中国人胃癌和肝癌血清蛋白质组的质谱研究
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摘要
消化道恶性肿瘤在所有恶性肿瘤的发病和死亡中占据前列，其中消化道实质性脏器肿瘤如肝癌、胰腺癌等肿瘤的预后差，空腔器官的恶性肿瘤如胃癌、大肠癌等发病率高，预后也不理想。2002年全球新发、死亡的癌症病例中，消化系统肿瘤中的胃癌和肝癌高居癌症死亡原因的第2、3位，分别为70万和59.8万。严重危害着人们的生命和健康。因此做好消化道恶性肿瘤的防治工作在一个地区的癌症控制规划中占有很重要的地位。本课题选择在消化道恶性肿瘤中占有非常重要地位而又具有代表性的胃癌和肝癌进行研究。
胃癌是危害人类健康的常见恶性肿瘤，仅次于肺癌、乳腺癌和肠癌之后，位居发病率的第4位、死因的第2位。而我国是胃癌高发国之一，与世界各国比较，我国男、女性胃癌世界调整死亡率均居于首位。在我国，胃癌发病率和死亡率均占各种恶性肿瘤之首，而且近年来发病有明显年轻化趋势。胃癌病因尚未完全阐明，起病隐匿，早期常因无明显症状而漏诊，防治工作存在许多困难，相当多的胃癌患者发现时已经是晚期。目前胃癌切除术5年生存率为20％～30％，而早期胃癌根治术5年生存率可达90～95％，由此可见提高早期胃癌诊断率是至关重要的。一直以来，胃癌的早期诊断及其治疗水平的提高受到医学界的高度关注。
肝癌也是我国常见的恶性肿瘤之一，根据上海市1998-2001年上海全市居民肿瘤登记统计，全市男性平均年发病率为39.86／10万，女性为16.45／10万。肝癌的早期诊断亦比较困难。肝癌就诊时早、中、晚三期分别占29.9％、51.5％和18.6％；目前以肝切除术为代表的外科治疗仍是原发性肝癌首选治疗方法，手术切除率仅占46.3％。全部肝癌患者的1、3、5年生存率分别为66.1％、39.7％和32.5％。晚期病人的半年、1年生存率为52.5％、14.7％。而早期患者的1、3、5年生存率分别为93.5％、70.1％和59.1％。可见，肝癌的早期发现和早期诊断是肝癌获得手术并取得较好疗效的关键。这也表明了肝癌早期发现、早期诊断和早期治疗对提高肝癌治疗效果是非常重要的。
一直以来，早期诊断恶性肿瘤的研究集中在肿瘤标志物上，并取得了巨大的成功。恶性肿瘤在增生、凋亡和分化及转移等多个环节的特点均可追朔到多种基因表达的改变，而这些改变最终都会导致细胞内蛋白质表达水平的变化，从而影响细胞的生命活动，这些与肿瘤相关蛋白质的改变或多或少在肿瘤组织、血液或者其他体液中留下一些可以找到的证据，即肿瘤标志物。
目前，可用于胃癌诊断的血清标记物有CEA、CA50、CA199、CA724、SIMA、端粒酶等，用放射免疫等方法检测这些标记物，对于胃癌的诊断和判断术后有无转移复发有一定的临床意义，但单项指标的灵敏度仅为18-40％，联合应用的结果虽然可达60-80％，但假阳性率较高，难以作为早期诊断指标。同样的，甲胎蛋白(AFP)是肝癌经典的诊断指标，但AFP对肝癌诊断阳性率一般为60％-70％，虽然联合检测AFP和肿瘤特异性生长因子可以提高检测阳性率，但在原发性肝癌诊断中存在着较高的假阳性率，因此，寻找更为精确的特征性的蛋白标志物是早期诊断原发性肝癌的当务之急。
然而血清肿瘤标志物的寻找存在许多的困难。几乎全部的肿瘤特征蛋白质浓度都很低，所以，发现和鉴定这些低丰度的蛋白质成为早期诊断这些肿瘤的新任务；其次，蛋白浓度是动态变化的，应激状态、疾病或治疗后就可能会发生显著的变化，从而影响了检测。近年来，随蛋白质组研究技术和生物信息处理技术的迅猛发展，使人们从蛋白质整体水平动态研究肿瘤的发生发展已成为可能。在传统的检测方法中，双向凝胶电泳技术是一项检测血清中蛋白标记物浓度的成熟方法。通常，双向凝胶电泳技术与质谱分析技术配对使用来鉴别蛋白质，为了保证分析和临床应用的可靠性，筛选生物标记物的分析工作往往需要比较大量的样本，所以该方法费力又费标本，不适合大规模普查及临床检测。同时，双向凝胶电泳技术对于低分子量蛋白质和低丰度蛋白质的检测依然存在一定的盲区。所以，这一技术对于筛选特异的低丰度或低分子量的生物标记物存在许多的困难。表面加强激光解析电离飞行时间质谱技术(surface-enhanced laser desorption／inionation-time of flight-mass spectra，SELDI-TOF-MS)的出现弥补了上述技术的许多不足和问题。它的优势有：一是可以直接用粗样本进行检验；二是可以进行大规模、超微量、高通量、全自动筛选蛋白质分析；三是样本消耗量少；四是它不仅能发现一种蛋白质或生物标记分子，还可发现不同的多种方式的组合蛋白质谱；五是可以验证基因组学方面的变化，并基于蛋白质特点发现新的基因等。2002年，Petricoin等学者利用该技术成功的建立了卵巢癌血清蛋白组波谱模型，检测敏感性为100％，特异性95％，阳性预测值94％，经过随后二年的二期临床研究后，该项技术已在美国正式开始应用于卵巢癌的筛查。因此，SELDI-TOF-MS技术的出现使应用少量临床样本即可检测到与疾病发展密切相关的低丰度蛋白成为可能，也使血清蛋白质组学诊断模式发展成为一种有价值的诊断标准。
目前，利用该技术对中国人胃癌和肝癌蛋白组学研究尚不深入，为了寻找其可能的特征性蛋白质组学变化，并阐明这些蛋白质标志物在其早期诊断中的意义，本研究选择消化道肿瘤中死亡人数最高的胃癌和肝癌作为研究对象，并以慢性胃炎和肝硬化分别作为上述两种肿瘤的良性疾病对照，同时再与健康志愿者进行对比，利用蛋白质芯片SELDI-TOF-MS技术建立蛋白质波谱模型，并验证该模型在临床诊断中的敏感性和特异性，以探讨其在肿瘤早期诊断中的意义。本课题还应用此模型对胃癌诊断进行动态盲法检测，进一步验证其应用价值。同时对该组的部分胃癌病例进行双向凝胶电泳和差异蛋白的MALDI质谱分析，以期获得肿瘤特异表达蛋白，并对这两种实验方法进行比较。
第一部分：胃癌蛋白质波谱模型的建立及在临床诊断中的应用
目的：通过胃癌、慢性胃炎和正常人血清标本对比，采用SELDI-TOF-MS技术建立胃癌诊断的蛋白质波谱模型，检测并通过盲法验证所建立的蛋白波谱模型对胃癌的诊断价值。材料：美国Ciphergen公司PBS-Ⅱ型SELDI-TOF-MS系统和SAX2(强阴离子交换)蛋白芯片。实验用血清来源，训练组：胃腺癌33例(经病理证实)，慢性胃炎30例(有上消化道症状、经胃镜检查及病理切片证实为慢性活动性胃炎)，健康对照31例(无消化道症状者，无既往胃疾病的病史和治疗史)；盲法实验组：胃腺癌15例、慢性胃炎10例、健康自愿者10例。方法：对训练组胃癌、慢性胃炎、健康对照血清做蛋白质波谱检测；Proteinchip 3.0分析，寻找组间差异表达的蛋白质峰；对差异表达的P＜0.01的所有蛋白质峰进行分析；选出最佳排列组合；输出原始判别和交叉验证的结果，选出最佳诊断蛋白质组模型。用建立的蛋白质谱模型对盲法实验组35例未知的血清标本(包括胃腺癌患者、胃炎患者与健康人)进行盲法预测，验证该模型。结果：胃腺癌组与正常人组比较，训练组：对33例胃癌和31例健康对照者样本检测质谱图比较，得到蛋白质峰为45个，其中5910Da、5084Da、8691Da这三个蛋白质峰组合差异最大。胃癌病例血清表达5910Da蛋白质比正常人高，5084Da峰旁出现二联峰或三联峰，其峰的高低与肿瘤的分化恶性程度正相关，8691Da峰值低于正常人。用该方法检测胃癌的灵敏度为90.91％(30／33)，特异性为93.55％(29／31)，阳性预测值为93.75(30／32)。盲法组：用上述三个蛋白质峰组成的蛋白质谱模型，对25例样本进行盲法测试，15例胃腺癌中14例被准确的检出，10例健康志愿者全部被准确的检出。其敏感性为93.3％(14／15)，特异性为100％(10／10)。胃腺癌组与胃炎组比较，训练组：对33例胃癌和30例健康对照者样本检测质谱图比较，共得到蛋白质峰为38个，其中5910Da、6440Da这两个蛋白质峰组合差异最大。而在5910Da峰胃腺癌的患者均比胃炎高，6440Da峰胃腺癌的患者则均比胃炎低，在胃腺癌和胃炎患者中5084Da峰均在峰旁出现二联峰或三联峰，可见该峰可能与胃损伤有关。利用这两个蛋白质峰数据建立蛋白质谱模型，用36例样本进行盲法测试，在15例胃腺癌中，14例被准确的检出，11例胃炎患者中有10例被准确的检出，其敏感性为93.3％(14／15)，特异性为90.9％(10／11)。结论：运用SELDI-TOF-MS技术，得出了胃癌患者不同于健康人的血清蛋白质指纹图谱，应用判别分析筛选出能达到最佳诊断效果的蛋白质峰组合并建立诊断模型，交叉验证结果较满意，其灵敏度和特异度均远高于现有的各种肿瘤标志物，具有一定的诊断价值，筛选出的差异表达蛋白质将可能成为新的候选肿瘤标志物。SELDI-TOF-MS技术在胃癌的诊断尤其是早期诊断和候选肿瘤标志物的筛选等方面具有一定的价值。
第二部分：双向电泳-质谱技术筛选胃癌血清标记物
目的：探讨胃癌患者血清中与胃癌发生、发展，以及手术治疗后相关的蛋白质表达差异，以期寻找肿瘤标志物。方法：用固相pH梯度二维电泳技术对胃癌患者手术前后和正常对照各2例的血清蛋白质进行电泳，硝酸银染色，通过2-DE图谱中蛋白质斑点的增减以及颜色的深浅筛选特异蛋白质斑点，AgfaDUOSCAN凝胶扫描分析，差异蛋白质点用基质辅助激光解析电离飞行时间质谱测定其胶内酶解后的肽质指纹图谱，并查询SWISS2 PROT数据库筛选出该差异蛋白。结果：双向凝胶电泳显示，正常组和胃癌手术后组血清蛋白中有5个斑点与胃癌手术前组有显著差异。5个差异蛋白点进行胶内原位酶解，肽质指纹图谱分析成功得到了5个蛋白质肽质指纹图，并查询数据库初步鉴定了该5个蛋白质分别为Serpin B6(Placental thrombin inhibitor)(Cytoplasmic antiproteinase)(CAP)(Protease inhibitor6)(PI-6)；Septin-1(LARP)(Serologically defined breast cancer antigen NY-BR-24)；Kallikrein-6 precursor(Protease M)(Neurosin)(Zyme)(SP59)；Hemoglobin beta chain；Beta-defensin 108 precursor(Beta-defensin 8)(DEFB-8)。结论：成功的筛选出胃癌相关蛋白，该蛋白有望成为胃癌患者筛选的癌标记蛋白。
第三部分：肝癌蛋白质波谱模型的建立及在临床诊断中的应用
目的：通过肝癌、肝硬化和正常人血清标本对比，采用SELDI-TOF-MS技术建立可能用于肝癌诊断的蛋白质波谱模型，检测并验证所建立的蛋白波谱模型对肝癌的诊断价值。材料：美国Ciphergen公司PBS-Ⅱ型SELDI-TOF-MS系统和WCX2(弱阳离子交换)蛋白芯片，实验用血清来源：肝癌组40例(经B超、CT检查、血清AFP检测或手术后病理确诊为原发性肝癌)；肝硬化组46例(有症状、肝穿刺活检符合肝硬化诊断并经B超检查除外原发性肝癌的患者)；健康对照组60例(无消化道症状者，无既往肝病史，经超声肝脏检查无占位性病变，HBSAg阴性，肝功能正常范围的正常体检者)。方法：对肝癌、肝硬化、健康对照者血清做蛋白质波谱检测；Proteinchip 3.0分析，寻找组间差异表达的蛋白质峰；对差异表达的P＜0.01的所有蛋白质峰进行分析；选出最佳排列组合；输出原始判别和交叉验证的结果，选出最佳诊断蛋白质组模型并验证模型
的应用价值。结果：肝癌组与健康志愿者组比较：对40例肝癌和60例健康志愿者检测到的质谱图进行分析比较，得到差异蛋白质峰为68个，其中发现4477Da、8943Da、5181Da、8617Da、13761Da具有显著性差异。且这5个蛋白质均为肝癌患者高于健康志愿者。通过4477 Da、8943 Da这两个蛋白质峰建立肝癌蛋白质谱模型可用于区分肝癌与健康志愿者，灵敏度为90％(18／20)，特异性为100％(60／60)。肝癌组与肝硬化组比较：分别对40例肝癌和20例肝硬化样本检测到的质谱图进行分析，得到差异蛋白质峰为46个，其中4477Da、13761Da、4097Da3个蛋白质峰具有显著性差异，在4477 Da、13761 Da峰处肝癌的患者均比肝硬化高，4097 Da峰处肝癌的患者均比肝硬化低，通过4477 Da这个蛋白质峰即可区分肝癌与肝硬化患者。同时发现肝癌、肝硬化、健康志愿者3者之间8943 Da，13761 Da蛋白质随着病情的出现及严重程度逐渐增加。肝硬化组与健康志愿组比较：分别对46例肝硬化和60例健康志愿者检测到的质谱图进行分析比较，得到差异蛋白质峰为85个，其中8943Da、13761 Da、4097 Da蛋白质峰具有显著性差异。同时肝硬化患者8943 Da和13761 Da峰均比健康志愿者高，而4097 Da峰均比健康志愿者低，通过这三个蛋白质峰即可区分肝硬化患者与健康志愿者。结论：运用SELDI-TOF-MS技术，得出了肝癌患者不同于肝硬化及健康人的血清蛋白质指纹图谱。应用判别分析筛选出能达到最佳诊断效果的蛋白质峰组合并建立诊断模型。交叉验证结果满意，其灵敏度和特异度均远高于现有的各种肿瘤标志物，具有较好的临床诊断价值。筛选出的差异表达蛋白质将有可能成为新的候选肿瘤标志物。SELDI-TOF-MS技术在肝癌的诊断尤其是早期诊断和候选肿瘤标志物的筛选等方面具有一定的价值。
Malignant neoplasms of digestive system constitute a major health problem around the world. It is estimated that approximately 10.9 millian new cases of digestive carcinomas and 6.7 millian deaths occured in the word in 2002. By some estimates, gastric cancer and primary hepatic carcinoma (PHC) is the second and third most common malignant disorder worldwide. In our country, the high incidence and the poor prognosis of gastric carcinoma and PHC have made people's life and heath in serious danger. So it is very important field to prevent and treat digestive tumor.
In this study we focus on gastric cancer and primary hepatic carcinoma (PHC) by reasons of their high incidence and representative of digestive
