-miology,and natural history oflow grad and hige grade forms.Am J Surg-Pathol,1998,22(4):399-403.
    30.Matsunou H,Konishi F,Jalal R EA,et al.Alpha protein producing gastric carcinoma with enteroblastic differentiation.Cancer,1994,73:534-537.
    31.李祥周,石凤娟,乐美兆.AFP阳性胃癌的组织形态及其分型研究.临床与实验病理学杂志,1999,15(4):227-228.
    32.Adachi Y,Tsuchihashi J,Shiraishi N,AFP-producing gastric carcinoma:multivariate analysis of prognostic factors in 270 patients.Oncology,2003,65(2):95-101.
    33.Satoshi Ohno,Toshiyuki Fujii,et al.Predictive factor and timing for liver recurrence after curative resection of gastric carcinoma.The American J Surg,2003,185:258-263.
    34.黄俊,王梦龙,邹叶青.胃癌患者外周血中CEA mRNA的检测及其临床意义实用癌症杂志,2003,18(2):182-183.
    35.YoshiokaT,MasukoT,KotanagiH,etal.Homotypic adhesion through carcino-embryonicantig enplays a role in hepatic metastasis development.Jpn J Can-cerRes,1998;89(2):177-185.
    1.Zhu H,Snyder M.Protein array sandmicroarrays.Curt Opin Chimbiol,2001,5:40-45.
    2.Macbeach G,Schreiber SL.Printing proteins as microarray a for high throu-hgt function determination science,2000,289(5485):1760-1763.
    3.Manassiev V,Hane mann V,Wolfl S.Preparation of DNA and protein mi-croarrays on glass slides coated with an agarose film.Nucleic Acids Res,2000,28(12):66-71.
    4.Lee Y,Lee EK,Cho YW,ProteoChip:a highly sensitive protein microarray prepared by a novel method of protein immobilization for application of pro-tein-protein interaction studies.Proteomics.2003 Dee;3(12):2289-304.
    5.靳刚,王占金.光学蛋白质芯片.自然杂志,2001,23(5):286-289.
    6.Shi H,Tsai W,Garrison MD,et al.Template-imprinted nanostructured surfaces for protein recogniction.Nature,1999,398:593-597.
    7.http://www.ciphergen.com.cn/zy/cpjs.htm
    8.齐军,车轶群.使用多肿瘤标志物蛋白质芯片诊断系统检测卵巢肿瘤.中华检验医学杂志,2003,26(6):358-359.
    9.张文,周枚芬,陈立炎,等.丙型肝炎病毒分片段抗体检测蛋白质芯片的制备及临床评价.生物化学与生物物理进展,2003,30(6):935-937.
    10.Weiss PS.Nanot,echnology.Molecules join the assembly line.Nature.2001,413(6856):585-586.
    11.O'Brien MJ,2nd,Perez-luna VH,Brueck SR,et al.A surface plasmon resonance array biosensor based on spectroscopic imaging.Biosens Bioelec-tron,2001,16(1-2):97-108.
    12.Merchant W,Weinberger S R.Recent advancements in surface enhanced laser desorption/ionization time of flight mass spectrum.Electrophorsis,2000,21(6):1164-117
    13.Austen BM,frears ER,Davies H.The use o SELDI proteinchip arrays to monitor production of Alzheimrs betaamyliod in transected cell.J Pept Sci, 2000,6:459-469.
    14.肖雪缘,卫秀平,何大澄.应用蛋白质芯片技术从血清中筛选肺癌标志蛋白.中国科学,2003,33(4):323-325.
    15.Copeland S,Siddiqui J,Remick D.Direct comparison of traditional ELISAs and membrane protein arrays for detection and quantification of human cyto-kines.J Immunol Methods,2004,284(1-2):99-106.
    16.K P.Rosenblatt,P B Greenwood,J.K Killian,Serum Proteomics in Canc-er Diagnosis and Management.Annual Review of Medicine,2004,55(1):97-112.
    17.Feng Y,Ke X,Ma R,Parallel detection of autoantibodies with microarrays in rheumatoid diseases.Clin Chem,2004,50(2):416-422.
    18.Koopmann J,Zhang Zh,White N,et al.Serum Diagnosis of Pancreatic Ad-enocarcinoma Using Surface-Enhanced Laser Desorption and Ionization Mass Spectrometry.Clinical Cancer Research,2004,10(2):860-868.
    19.Petricoin EF,Ardekani AM,Hitt BA,et al.Use of proteomic patterns in se-rum to identify ovarian cancer,2002,359:572-577.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700