system neoplasms. Gastric cancer is a debilitating disease associated with a high mortality. Gastric carcinoma is rampant in many countries around the world. In 2002, more than 876, 000 new cases of gastric cancer are estimated to occur in the word. It remains the second leading cause of cancer death. The incidence of gastric carcinoma is extremely high and its incidence has been on the increase for less than 40 years in China. Its successful treatment relies on early diagnosis, but this remains a challenge since the progression of the malignancy is usually silent until it reaches a more advanced stage which prognosis is poor. Certainly, early detection can drastically facilitate treatment and improve the long-term survival of the patient. Thus, gastric carcinoma continues to pose a major challenge for epidemiologists, gastroenterologists, surgical oncologists, radiation oncologists, and medical oncologists. PHC is also rampant in many countries around the world. According to the statistic of enregister in Shanghai, the incidence of the liver cancer is 39.86 in each 100, 000 population, in which female is 16.45 each 100, 000 persons. PHC has become the third most common malignant disorder in China. PHC is often diagnosed at an advanced stage and remain a poor prognosis.
Primary hepatic carcinoma (PHC) presents at a late clinical stage in more than 80% of patients and the 1、3、5-year survival of PHC in this population is 66.1%、39.7% and 32.5%. By contrast, the 1、3、5-year survival for patients with early stage liver cancer exceeds 93.5%、70.1% and 59.1%. Therefore, increasing the number of patients diagnosed with early stage disease should have a direct effect on the mortality and economics of this cancer without the need to change surgical or chemotherapeutic approaches.
Early diagnosis improves the long-term survival chances of patients with
cancers. However, one of the features of nearly all human cancers is that there is molecular heterogenicity, meaning that screening for a single diagnostic marker is not efficient, with some patients not being correctly diagnosed. A logical development to improve the early diagnosis of cancer is to therefore simultaneously screen for multiple biomarkers to increase the probability of detection. The sensitivity of the current single biomarkers in tumor diagnosis is low (usually less than 40%) and complicated by a high return of 'false-positives. Further, none of the existing serum markers can be used individually for screening for cancer. It would be highly desirable to have a new rapid and sensitive diagnostic test for cancer. And new technologies for the detection of early stage cancer are urgently needed.
Clearly , the use of single biomarkers to diagnose cancer has disadvantages. It is a logical step, therefore, to explore the possibility that a use of a combination of biomarkers could improve diagnostic power. Pathological changes within an organ might be reflected in proteomic patterns in serum. Proteomics is the large-scale study of proteins, or the simultaneous measurement of a large number of expressed proteins. Proteomic profiling enables a new approach to the discovery of biomarkers in disease. It has recently been shown to be useful in identifying biomarkers for the diagnosis of bladder, prostate, ovary, breast, liver malignancies and other cancers.
With the development in genomic research, protemics has become an extremely active and increasingly important part of the worldwide life science research and the core of the functional genomics era or the "postgenomicera". Several new technologies in proteomics developed in the past few years greatly boosted the discovery-based research of tumor markers beyond the
disadvantages of traditional approaches.
Generation of the mass spectra requires only a small serum sample that could be obtained by fingerprick, and results are obtained in less than 30 min. Cost-effective, high-throughput screening is feasible. The concept and tool are flexible, and can be applied to any biological state and to data derived from future mass spectrometry platforms with higher resolution, sensitivity, and mass accuracy than the platform used herein. Samples can be applied to mass spectroscopy chips in the local laboratory and then transported to a central laboratory that houses the analytical software. Moreove , transportation of the raw spectra via the internet to a central site that incorporates an ever-expanding training set is feasible. By this approach, the pattern itself, independent of the identity of the proteins or peptides, is the discriminator, and might represent a new diagnostic paradigm.
Modern proteomic profiling involves ProteinChip technology that sometimes utilizes an enhanced surface laser desorption/ionization (SELDI) time of flightmass spectrometry system. Clinical proteomics involves the analysis of protein expression profiles in samples for de novo discovery of disease-related biomarkers and for gaining insight into the biology of disease processes. Analysis of samples through SELDI has tremendous potential because of its ability to conveniently resolve a wide spectrum of proteins. The technology utilizes patented biochip arrays to capture time of flight mass spectrometry data on individual proteins, or groups of proteins, with common biochemical properties such as hydrophobicity, from complex mixtures. It is numerous advantages over other analytic methods such as 2D-gel electrophoresis. For example, although 2D-gel electrophoresis is capable of
resolving several thousands of proteins, it is labor intensive, requires a large sample volume And needs an additional evaluation assay system. Conversely, SELDI has a much higher throughput capacity, requires significantly lower amounts of samples, has sub-femtomol range sensitivity and enables higher resolution over a lower mass range. There are also several ProteinChip arrays available that have different chromatographic properties, such as anion exchange, cation exchange, immobilized-metal affinity, with hydrophilic or hydrophobic surfaces. These are directly compatible with serum and urine, which are the commonly used body fluids to screen for biomarkers. Recent studies have successfully implemented the use of SELDI on sera to identify unique proteomic patterns that discriminate between malignant and benign growth in the ovary.
The SELDI assay and detection system has not been used for the profiling of protein clusters in gastric cancer or liver cancer. In the present studies, therefore, we employed SELDI to probe for serum proteins that may be expressed in patients with gastric cancer and liver cancer. We addressed the possibility that serum proteins critical to the progression of gastric cancer and liver cancer can be identified through the changes in peptide/proteinmass spectral patterns. We also investigated if such potential changes in spectral patterns are discriminatory of patients with gastritis compared to healthy volunteers.
Part I Serum proteomic patterns for gastric lesions as revealed by SELDI mass spectrometry
Aim: SELDI-TOF mass spectrometry was used to investigate protein expression in sera of patients with gastric cancer and gastritis compared to normal volunteers. To establish a serum protein pattern model for screening gastric cancer, and used it to identify proteomic patterns in serum that distinguish neoplastic from non-neoplastic disease within the gastric cancer. Material: Type protein chip reader (PBS II, Ciphergen Biosystems, Inc., Fremont , USA); strong anionic exchanger (SAX2) Chips (Ciphergen Biosystems, Inc., Fremont, USA); the BiomarkerWizard software (version 3.0, Ciphergen Biosystems, Fremont, CA, USA). Approximately 5 ml of blood was withdrawn via vein puncture from each patient. Training group: gastric cancer patients 33 samples (confirmed by pathology), gastritis patients 30 samples (confirmed by gastroscopy and pathology), healthy volunteers 31 samples; independent group: gastric cancer 15 samples, gastritis 10 samples, healthy volunteers 10 samples. Methods: SELDI-TOF mass spectrometry was used to investigate protein expression in sera of patients with gastric cancer and gastritis compared to normal volunteers. A preliminary "training" set of spectra derived from analysis of serum from 33 patients with gastric cancer and 30 patients with and 31 healthy volunteers were analyzed by an iterative searching algorithm that identified a proteomic pattern that completely discriminated cancer from noncancer. The discovered pattern was then used to classify an independent set of 35 masked serum samples: 15 from gastric cancer, and 10 from gastritis, and 10 from healthy volunteers. Results: A comparison of protein mass spectra obtained from the sera of the gastric cancer group and the healthy volunteer group revealed 45 protein peaks. There were statistical differences between 3 protein peaks located at 5910
Da, 5084 Da and 8691 Da (P<0.05); the intensity of protein peaks at 5910 Da in the sera from patients with gastric cancer was clearly higher than that of the healthy controls (P<0.05). Bi-peak or tri-peaks at 5084 Da were also observed in the sera from patients with gastric cancer. Further, the protein peak at 8691 Da in the sera frompatients with gastric cancer was down-regulated compared to normal healthy volunteers. Using the above profiles, 30 of 33 patients diagnosed pathologically with gastric cancer were correctly identified by SELDI. Twenty-nine of 31 healthy volunteers were correctly identified as normal. The sensitivity of neoplastic identification was 90.91 % (30/33) for patients, whereas the specificity of control verification was 93.55% (29/31). The mass spectral patterns with three special protein peaks, were used on the double-blinded sera. Using these criteria, gastric cancerwas correctly identified in 14 out of 15 samples, gastritis was correctly identified in 10 out of 11 patients and all 10 out of 10 normal volunteers were identified. The biomarkers therefore had an accuracy of 93.3%and 90.9%for the identification of gastric cancer and gastritis, respectively. The biomarkers had a 100% accuracy to identify healthy volunteers that did not have gastric cancer or gastritis. Conclusion: Use the SELDI-TOF mass spectrometry to establish a serum protein pattern model for screening gastric cancer, and identify proteomic patterns in serum that distinguish gastric cancer from healthy volunteers. The importance of our studies has been to demonstrate the usefulness of SELDI in the discovery of potential tumor markers in serum samples. The comparison of protein expression profiles from serum appears to provide an effective approach to identifying unique biomarkers for gastric cancer and gastritis. Larger scale studies appear warranted to confirm the
ability of SELDI as a proteomic screen in the clinical setting.
Part II Screening Serum Biomarkers of gastric cancer by Two-dimensional Electrophoresis and Mass Spectrometry
Aim: To study the proteins related to the Gastric carcinoma occurrence and development in the blood plasma of Gastric carcinoma patients. Methods: To establish two-dimensional eletrophoresis profiles with serum protein of patients before or after gastric carcinoma operation and paired normal serum protein.After argent nitrate staining and diferential expression protein spots screening via protein blotched increase and decrease and color shade of 2-DE atlas, we selected these distinction proteins via Agfa DUOSCAN gel scanning and measured the peptides'finger printings after enzymolysis analysis by MALDITOF.Then searched SWISS2PROT data base to screen these distinction proteins. Results: When comparing the sera proteins of these three groups by dimensional gel electrophoresis, we found 5 protein spots in serum protein of patients before gastric carcinoma operation were different significantly from after gastric carcinoma operation or normal group The five proteins undertook situ enzymolysis, we harvested successfully five finger printings via peptides finger printing analysis, the data base searching showed that the five proteins were Serpin B6 (Placental thrombin inhibitor) (Cytoplasmic antiproteinase) (CAP) (Protease inhibitor6) (PI-6) , Septin-I(LARP) (Serologically defined breast cancer antigen NY-BR-24), Kallikrein-6 precursor(Protease M) (Neurosin) (Zyme) (SP59), Hemoglobin beta chain and Beta-defensin 108 precursor (Beta-defensin 8) (DEFB-8). Conclusion: We successfully screened gastric carcinoma related proteins,
these proteins might be symbol protein for gastric carcinoma, so the success of this experiment undoubtedly could serve as a basis for the gastric carcinoma earlier detection and therapy.
Part III Serum proteomic patterns for primary hepatic carcinoma as revealed by SELDI mass spectrometry
Aim: SELDI-TOF mass spectrometry was used to investigate protein expression in sera of patients with Primary hepatic carcinoma (PHC) and hepatocirrhosis compared to normal volunteers. To establish a serum protein pattern model for screening liver cancer, and used it to identify proteomic patterns in serum that distinguish neoplastic from non-neoplastic disease within the PHC. Material: Type protein chip reader (PBS II, Ciphergen Biosystems, Inc., Fremont, USA); Strong anionic exchanger (SAX2) Chips (Ciphergen Biosystems, Inc., Fremont, USA); The Biomarker Wizard software (version 3.0, Ciphergen Biosystems, Fremont, CA, USA). Approximately 5 ml of blood was withdrawn via veinipuncture from each patient. PHC patients 40 samples (confirmed by type-B ultrasonic、 CT、AFP mensuration、 pathology), hepatocirrhosis patients 46 samples ( confirmed by liver puncture), healthy volunteers 60 samples. Methods: SELDI-TOF mass spectrometry was used to investigate protein expression in sera of patients with PHC and hepatocirrhosis compared to normal volunteers. A preliminary "training" set of spectra derived from analysis of serum from 40 patients with PHC and 46 patients with hepatocirrhosis and 60 healthy volunteers were analysed by an
iterative searching algorithm that identified a proteomic pattern that completely discriminated cancer from noncancer. Results: A comparison of protein mass spectra obtained from the sera of the PHC group and the healthy volunteer group revealed 68 protein peaks. There were statistical differences between 5 protein peaks located at 4477 Da、8943 Da、5181 Da、8617 Da、 13761 Da; the intensity of this 5 protein peaks from patients with PHC was clearly higher than that of the healthy controls (P<0.05). Using the above profiles, 18 of 20 patients diagnosed pathologically with PHC were correctly identified by SELDI. Sixty of 60 healthy volunteers were correctly identified as normal. The sensitivity of neoplastic identification was 90% (18/20) for patients, whereas the specificity of control verification was 100% (60/60). A comparison of protein mass spectra obtained from the sera of the PHC group and the hepatocirrhosis group revealed 46 protein peaks. There were statistical differences between 3 protein peaks located at 4477 Da、13761 Da、4097 Da; the intensity of the 4477 Da and 13761 Da protein peaks from patients with PHC was clearly higher than that of hepatocirrhosis. Meanwhile, the intensity of the 4097 Da protein peaks from patients with PHC was clearly lower than that of hepatocirrhosis. Using the 4477 Da, we can distinguish PHC from hepatocirrhosis. A comparison of protein mass spectra obtained from the sera of the hepatocirrhosis group and the healthy volunteer group revealed 85 protein peaks. There were statistical differences between 3 protein peaks located at 8943 Da、13761 Da> 4097 Da; the intensity of the 8943 Da、13761 Da protein peaks from patients with hepatocirrhosis was clearly higher than that of the healthy volunteer. Meanwhile, the intensity of the 4097 Da protein peaks from patients with hepatocirrhosis was clearly lower than that of the
healthy volunteer. Using the this 3 protein peaks, we can distinguish hepatocirrhosis from the healthy volunteer. Conclusion: Use the SELDI-TOF mass spectrometry to establish a serum protein pattern model for screening PHC and hepatocirrhosis and the healthy volunteer, and identify proteomic patterns in serum that distinguish PHC from noncancer. The importance of our studies has been to demonstrate the usefulness of SELDI in the discovery of potential tumor markers in serum samples. The comparison of protein expression profiles from serum appears to provide an effective approach to identifying unique biomarkers for PHC and hepatocirrhosis. Larger scale studies appear warranted confirming the ability of SELDI as a proteomic screen in the clinical setting.
胃癌是危害人类健康的常见恶性肿瘤，仅次于肺癌、乳腺癌和肠癌之后，位居发病率的第4位、死因的第2位。而我国是胃癌高发国之一，与世界各国比较，我国男、女性胃癌世界调整死亡率均居于首位。在我国，胃癌发病率和死亡率均占各种恶性肿瘤之首，而且近年来发病有明显年轻化趋势。胃癌病因尚未完全阐明，起病隐匿，早期常因无明显症状而漏诊，防治工作存在许多困难，相当多的胃癌患者发现时已经是晚期。目前胃癌切除术5年生存率为20％～30％，而早期胃癌根治术5年生存率可达90～95％，由此可见提高早期胃癌诊断率是至关重要的。一直以来，胃癌的早期诊断及其治疗水平的提高受到医学界的高度关注。
肝癌也是我国常见的恶性肿瘤之一，根据上海市1998-2001年上海全市居民肿瘤登记统计，全市男性平均年发病率为39.86／10万，女性为16.45／10万。肝癌的早期诊断亦比较困难。肝癌就诊时早、中、晚三期分别占29.9％、51.5％和18.6％；目前以肝切除术为代表的外科治疗仍是原发性肝癌首选治疗方法，手术切除率仅占46.3％。全部肝癌患者的1、3、5年生存率分别为66.1％、39.7％和32.5％。晚期病人的半年、1年生存率为52.5％、14.7％。而早期患者的1、3、5年生存率分别为93.5％、70.1％和59.1％。可见，肝癌的早期发现和早期诊断是肝癌获得手术并取得较好疗效的关键。这也表明了肝癌早期发现、早期诊断和早期治疗对提高肝癌治疗效果是非常重要的。
一直以来，早期诊断恶性肿瘤的研究集中在肿瘤标志物上，并取得了巨大的成功。恶性肿瘤在增生、凋亡和分化及转移等多个环节的特点均可追朔到多种基因表达的改变，而这些改变最终都会导致细胞内蛋白质表达水平的变化，从而影响细胞的生命活动，这些与肿瘤相关蛋白质的改变或多或少在肿瘤组织、血液或者其他体液中留下一些可以找到的证据，即肿瘤标志物。
目前，可用于胃癌诊断的血清标记物有CEA、CA50、CA199、CA724、SIMA、端粒酶等，用放射免疫等方法检测这些标记物，对于胃癌的诊断和判断术后有无转移复发有一定的临床意义，但单项指标的灵敏度仅为18-40％，联合应用的结果虽然可达60-80％，但假阳性率较高，难以作为早期诊断指标。同样的，甲胎蛋白(AFP)是肝癌经典的诊断指标，但AFP对肝癌诊断阳性率一般为60％-70％，虽然联合检测AFP和肿瘤特异性生长因子可以提高检测阳性率，但在原发性肝癌诊断中存在着较高的假阳性率，因此，寻找更为精确的特征性的蛋白标志物是早期诊断原发性肝癌的当务之急。
然而血清肿瘤标志物的寻找存在许多的困难。几乎全部的肿瘤特征蛋白质浓度都很低，所以，发现和鉴定这些低丰度的蛋白质成为早期诊断这些肿瘤的新任务；其次，蛋白浓度是动态变化的，应激状态、疾病或治疗后就可能会发生显著的变化，从而影响了检测。近年来，随蛋白质组研究技术和生物信息处理技术的迅猛发展，使人们从蛋白质整体水平动态研究肿瘤的发生发展已成为可能。在传统的检测方法中，双向凝胶电泳技术是一项检测血清中蛋白标记物浓度的成熟方法。通常，双向凝胶电泳技术与质谱分析技术配对使用来鉴别蛋白质，为了保证分析和临床应用的可靠性，筛选生物标记物的分析工作往往需要比较大量的样本，所以该方法费力又费标本，不适合大规模普查及临床检测。同时，双向凝胶电泳技术对于低分子量蛋白质和低丰度蛋白质的检测依然存在一定的盲区。所以，这一技术对于筛选特异的低丰度或低分子量的生物标记物存在许多的困难。表面加强激光解析电离飞行时间质谱技术(surface-enhanced laser desorption／inionation-time of flight-mass spectra，SELDI-TOF-MS)的出现弥补了上述技术的许多不足和问题。它的优势有：一是可以直接用粗样本进行检验；二是可以进行大规模、超微量、高通量、全自动筛选蛋白质分析；三是样本消耗量少；四是它不仅能发现一种蛋白质或生物标记分子，还可发现不同的多种方式的组合蛋白质谱；五是可以验证基因组学方面的变化，并基于蛋白质特点发现新的基因等。2002年，Petricoin等学者利用该技术成功的建立了卵巢癌血清蛋白组波谱模型，检测敏感性为100％，特异性95％，阳性预测值94％，经过随后二年的二期临床研究后，该项技术已在美国正式开始应用于卵巢癌的筛查。因此，SELDI-TOF-MS技术的出现使应用少量临床样本即可检测到与疾病发展密切相关的低丰度蛋白成为可能，也使血清蛋白质组学诊断模式发展成为一种有价值的诊断标准。
目前，利用该技术对中国人胃癌和肝癌蛋白组学研究尚不深入，为了寻找其可能的特征性蛋白质组学变化，并阐明这些蛋白质标志物在其早期诊断中的意义，本研究选择消化道肿瘤中死亡人数最高的胃癌和肝癌作为研究对象，并以慢性胃炎和肝硬化分别作为上述两种肿瘤的良性疾病对照，同时再与健康志愿者进行对比，利用蛋白质芯片SELDI-TOF-MS技术建立蛋白质波谱模型，并验证该模型在临床诊断中的敏感性和特异性，以探讨其在肿瘤早期诊断中的意义。本课题还应用此模型对胃癌诊断进行动态盲法检测，进一步验证其应用价值。同时对该组的部分胃癌病例进行双向凝胶电泳和差异蛋白的MALDI质谱分析，以期获得肿瘤特异表达蛋白，并对这两种实验方法进行比较。
第一部分：胃癌蛋白质波谱模型的建立及在临床诊断中的应用
目的：通过胃癌、慢性胃炎和正常人血清标本对比，采用SELDI-TOF-MS技术建立胃癌诊断的蛋白质波谱模型，检测并通过盲法验证所建立的蛋白波谱模型对胃癌的诊断价值。材料：美国Ciphergen公司PBS-Ⅱ型SELDI-TOF-MS系统和SAX2(强阴离子交换)蛋白芯片。实验用血清来源，训练组：胃腺癌33例(经病理证实)，慢性胃炎30例(有上消化道症状、经胃镜检查及病理切片证实为慢性活动性胃炎)，健康对照31例(无消化道症状者，无既往胃疾病的病史和治疗史)；盲法实验组：胃腺癌15例、慢性胃炎10例、健康自愿者10例。方法：对训练组胃癌、慢性胃炎、健康对照血清做蛋白质波谱检测；Proteinchip 3.0分析，寻找组间差异表达的蛋白质峰；对差异表达的P＜0.01的所有蛋白质峰进行分析；选出最佳排列组合；输出原始判别和交叉验证的结果，选出最佳诊断蛋白质组模型。用建立的蛋白质谱模型对盲法实验组35例未知的血清标本(包括胃腺癌患者、胃炎患者与健康人)进行盲法预测，验证该模型。结果：胃腺癌组与正常人组比较，训练组：对33例胃癌和31例健康对照者样本检测质谱图比较，得到蛋白质峰为45个，其中5910Da、5084Da、8691Da这三个蛋白质峰组合差异最大。胃癌病例血清表达5910Da蛋白质比正常人高，5084Da峰旁出现二联峰或三联峰，其峰的高低与肿瘤的分化恶性程度正相关，8691Da峰值低于正常人。用该方法检测胃癌的灵敏度为90.91％(30／33)，特异性为93.55％(29／31)，阳性预测值为93.75(30／32)。盲法组：用上述三个蛋白质峰组成的蛋白质谱模型，对25例样本进行盲法测试，15例胃腺癌中14例被准确的检出，10例健康志愿者全部被准确的检出。其敏感性为93.3％(14／15)，特异性为100％(10／10)。胃腺癌组与胃炎组比较，训练组：对33例胃癌和30例健康对照者样本检测质谱图比较，共得到蛋白质峰为38个，其中5910Da、6440Da这两个蛋白质峰组合差异最大。而在5910Da峰胃腺癌的患者均比胃炎高，6440Da峰胃腺癌的患者则均比胃炎低，在胃腺癌和胃炎患者中5084Da峰均在峰旁出现二联峰或三联峰，可见该峰可能与胃损伤有关。利用这两个蛋白质峰数据建立蛋白质谱模型，用36例样本进行盲法测试，在15例胃腺癌中，14例被准确的检出，11例胃炎患者中有10例被准确的检出，其敏感性为93.3％(14／15)，特异性为90.9％(10／11)。结论：运用SELDI-TOF-MS技术，得出了胃癌患者不同于健康人的血清蛋白质指纹图谱，应用判别分析筛选出能达到最佳诊断效果的蛋白质峰组合并建立诊断模型，交叉验证结果较满意，其灵敏度和特异度均远高于现有的各种肿瘤标志物，具有一定的诊断价值，筛选出的差异表达蛋白质将可能成为新的候选肿瘤标志物。SELDI-TOF-MS技术在胃癌的诊断尤其是早期诊断和候选肿瘤标志物的筛选等方面具有一定的价值。
第二部分：双向电泳-质谱技术筛选胃癌血清标记物
目的：探讨胃癌患者血清中与胃癌发生、发展，以及手术治疗后相关的蛋白质表达差异，以期寻找肿瘤标志物。方法：用固相pH梯度二维电泳技术对胃癌患者手术前后和正常对照各2例的血清蛋白质进行电泳，硝酸银染色，通过2-DE图谱中蛋白质斑点的增减以及颜色的深浅筛选特异蛋白质斑点，AgfaDUOSCAN凝胶扫描分析，差异蛋白质点用基质辅助激光解析电离飞行时间质谱测定其胶内酶解后的肽质指纹图谱，并查询SWISS2 PROT数据库筛选出该差异蛋白。结果：双向凝胶电泳显示，正常组和胃癌手术后组血清蛋白中有5个斑点与胃癌手术前组有显著差异。5个差异蛋白点进行胶内原位酶解，肽质指纹图谱分析成功得到了5个蛋白质肽质指纹图，并查询数据库初步鉴定了该5个蛋白质分别为Serpin B6(Placental thrombin inhibitor)(Cytoplasmic antiproteinase)(CAP)(Protease inhibitor6)(PI-6)；Septin-1(LARP)(Serologically defined breast cancer antigen NY-BR-24)；Kallikrein-6 precursor(Protease M)(Neurosin)(Zyme)(SP59)；Hemoglobin beta chain；Beta-defensin 108 precursor(Beta-defensin 8)(DEFB-8)。结论：成功的筛选出胃癌相关蛋白，该蛋白有望成为胃癌患者筛选的癌标记蛋白。
第三部分：肝癌蛋白质波谱模型的建立及在临床诊断中的应用
目的：通过肝癌、肝硬化和正常人血清标本对比，采用SELDI-TOF-MS技术建立可能用于肝癌诊断的蛋白质波谱模型，检测并验证所建立的蛋白波谱模型对肝癌的诊断价值。材料：美国Ciphergen公司PBS-Ⅱ型SELDI-TOF-MS系统和WCX2(弱阳离子交换)蛋白芯片，实验用血清来源：肝癌组40例(经B超、CT检查、血清AFP检测或手术后病理确诊为原发性肝癌)；肝硬化组46例(有症状、肝穿刺活检符合肝硬化诊断并经B超检查除外原发性肝癌的患者)；健康对照组60例(无消化道症状者，无既往肝病史，经超声肝脏检查无占位性病变，HBSAg阴性，肝功能正常范围的正常体检者)。方法：对肝癌、肝硬化、健康对照者血清做蛋白质波谱检测；Proteinchip 3.0分析，寻找组间差异表达的蛋白质峰；对差异表达的P＜0.01的所有蛋白质峰进行分析；选出最佳排列组合；输出原始判别和交叉验证的结果，选出最佳诊断蛋白质组模型并验证模型
的应用价值。结果：肝癌组与健康志愿者组比较：对40例肝癌和60例健康志愿者检测到的质谱图进行分析比较，得到差异蛋白质峰为68个，其中发现4477Da、8943Da、5181Da、8617Da、13761Da具有显著性差异。且这5个蛋白质均为肝癌患者高于健康志愿者。通过4477 Da、8943 Da这两个蛋白质峰建立肝癌蛋白质谱模型可用于区分肝癌与健康志愿者，灵敏度为90％(18／20)，特异性为100％(60／60)。肝癌组与肝硬化组比较：分别对40例肝癌和20例肝硬化样本检测到的质谱图进行分析，得到差异蛋白质峰为46个，其中4477Da、13761Da、4097Da3个蛋白质峰具有显著性差异，在4477 Da、13761 Da峰处肝癌的患者均比肝硬化高，4097 Da峰处肝癌的患者均比肝硬化低，通过4477 Da这个蛋白质峰即可区分肝癌与肝硬化患者。同时发现肝癌、肝硬化、健康志愿者3者之间8943 Da，13761 Da蛋白质随着病情的出现及严重程度逐渐增加。肝硬化组与健康志愿组比较：分别对46例肝硬化和60例健康志愿者检测到的质谱图进行分析比较，得到差异蛋白质峰为85个，其中8943Da、13761 Da、4097 Da蛋白质峰具有显著性差异。同时肝硬化患者8943 Da和13761 Da峰均比健康志愿者高，而4097 Da峰均比健康志愿者低，通过这三个蛋白质峰即可区分肝硬化患者与健康志愿者。结论：运用SELDI-TOF-MS技术，得出了肝癌患者不同于肝硬化及健康人的血清蛋白质指纹图谱。应用判别分析筛选出能达到最佳诊断效果的蛋白质峰组合并建立诊断模型。交叉验证结果满意，其灵敏度和特异度均远高于现有的各种肿瘤标志物，具有较好的临床诊断价值。筛选出的差异表达蛋白质将有可能成为新的候选肿瘤标志物。SELDI-TOF-MS技术在肝癌的诊断尤其是早期诊断和候选肿瘤标志物的筛选等方面具有一定的价值。
Malignant neoplasms of digestive system constitute a major health problem around the world. It is estimated that approximately 10.9 millian new cases of digestive carcinomas and 6.7 millian deaths occured in the word in 2002. By some estimates, gastric cancer and primary hepatic carcinoma (PHC) is the second and third most common malignant disorder worldwide. In our country, the high incidence and the poor prognosis of gastric carcinoma and PHC have made people's life and heath in serious danger. So it is very important field to prevent and treat digestive tumor.
In this study we focus on gastric cancer and primary hepatic carcinoma (PHC) by reasons of their high incidence and representative of digestive
system neoplasms. Gastric cancer is a debilitating disease associated with a high mortality. Gastric carcinoma is rampant in many countries around the world. In 2002, more than 876, 000 new cases of gastric cancer are estimated to occur in the word. It remains the second leading cause of cancer death. The incidence of gastric carcinoma is extremely high and its incidence has been on the increase for less than 40 years in China. Its successful treatment relies on early diagnosis, but this remains a challenge since the progression of the malignancy is usually silent until it reaches a more advanced stage which prognosis is poor. Certainly, early detection can drastically facilitate treatment and improve the long-term survival of the patient. Thus, gastric carcinoma continues to pose a major challenge for epidemiologists, gastroenterologists, surgical oncologists, radiation oncologists, and medical oncologists. PHC is also rampant in many countries around the world. According to the statistic of enregister in Shanghai, the incidence of the liver cancer is 39.86 in each 100, 000 population, in which female is 16.45 each 100, 000 persons. PHC has become the third most common malignant disorder in China. PHC is often diagnosed at an advanced stage and remain a poor prognosis.
Primary hepatic carcinoma (PHC) presents at a late clinical stage in more than 80% of patients and the 1、3、5-year survival of PHC in this population is 66.1%、39.7% and 32.5%. By contrast, the 1、3、5-year survival for patients with early stage liver cancer exceeds 93.5%、70.1% and 59.1%. Therefore, increasing the number of patients diagnosed with early stage disease should have a direct effect on the mortality and economics of this cancer without the need to change surgical or chemotherapeutic approaches.
Early diagnosis improves the long-term survival chances of patients with
cancers. However, one of the features of nearly all human cancers is that there is molecular heterogenicity, meaning that screening for a single diagnostic marker is not efficient, with some patients not being correctly diagnosed. A logical development to improve the early diagnosis of cancer is to therefore simultaneously screen for multiple biomarkers to increase the probability of detection. The sensitivity of the current single biomarkers in tumor diagnosis is low (usually less than 40%) and complicated by a high return of 'false-positives. Further, none of the existing serum markers can be used individually for screening for cancer. It would be highly desirable to have a new rapid and sensitive diagnostic test for cancer. And new technologies for the detection of early stage cancer are urgently needed.
Clearly , the use of single biomarkers to diagnose cancer has disadvantages. It is a logical step, therefore, to explore the possibility that a use of a combination of biomarkers could improve diagnostic power. Pathological changes within an organ might be reflected in proteomic patterns in serum. Proteomics is the large-scale study of proteins, or the simultaneous measurement of a large number of expressed proteins. Proteomic profiling enables a new approach to the discovery of biomarkers in disease. It has recently been shown to be useful in identifying biomarkers for the diagnosis of bladder, prostate, ovary, breast, liver malignancies and other cancers.
With the development in genomic research, protemics has become an extremely active and increasingly important part of the worldwide life science research and the core of the functional genomics era or the "postgenomicera". Several new technologies in proteomics developed in the past few years greatly boosted the discovery-based research of tumor markers beyond the
disadvantages of traditional approaches.
Generation of the mass spectra requires only a small serum sample that could be obtained by fingerprick, and results are obtained in less than 30 min. Cost-effective, high-throughput screening is feasible. The concept and tool are flexible, and can be applied to any biological state and to data derived from future mass spectrometry platforms with higher resolution, sensitivity, and mass accuracy than the platform used herein. Samples can be applied to mass spectroscopy chips in the local laboratory and then transported to a central laboratory that houses the analytical software. Moreove , transportation of the raw spectra via the internet to a central site that incorporates an ever-expanding training set is feasible. By this approach, the pattern itself, independent of the identity of the proteins or peptides, is the discriminator, and might represent a new diagnostic paradigm.
Modern proteomic profiling involves ProteinChip technology that sometimes utilizes an enhanced surface laser desorption/ionization (SELDI) time of flightmass spectrometry system. Clinical proteomics involves the analysis of protein expression profiles in samples for de novo discovery of disease-related biomarkers and for gaining insight into the biology of disease processes. Analysis of samples through SELDI has tremendous potential because of its ability to conveniently resolve a wide spectrum of proteins. The technology utilizes patented biochip arrays to capture time of flight mass spectrometry data on individual proteins, or groups of proteins, with common biochemical properties such as hydrophobicity, from complex mixtures. It is numerous advantages over other analytic methods such as 2D-gel electrophoresis. For example, although 2D-gel electrophoresis is capable of
resolving several thousands of proteins, it is labor intensive, requires a large sample volume And needs an additional evaluation assay system. Conversely, SELDI has a much higher throughput capacity, requires significantly lower amounts of samples, has sub-femtomol range sensitivity and enables higher resolution over a lower mass range. There are also several ProteinChip arrays available that have different chromatographic properties, such as anion exchange, cation exchange, immobilized-metal affinity, with hydrophilic or hydrophobic surfaces. These are directly compatible with serum and urine, which are the commonly used body fluids to screen for biomarkers. Recent studies have successfully implemented the use of SELDI on sera to identify unique proteomic patterns that discriminate between malignant and benign growth in the ovary.
The SELDI assay and detection system has not been used for the profiling of protein clusters in gastric cancer or liver cancer. In the present studies, therefore, we employed SELDI to probe for serum proteins that may be expressed in patients with gastric cancer and liver cancer. We addressed the possibility that serum proteins critical to the progression of gastric cancer and liver cancer can be identified through the changes in peptide/proteinmass spectral patterns. We also investigated if such potential changes in spectral patterns are discriminatory of patients with gastritis compared to healthy volunteers.
Part I Serum proteomic patterns for gastric lesions as revealed by SELDI mass spectrometry
Aim: SELDI-TOF mass spectrometry was used to investigate protein expression in sera of patients with gastric cancer and gastritis compared to normal volunteers. To establish a serum protein pattern model for screening gastric cancer, and used it to identify proteomic patterns in serum that distinguish neoplastic from non-neoplastic disease within the gastric cancer. Material: Type protein chip reader (PBS II, Ciphergen Biosystems, Inc., Fremont , USA); strong anionic exchanger (SAX2) Chips (Ciphergen Biosystems, Inc., Fremont, USA); the BiomarkerWizard software (version 3.0, Ciphergen Biosystems, Fremont, CA, USA). Approximately 5 ml of blood was withdrawn via vein puncture from each patient. Training group: gastric cancer patients 33 samples (confirmed by pathology), gastritis patients 30 samples (confirmed by gastroscopy and pathology), healthy volunteers 31 samples; independent group: gastric cancer 15 samples, gastritis 10 samples, healthy volunteers 10 samples. Methods: SELDI-TOF mass spectrometry was used to investigate protein expression in sera of patients with gastric cancer and gastritis compared to normal volunteers. A preliminary "training" set of spectra derived from analysis of serum from 33 patients with gastric cancer and 30 patients with and 31 healthy volunteers were analyzed by an iterative searching algorithm that identified a proteomic pattern that completely discriminated cancer from noncancer. The discovered pattern was then used to classify an independent set of 35 masked serum samples: 15 from gastric cancer, and 10 from gastritis, and 10 from healthy volunteers. Results: A comparison of protein mass spectra obtained from the sera of the gastric cancer group and the healthy volunteer group revealed 45 protein peaks. There were statistical differences between 3 protein peaks located at 5910
Da, 5084 Da and 8691 Da (P<0.05); the intensity of protein peaks at 5910 Da in the sera from patients with gastric cancer was clearly higher than that of the healthy controls (P<0.05). Bi-peak or tri-peaks at 5084 Da were also observed in the sera from patients with gastric cancer. Further, the protein peak at 8691 Da in the sera frompatients with gastric cancer was down-regulated compared to normal healthy volunteers. Using the above profiles, 30 of 33 patients diagnosed pathologically with gastric cancer were correctly identified by SELDI. Twenty-nine of 31 healthy volunteers were correctly identified as normal. The sensitivity of neoplastic identification was 90.91 % (30/33) for patients, whereas the specificity of control verification was 93.55% (29/31). The mass spectral patterns with three special protein peaks, were used on the double-blinded sera. Using these criteria, gastric cancerwas correctly identified in 14 out of 15 samples, gastritis was correctly identified in 10 out of 11 patients and all 10 out of 10 normal volunteers were identified. The biomarkers therefore had an accuracy of 93.3%and 90.9%for the identification of gastric cancer and gastritis, respectively. The biomarkers had a 100% accuracy to identify healthy volunteers that did not have gastric cancer or gastritis. Conclusion: Use the SELDI-TOF mass spectrometry to establish a serum protein pattern model for screening gastric cancer, and identify proteomic patterns in serum that distinguish gastric cancer from healthy volunteers. The importance of our studies has been to demonstrate the usefulness of SELDI in the discovery of potential tumor markers in serum samples. The comparison of protein expression profiles from serum appears to provide an effective approach to identifying unique biomarkers for gastric cancer and gastritis. Larger scale studies appear warranted to confirm the
ability of SELDI as a proteomic screen in the clinical setting.
Part II Screening Serum Biomarkers of gastric cancer by Two-dimensional Electrophoresis and Mass Spectrometry
Aim: To study the proteins related to the Gastric carcinoma occurrence and development in the blood plasma of Gastric carcinoma patients. Methods: To establish two-dimensional eletrophoresis profiles with serum protein of patients before or after gastric carcinoma operation and paired normal serum protein.After argent nitrate staining and diferential expression protein spots screening via protein blotched increase and decrease and color shade of 2-DE atlas, we selected these distinction proteins via Agfa DUOSCAN gel scanning and measured the peptides'finger printings after enzymolysis analysis by MALDITOF.Then searched SWISS2PROT data base to screen these distinction proteins. Results: When comparing the sera proteins of these three groups by dimensional gel electrophoresis, we found 5 protein spots in serum protein of patients before gastric carcinoma operation were different significantly from after gastric carcinoma operation or normal group The five proteins undertook situ enzymolysis, we harvested successfully five finger printings via peptides finger printing analysis, the data base searching showed that the five proteins were Serpin B6 (Placental thrombin inhibitor) (Cytoplasmic antiproteinase) (CAP) (Protease inhibitor6) (PI-6) , Septin-I(LARP) (Serologically defined breast cancer antigen NY-BR-24), Kallikrein-6 precursor(Protease M) (Neurosin) (Zyme) (SP59), Hemoglobin beta chain and Beta-defensin 108 precursor (Beta-defensin 8) (DEFB-8). Conclusion: We successfully screened gastric carcinoma related proteins,
these proteins might be symbol protein for gastric carcinoma, so the success of this experiment undoubtedly could serve as a basis for the gastric carcinoma earlier detection and therapy.
Part III Serum proteomic patterns for primary hepatic carcinoma as revealed by SELDI mass spectrometry
Aim: SELDI-TOF mass spectrometry was used to investigate protein expression in sera of patients with Primary hepatic carcinoma (PHC) and hepatocirrhosis compared to normal volunteers. To establish a serum protein pattern model for screening liver cancer, and used it to identify proteomic patterns in serum that distinguish neoplastic from non-neoplastic disease within the PHC. Material: Type protein chip reader (PBS II, Ciphergen Biosystems, Inc., Fremont, USA); Strong anionic exchanger (SAX2) Chips (Ciphergen Biosystems, Inc., Fremont, USA); The Biomarker Wizard software (version 3.0, Ciphergen Biosystems, Fremont, CA, USA). Approximately 5 ml of blood was withdrawn via veinipuncture from each patient. PHC patients 40 samples (confirmed by type-B ultrasonic、 CT、AFP mensuration、 pathology), hepatocirrhosis patients 46 samples ( confirmed by liver puncture), healthy volunteers 60 samples. Methods: SELDI-TOF mass spectrometry was used to investigate protein expression in sera of patients with PHC and hepatocirrhosis compared to normal volunteers. A preliminary "training" set of spectra derived from analysis of serum from 40 patients with PHC and 46 patients with hepatocirrhosis and 60 healthy volunteers were analysed by an
iterative searching algorithm that identified a proteomic pattern that completely discriminated cancer from noncancer. Results: A comparison of protein mass spectra obtained from the sera of the PHC group and the healthy volunteer group revealed 68 protein peaks. There were statistical differences between 5 protein peaks located at 4477 Da、8943 Da、5181 Da、8617 Da、 13761 Da; the intensity of this 5 protein peaks from patients with PHC was clearly higher than that of the healthy controls (P<0.05). Using the above profiles, 18 of 20 patients diagnosed pathologically with PHC were correctly identified by SELDI. Sixty of 60 healthy volunteers were correctly identified as normal. The sensitivity of neoplastic identification was 90% (18/20) for patients, whereas the specificity of control verification was 100% (60/60). A comparison of protein mass spectra obtained from the sera of the PHC group and the hepatocirrhosis group revealed 46 protein peaks. There were statistical differences between 3 protein peaks located at 4477 Da、13761 Da、4097 Da; the intensity of the 4477 Da and 13761 Da protein peaks from patients with PHC was clearly higher than that of hepatocirrhosis. Meanwhile, the intensity of the 4097 Da protein peaks from patients with PHC was clearly lower than that of hepatocirrhosis. Using the 4477 Da, we can distinguish PHC from hepatocirrhosis. A comparison of protein mass spectra obtained from the sera of the hepatocirrhosis group and the healthy volunteer group revealed 85 protein peaks. There were statistical differences between 3 protein peaks located at 8943 Da、13761 Da> 4097 Da; the intensity of the 8943 Da、13761 Da protein peaks from patients with hepatocirrhosis was clearly higher than that of the healthy volunteer. Meanwhile, the intensity of the 4097 Da protein peaks from patients with hepatocirrhosis was clearly lower than that of the
healthy volunteer. Using the this 3 protein peaks, we can distinguish hepatocirrhosis from the healthy volunteer. Conclusion: Use the SELDI-TOF mass spectrometry to establish a serum protein pattern model for screening PHC and hepatocirrhosis and the healthy volunteer, and identify proteomic patterns in serum that distinguish PHC from noncancer. The importance of our studies has been to demonstrate the usefulness of SELDI in the discovery of potential tumor markers in serum samples. The comparison of protein expression profiles from serum appears to provide an effective approach to identifying unique biomarkers for PHC and hepatocirrhosis. Larger scale studies appear warranted confirming the ability of SELDI as a proteomic screen in the clinical setting.
引文
1. Parkin D M, M., Bray F J, Pisani F P. Global cancer statistics. CA Cancer J. Clin., 2005, 55: 74-108.
2．项永兵．消化道恶性肿瘤的流行与现状．肿瘤，2002，22(4)：336-339．
3. Yoshitaka H, Shuichi T, Naoko M, Chong J M, Masashi F, Tatsuhiko K, Hiroyuki A. Global gene expression analysis of gastric cancer by oligonucleotide microarrays. Cancer Research, 2002, 62: 233-240.
4. Li P, Zhang X M. Mass spectrometry-based clinical serum proteomics in tumor research: progress in its application, bulletin of the academy of military medical sciences, 2006, 30(1): 91-94.
5. Anderson N L. The human plasma proteome history, character, and diagnostic prospects. Molecular & Cellular Proteomics, 2002, 1: 845-867.
6. Wilkins M R, Pasquali C, Appel R D. From poteins to proteomes: large scale protein identification by two-dimensional electrophoresis and amino acid analysis. Biotechnology, 1996, 14: 61-65.
7. Lee J R, Yamaguchi H, Wang T C, Goldenring J R, Anderson M G. Differential protein analysis of spasomolytic polypeptide expressing metaplasia using Laser capture microdissection and two-dimensional difference gel electrophoresis. Appl Immunohistochem Mol Morphol, 2003, 11:188-193.
8. Guolin Z, WANG S Y, Li Q F, Gao L, Peng X X. Applying Ppoteomic methodologies to analyze the effect of hexamethylene bisacetamide (HMBA) on proliferation and differentiation of human gastric garcinoma BGC-823 cells. International Journal of Biochemistry & Cell Biology, 2004, 36(8): 1613-1623.
9. Pandey A. Proteomics to study genes and genomes. Nature. 2000, 405: 837-846.
10. Molloy M P. Proteomics: Technologies and applications. Briefings in Functional Genomics and Proteomics, 2002, 1(1): 23-39.
11. He Q Y, Leung SY, Yuen S T, Chu K M, Chiu J F. Diverse proteomic alterations in gastric adenocarcinoma. Proteomics, 2004, 4: 3276-3287.
12. Seibert V, Buschmann T, Meuer J. Surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI TOF-MS) and proteinChip technology in proteomics research. Pathol Res Pract, 2004, 200(2): 83-94.
13. Petricoin E F, Hitt B A, Levine P J, Fusaro V A, Steinberg S M, Mills G B, Simone C, Fishman D A, Kohn E C, Liotta L. Use of proteomic patterns in serum to identify ovarian cancer. Lancet, 2002, 16: 572-577.
14. Ebert M P, Wiemer J C, Schulz H U, Reymond M A, Traugott U, Malfertheiner P, Rocken C. Identification of gastric cancer patients by serum protein profiling. J Proteome Res, 2004, 3:1261-126.
15. Song S T, Wang W J, Luo Z W, Xie W F. Detection of serum differential expression protein by SELDI-TOF MS technology in Hepatocellular carcinoma. The Practical Journal of Cancer, 2004, 19(6): 601-604.
16. Palli D. Epidemiology of gastric cancer: an evaluation of available evidence. J Gastroenterol, 2000, 35(12): 84-89.
17．徐飚，王建明．胃癌流行病学研究．中华肿瘤防治杂志，2006，13(1)：1-7．
18. Sun X D, Zhou Y S, Dai X D, Zhang S W, Huang X M, Sun J, Li L D, Lu F Z, Qiao Y L. Analysis of mortality rate of stomach cancer and its trend in twenty years in China. Chinese Journal of Oncology, 2004, 26(1): 4-9.
19. Kelley J. Gastric cancer epidemiology and risk factors. Journal of Clinical Epidemiology, 2003, 56(1): 1-9.
20．杨其贤，吴秀梅，徐克群．血清CA-724、CA-199、CEA及CA-50联检对消化系统肿瘤的临床应用价值分析．放射免疫学杂志，2005，18(2)：112-113．
21. John H W, Paul C. Neoplasms of the stomach. Cancer Medicine. E5th American Cancer Society, 2001, 1355.
22．王琼，张春荣，刘娟，高学硕，万文徽．血清肿瘤标志联合检测在胃癌诊断中的应用．中华肿瘤杂志，2000，22(2)：150．
23. Shevchenko A, Vorm O, Mann M. Mass spectrometric sequencing of proteins from silver-stained polyacrylamide gels. Analytical Chemistry, 1996, 68(5): 850-858.
24. He D C. SELDI protein chip technology and its applications in proteomics. Modern Instruments, 2002 38(1): 1-4.
25. Weinberger S R, Santambien P, Brenac V. Surface-enhanced laser desorption-ionization retentate chromato graphy mass spectrometry (SELDI-RC-MS): A new method for rapid development of process chromatography conditions. J Chromatogr B Analyt Technol Biomed Life Sci, 2002, 782(1-2): 307-316.
26. Zhang L Q, He T, Yu J, Rebecca E. Caffrey, Enrique A. Dalmasso, Fu SY, Thang P, Mei J F, Zhang WY, Lopez P, David D. Contribution of human -defensin 1, 2, and 3 to the anti-HIV-1 activity of CD8 antiviral factor. Science, 2002, 298(5595): 995-1000.
27. Bane T K, Leblanc J F, Lee T D, Riggs AD. DNA affinity capture and protein profiling by SELDI-TOF mass spectrometry: Effect of DNA Methylation. Nucleic Acids Research, 2002, 30(14): 58-69.
28. Wang Y, Guo S T. Progress in the early stage diagnosis of tumor using SELDI-TOF-MS protein chip technique. Cancer Research and Clinic, 2006, 18(1): 60-63.
29. AntoniaV, SavvasM, Patel K, Filippos I, Kondylis, Gong L, Nasim S H and George L, Wright J. Development of a novel proteomic approach for the detection of transitional cell carcinoma of the bladder in urine. American Journal of Pathology, 2001, 158:1491-1502.
30. Adam B L, John W. Davis, Michael D, Clements M A, Cazares L H, Semmes O J, Paul F, YasuiY, Feng Z D, George L. Wright, J. Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Research, 2002, 62:3609-3614.
31. Bandera C A, Ye B, Mok S C. New Technologies for the identification of markers for early detection of ovarian cancer. Gynecologic Oncology and Pathology, 2003, 15(1): 51-55.
32. Cloud P. Paweletz B T, Pennanen M, Tsangaris T, Magnant C, Lance A, Emanuel F, Petricoin Ⅲ. Proteomic patterns of nipple aspirate fluids obtained by SELDI-TOF: potential for new biomarkers to aid in the diagnosis of breast cancer. Disease Markers, 2001, 17(4): 301-307.
33. Melle C, Schimmel B, Bleul A, Kaufmann R, Hommann M, Richter K K, Daffner W, Settmacher U, Claussen U, Eggeling F V. Characterization of pepsinogen C as a potential biomarker for gastric cancer using a histo-proteomic Approach. J. Proteome Res, 2005, 4(5): 1799-1804.
34．裴毅，郭素堂，王清馨，米振国，赵嘉训．LGT蛋白质组对肿瘤患者临床意义的首次观察．肿瘤研究与临床，2005，17(3)：156-158
35. Li H X, Tan S Y. Expression of tumor marker CDw75 and its clinical impact in gastric carcinoma. Chinese journal of cancer, 2003, 22(1): 83-85.
36. Chan A, Chu K M, Lam C M, Kwok E, Leung S Y, Yuen S T, Law S Y K, Hui W M, Lai K C, Wong C Y, Hu H C, Lai C L, Wong J. Soluble E-cadherin is a valid prognostic marker in gastric carcinoma. GUT, 2001, 48(6): 808-811.
37．贾汝梅，张玉印．胃癌与端粒酶活性表达及DNA倍体关系的研究．中国胃肠外科杂志，2000，3(2)：88-91．
38．庄小强，郑杰．C-met蛋白表达与胃粘膜病变的关系及预后意义．中华病理学杂志，2000，29(6)：409-411．
39．吴泰璜，苏忠学，穆庆岭．血管内皮生长因子在胃癌中的表达及其与血管生成和细胞增殖的关系．中华实验外科杂志，2000，17(1)：42-43．
40. Stewart BW, Kleihues P P. World cancer report. 2003.
41. Kensler T W, Chen J G, Groopman J D. Translational strategies for cancer prevention in liver. Nature Reviews. Cancer, 2003, 3(5): 321-329.
42．杨秉辉，任正刚．原发性肝癌诊断标准．中华肝脏病杂志．2000，8(3)：135．
43．田伏洲．肝癌肿瘤标记物．世界华人消化杂志，2000，8(4)：440-441．
44．张晓华．提高肝癌治疗效果的途径．中国普通外科杂志，2002，11(9)：513-514．
45．吴孟超．原发性肝癌外科综合治疗的现状和展望．中华外科杂志，2004，42(1)：13-15．
46. Hashem B, EI-Serag, MD, Andrew C. Risk Factors for the Rising Rates of Primary Liver Cancer in the United States. Arch Intern Med, 2000, 160: 3227-3230.
47. Chen J G, Song XM. An evaluation on incident cases of liver cancer in China. bulletin of Chinese cancer, 2005, 14(1): 28-31.
48．张思维，李连弟．中国1990—1992年原发性肝癌死亡调查分析．中华肿瘤杂志，1999，21(4)：245-249．
49. Poon T C, Johnson P J. Proteome analysis and its impact on the discovery of serological tumor markers. Clinica Chimica Acta, 2001, 313(1): 231-239.
50．冯钜涛，刘银坤，代智，周海君，宋海燕，陆豪杰，汤钏猷．双向电泳-质谱技术筛选肝癌血清标记物．生物化学与生物物理进展，2005，32(7)：673-677．
51. Lehrer S, Ding H, Zhao S., Diamond E J, Holland J F, Stone N N, Droller M J, Stock R G. Putative protein markers in the sera of men with prostatic neoplasms. BJU International, 2003, 92(3): 223.
52. Grizzle W E, Adam B L, Bigbee W L, Conrads T P, Carroll C, Feng Z D, Izbicka E, Jendoubi M, Verma M, Zhang Z, Zou Z Q. Serum protein expression profiling for cancer detection: validation of a SELDI-based approach for prostate cancer. Disease Markers, 2004, 19(4-5): 185-195.
53. Wu S P, Lai H C, Chu T Y, Kuo Y L, Liu H S. SELDI-TOF MS profiling of plasma proteins in ovarian cancer. Taiwanese Journal of Obstetrics & Gynecology, 2006, 45(1): 26-32.
54. Clarke C H, Buckley J A, Fung E T. SELDI-TOF-MS proteomics of breast cancer. Clinical Chemistry and Laboratory Medicine, 2005, 43(12): 1314-1320.
55. Zhang G Q, Pang D. Detection and clinical significance of serum proteomic patterns of breast cancers by surface enhanced laser desorption/ionization time of flight mass spectrometry. Chinese Journal of Oncology, 2006, 28(3): 204-207.
56．胡跃，余捷凯，刘建，郑树．乳腺癌患者血清蛋白质指纹图谱检测及其临床意义．中华检验医学杂志，2004，27(10)：646-648．
57. Lu X B, Wang J X. Application of serum protein fingerprint model in diagnosis of Thyroid cancer. Chinese Journal of Experimental Surgery, 2006, 23(6): 703-705.
58. Wang L, Zheng J Z. The application of surface enhanced laser desorption ionization technique in lung cancer research. International Journal of Respiration, 2006, 26(2): 146-148.
59. Xue W C, Meng D Y. A preliminary study of the serum protein fingerprint in patients with gastric cancer. Chinese Journal of Clinical Oncology, 2006, 33(11): 617-620.
60. LiangY, Fang M R, Li J C, Liu C B, Rudd J A, Kung H F, David TW. Serum proteomic patterns for gastric lesions as revealed by SELDI mass spectrometry Experimental and Molecular Pathology, 2006, 81: 176-180.
61. Cheng L, Tao L, Xiang C Q, Jia X D, Lu Y, Liao P. Preliminary study of serum proteome biomarkers of hypopharyngeal squamous cell carcinoma.National Medical Journal of China, 2006, 86(21): 1484-1488.
62．刘池波，孙灵芬，梁勇．血清蛋白质谱模型在肝硬化诊断中的应用．中华检验医学杂志，2006，29(6)：529-530．
63．刘建，余捷凯，俞学斌，沈宏，胡汛．胶质瘤脑脊液蛋白指纹图质谱仪分析及其在临床诊断中的应用．中华神经外科杂志，2004，20(5)：362-366．
64．吴登龙，关明，刘薇薇，徐月敏，金三宝，金重睿，张炯，吕元．蛋白质芯片技术筛选膀胱癌尿液标记物的研究．中华泌尿外科杂志，2004，25(7)：453-455．
65．黄成．蛋白质芯片SELDI-TOF-MS技术在肿瘤学研究中的应用．国外医学：肿瘤学分册，2004，3P(6)：434-437．
66. Conrads T P, Issaq H J, Veenstra T D. Proteomic patterns as a diagnostic tool for early-stage cancer: A review of its progress to a clinically relevant tool. Molecular Diagnosis, 2004, 8(2): 77-85.
67．宋森涛，王文静，罗治文，谢渭芬．应用SELDI-TOF MS技术分析肝癌血清差异表达蛋白．实用癌症杂志，2004，P9(6)：601-604．
68. Zheng Y H, Feng Kai, Cui Y, Zhang M, Li N, Xu Y Z, Hang J Z. Screening of serum biomarkers in Hepatocelular carcinoma by SELDI technique: A preliminary study. Chin J lab Med, 2005, 28(6): 628-631.
1. Seibert V, Buschmann T. Advances in clinical cancer proteomics: SELDI-TOF-mass spectrometry and biomarker discovery. Briefings in Functional Genomics and Proteomics, 2005, 4(1): 16-26.
2. Liang Y, Fang M R, Li J C, Liu C B, Rudd JA, Kung HF, Divid T W Yew. Serum proteomic patterns for gastric lesions as revealed by SELDI mass spectrometry. Experimental and Molecular Pathology, 2006, 81(2):176-80.
3. Grossklaus D J, Shappell S B, Coffey C S, Chang S S, Cookson M S. The free/total prostate-specific antigen ratio (%fPSA) is the best predictor of tumor involvement in the radical prostatectomy specimen among men with an elevated PSA. Urol Oncol, 2002, 7(5): 195-8.
4. Morita S J, FukushimaY, MorimotoT, Hiraoka N, Shibata N. Does serum CA19-9 play a practical role in the management of patients with colorectal cancer. Diseases of the Colon & Retum, 2004, 47(2): 227-232.
5. Gion M, Leon A E, Dittadi R. Comparison of the diagnostic accuracy of CA27.29 and CA15.3 in primary breast cancer. Clinical Chemistry, 1999, 45:630-637.
6. Jacobs I J, Skates S J, MacDonald N. Screening for ovarian cancer: a pilot randomized controlled trial. Lancet, 1999, 353:1207-1210.
7. Petricoin E F. Proteomic approaches in cancer risk and response assessment. Trends Mol Med, 2004, 10(2): 59-64.
8. Tyers M, Mann M. From genomics to proteomics. Nature, 2003, 422: 193-197.
9. MacBeath G. Protein microarrays and proteomics. Nature Genetics, 2002, 32:526-532.
10. Figeys D. Adapting arrays and lab-on-a-chip technology for proteomics. Proteomics, 2002, 2(4): 373 - 382.
11. Aebersold R,Mann M. Mass spectrometry-based proteomics. Nature, 2003, 422: 198-207.
12. Weinberger S R, Fung E T. Current achievements using ProteinChip Array technology. Curr Opin Chem Biol, 2002, 6(1): 86-91.
13. Tang N, Weinberger S R. Current developments in SELDI affinity technology. Mass Spectrometry Reviews, 2004, 23(1): 34 - 44.
14. Seibert V, Buschmann T, Meuer J. Surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI TOF-MS) and ProteinChip technology in proteomics research. Pathol Res Pract, 2004, 200(2): 83-94.
15. Seibert V, Buschmann T. Advances in clinical cancer proteomics: SELDI-TOF-mass spectrometry and biomarker discovery. Briefings in Functional Genomics and Proteomics, 2005, 4(1): 16-26.
16. Blackstock W P, Weir M P. Proteomics: quantitative and physical mapping of cellular proteins. Trends Biotechnol. 1999, 17(3): 121-7.
17. N. Leigh Anderson, Anderson N G. The human plasma proteome history, character, and diagnostic prospects. Molecular & Cellular Proteomics, 2002, 1:845-867.
18. Li P, Wang H X, Zhang X M. Mass spectrometry-based clinical serum proteomics in tumor research: progress in its application. Bulletin of the academy of military medical science, 2006, 30(1): 91-94.
19. Wulfkuhle J, Espina V, Liotta L, Petricoin E. Genomic and proteomic technologies for individualisation and improvement of cancer treatment. European Journal of Cancer, 2004, 40:2623-2632.
20. Wilkins M R, Pasquali C, Appel R D. From proteins to proteomes: Large scale protein identification by two-dimensional electrophoresis and amino acid analysis. Biotechnology, 1996, 14:61-65.
21. Molloy M P, Witzmann FA. Proteomics: technologies and applications. Briefings in Functional Genomics and Proteomics, 2002, 1(1): 23-39.
22. Pandey A, Mann M. Proteomics to study genes and genomes. Nature, 2000, 405: 837-846.
23. Yates. Mass spectrometry and the age of the proteome. J. Mass Spectrom 1998, 33: 1-19.
24. Yuen H, Albrecht G, HeilbutA, Bader G D, Moore L, Adams S L, Millar A, Taylor P, Hogue C W, Figeys D, Tyers M. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature, 2002, 415(6868): 180-183.
25．刘建，郑树．蛋白质组学在寻找肿瘤生物标记物中的技术与应用．实用肿瘤杂志，2004，19(6)：547-551．
26. Kang X X, Xu Y, Wu X Y, Liang Y, Wang C, Guo J H, Song Y Z, Zhang L Q, Suo F S, Chen T Y, Huang Z Y, Zhao Y Z, Lu H, Pan C Q, Tang H. Proteomic fingerprints for potential application to early diagnosis of severe acute respiratory syndrome. Clinical Chemistry, 2005, 51: 56-64.
27. Steven P, Gygi, B R, Timothy J G, Jimmy E, Ruedi A. proteome analysis of low-abundance proteins using multidimensional chromatography and isotope-coded affinity tags. journal of proteome research, 2002, 1(1): 47-54.
28. Regnier F, Huang G. Future potential of targeted component analysis by multidimensional liquid chromatography-mass spectrometry. J. Chromatogr A. 1996, 750(1-2): 3-10.
29. Wiesner A. Detection of tumor markers with proteinchip technology. Current Pharmaceutical Biotechnology, 2004, 5(1): 45-67.
30. Edward R, SauterS S, John E, Hewett, Speckman P, Garrett C, Bois D. Early Detection and Diagnosis Proteomic analysis of nipple aspirate fluid using SELDI-TOF-MS. International Journal of Cancer, 2005, 114(5): 791-796.
31. Petricoin III E, Ardekani A, HittB, Levine P, FusaroV, Steinberg S, Mills G, Simone C, Fishman D, Kohn E. Use of proteomic patterns in serum to identify ovarian cancer. The Lancet, 2002, 359(9306): 572-577.
32. Zhang Z, Robert C, Yu Y H, Li J N, Lori J, Sokoll, Alex J R, Jason M R, Cameron B, Wang Y Y, Fung E T, Chan D W. Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer. cancer research, 2004, 64: 5882-5890.
33. Wang Y, Wang J F, Guo S T. Progress in the early stage diagnosis of tumor using SELDI-TOF-MS protein chip technique. Cancer research and clinic, 2006, 18(1): 60-63.
34. Petricoin E F, Ornstein D K, Paweletz C P, Ardekani A. Serum proteomic patterns for detection of prostate cancer. Journal of the National Cancer Institute, 2002, 94(20): 1576-1578.
35. Bandera, Christina A, Bin Y, Mok, Samuel C. New technologies for the identification of markers for early detection of ovarian cancer. Gynecologic oncology and pathology. Current Opinion in Obstetrics & Gynecology, 2003, 15(1): 51-55.
36. Katherine R K, Malaika W A, Suzanne M P, Su F, Luong M N, Luong S A, Reddy S T, Robin F E. Identification of biomarkers for ovarian cancer using strong anion-exchange ProteinChips: Potential use in diagnosis and prognosis. Medical sciences , 2003 , 100(21): 12343-12348.
37. Petricoin E, Ardekani A., Hitt B, Levine P, Fusaro V, Steinberg S, Mills G, Simone C, Fishman D, Kohn E. Use of proteomic patterns in serum to identify ovarian cancer. The Lancet, 9306(359): 572-577.
38. Wu S P, Lin Y W, Lai H C, Chu T Y, Kuo Y L, Liu H S. SELDI-TOF-MS Profiling of plasma proteins in ovarian cancer. Taiwanese Journal of Obstetrics & Gynecology. 2006, 45(1): 26-32.
39. Matthias P A E, Meuer J, Jan C W, Schulz H U, Reymond M A, Traugott U, Malfertheiner P, Rocken C. Identification of gastric cancer patients by serum protein profiling. J. Proteome Res, 2004, 3(6): 1261-1266.
40. Cadieux P A, Darren T, Watterson J D, Burton J P, Howard J C, Knudsen B E, Gan B S, John K M, Chambers A F, Denstedt J D. Surface-enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS): A new proteomic urinary test for patients with urolithiasis. Journal of Clinical Laboratory Analysis, 2004, 3(18): 170-175.
41. Xue W C, Feng k, Meng D Y. Apreliminary study of the serum protein fingerprint in patients with gastric cancer, hinese Journal of clinical oncology, 2006, 33(11): 617-620.
42．宋森涛，王文静，罗治文，谢渭汾．应用SELDI-TOF-MS技术分析肝癌血清差异表达蛋白．实用癌症杂志，2004，19(6)．601-604．
43. ClarkeC H, Buckley J, Fung E T. SELDI-TOF-MS proteomics of breast cancer. Clinical chemistry and laboratory medicine. 2005, 43(12):1314-1320.
44. Zhang G Q, Du J, Pang D. Detection and clinical significance of serum proteomic patterns of breast cancers by surface enhanced laser desorption/ionization time of flight mass spectrometry. Chinese Journal of clinical oncology, 2006, 28(3): 204-207.
45. Li J N, Zhang Z, Rosenzweig J, Wang Y Y and Chan D W. Proteomics and Bioinformatics Approaches for identification of serum biomarkers to detect breast cancer. Clinical Chemistry, 2002, 48:1296-1304.
46. Lin D, Lay J O, Bryant M S, Malaveille C, Friesen M, Bartsch H, Lang N P and Kadlubar F F. Analysis of 4-aminobiphenyl-DNA adducts in human urinary bladder and lung by alkaline hydrolysis and negative ion gas chromatography-mass spectrometry. Environ Health Perspect.,1994, 10(102):11-16.
47. Lu X B, Wang L, Wang J X. Application of serum protein fingerprint model in diagnosis of thyroid cancer. Chinese journal of experimental surgery, 2006, 23(6): 703-705.
48. Lehrer S, Roboz J, Ding H, Zhao S, Diamond E J, Holland J F, Stone N N, Droller M J, Stock R G. Putative protein markers in the sera of men with prostatic neoplasms. BJU International, 2003, 92(3): 223.
49. Grizzle W E, Bigbee W L, Srivastava S, Thompsonl M, Thornquist M D, Verma M, Zhang Z, Zou Z Q. Serum protein expression profiling for cancer detection: Validation of a SELDl-based approach for prostate cancer. Disease Markers, 2004, 19(4-5): 185-195.
50．闫志勇，钱冬萌，丁守怡，宋旭霞．应用SELDI—TOF—MS 技术建立直肠癌筛选血清蛋白质指纹图谱模型．世界华人消化杂志，2005，13(19)：2395-2398．
51. Wang L, Du Y C, Zheng J Z. The application of surface enhanced laser desorption ionization technique in lung cancer research. International Journal of Respiration, 2006, 26(2): 146-148.
52. Cheng L, Zhou L, Tao L, Xiang C Q, Jia X D, Lu Y, Liao P. Preliminary study of serum proteome biomarkers of hypopharyngeal squamous cell carcinoma. National medical joural of Chia, 2006, 86(21): 1484-1488.
53. Sarah A S, Robert J W, Reid C T, Yu S, Jason H M, Steven A T, Mahlon D J and Richard M C. Proteomic-based prognosis of brain tumor patients using direct-tissue matrix-assisted laser desorption ionization mass spectrometry. Cancer Research, 2005, 65(9):7674-7681.
54．刘池波，潘春琴，孙灵芬，梁勇．血清蛋白质谱模型在肝硬化诊断中的应用．中华检验医学杂志，2006，29(6)：529-530．
55. Wiemer J C, Prokudin A. Bioinformatics in proteomics: application, terminology, and pitfalls. Pathol Res Pract, 2004, 200(2): 173-8.
56. Diamandis, E.leftherios P. Proteomic patterns in biological fluids: do they represent the future of cancer diagnostics? Clinical Chemistry, 2003, 49: 1272-1275.
57. Qu Y s, Yutaka Y, Michael D W, Lisa H C, Schellhammer P F, Feng Z d, Semmes O J, Wright G L. Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. Clinical Chemistry, 2002, 48: 1835-1843.
58. Wright G L, Cazares L H, Leung S M, Nasim S, Adam B L, Yip T T, Schellhammer P F, Gong L and VlahouA. Proteinchip surface enhanced laser desorptionionization (SELDI) mass spectrometry: a novel protein biochip technology for detection of prostate cancer biomarkers in complex protein mixtures. Nature, 1999, 5(2): 264-276.
2．项永兵．消化道恶性肿瘤的流行与现状．肿瘤，2002，22(4)：336-339．
3. Yoshitaka H, Shuichi T, Naoko M, Chong J M, Masashi F, Tatsuhiko K, Hiroyuki A. Global gene expression analysis of gastric cancer by oligonucleotide microarrays. Cancer Research, 2002, 62: 233-240.
4. Li P, Zhang X M. Mass spectrometry-based clinical serum proteomics in tumor research: progress in its application, bulletin of the academy of military medical sciences, 2006, 30(1): 91-94.
5. Anderson N L. The human plasma proteome history, character, and diagnostic prospects. Molecular & Cellular Proteomics, 2002, 1: 845-867.
6. Wilkins M R, Pasquali C, Appel R D. From poteins to proteomes: large scale protein identification by two-dimensional electrophoresis and amino acid analysis. Biotechnology, 1996, 14: 61-65.
7. Lee J R, Yamaguchi H, Wang T C, Goldenring J R, Anderson M G. Differential protein analysis of spasomolytic polypeptide expressing metaplasia using Laser capture microdissection and two-dimensional difference gel electrophoresis. Appl Immunohistochem Mol Morphol, 2003, 11:188-193.
8. Guolin Z, WANG S Y, Li Q F, Gao L, Peng X X. Applying Ppoteomic methodologies to analyze the effect of hexamethylene bisacetamide (HMBA) on proliferation and differentiation of human gastric garcinoma BGC-823 cells. International Journal of Biochemistry & Cell Biology, 2004, 36(8): 1613-1623.
9. Pandey A. Proteomics to study genes and genomes. Nature. 2000, 405: 837-846.
10. Molloy M P. Proteomics: Technologies and applications. Briefings in Functional Genomics and Proteomics, 2002, 1(1): 23-39.
11. He Q Y, Leung SY, Yuen S T, Chu K M, Chiu J F. Diverse proteomic alterations in gastric adenocarcinoma. Proteomics, 2004, 4: 3276-3287.
12. Seibert V, Buschmann T, Meuer J. Surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI TOF-MS) and proteinChip technology in proteomics research. Pathol Res Pract, 2004, 200(2): 83-94.
13. Petricoin E F, Hitt B A, Levine P J, Fusaro V A, Steinberg S M, Mills G B, Simone C, Fishman D A, Kohn E C, Liotta L. Use of proteomic patterns in serum to identify ovarian cancer. Lancet, 2002, 16: 572-577.
14. Ebert M P, Wiemer J C, Schulz H U, Reymond M A, Traugott U, Malfertheiner P, Rocken C. Identification of gastric cancer patients by serum protein profiling. J Proteome Res, 2004, 3:1261-126.
15. Song S T, Wang W J, Luo Z W, Xie W F. Detection of serum differential expression protein by SELDI-TOF MS technology in Hepatocellular carcinoma. The Practical Journal of Cancer, 2004, 19(6): 601-604.
16. Palli D. Epidemiology of gastric cancer: an evaluation of available evidence. J Gastroenterol, 2000, 35(12): 84-89.
17．徐飚，王建明．胃癌流行病学研究．中华肿瘤防治杂志，2006，13(1)：1-7．
18. Sun X D, Zhou Y S, Dai X D, Zhang S W, Huang X M, Sun J, Li L D, Lu F Z, Qiao Y L. Analysis of mortality rate of stomach cancer and its trend in twenty years in China. Chinese Journal of Oncology, 2004, 26(1): 4-9.
19. Kelley J. Gastric cancer epidemiology and risk factors. Journal of Clinical Epidemiology, 2003, 56(1): 1-9.
20．杨其贤，吴秀梅，徐克群．血清CA-724、CA-199、CEA及CA-50联检对消化系统肿瘤的临床应用价值分析．放射免疫学杂志，2005，18(2)：112-113．
21. John H W, Paul C. Neoplasms of the stomach. Cancer Medicine. E5th American Cancer Society, 2001, 1355.
22．王琼，张春荣，刘娟，高学硕，万文徽．血清肿瘤标志联合检测在胃癌诊断中的应用．中华肿瘤杂志，2000，22(2)：150．
23. Shevchenko A, Vorm O, Mann M. Mass spectrometric sequencing of proteins from silver-stained polyacrylamide gels. Analytical Chemistry, 1996, 68(5): 850-858.
24. He D C. SELDI protein chip technology and its applications in proteomics. Modern Instruments, 2002 38(1): 1-4.
25. Weinberger S R, Santambien P, Brenac V. Surface-enhanced laser desorption-ionization retentate chromato graphy mass spectrometry (SELDI-RC-MS): A new method for rapid development of process chromatography conditions. J Chromatogr B Analyt Technol Biomed Life Sci, 2002, 782(1-2): 307-316.
26. Zhang L Q, He T, Yu J, Rebecca E. Caffrey, Enrique A. Dalmasso, Fu SY, Thang P, Mei J F, Zhang WY, Lopez P, David D. Contribution of human -defensin 1, 2, and 3 to the anti-HIV-1 activity of CD8 antiviral factor. Science, 2002, 298(5595): 995-1000.
27. Bane T K, Leblanc J F, Lee T D, Riggs AD. DNA affinity capture and protein profiling by SELDI-TOF mass spectrometry: Effect of DNA Methylation. Nucleic Acids Research, 2002, 30(14): 58-69.
28. Wang Y, Guo S T. Progress in the early stage diagnosis of tumor using SELDI-TOF-MS protein chip technique. Cancer Research and Clinic, 2006, 18(1): 60-63.
29. AntoniaV, SavvasM, Patel K, Filippos I, Kondylis, Gong L, Nasim S H and George L, Wright J. Development of a novel proteomic approach for the detection of transitional cell carcinoma of the bladder in urine. American Journal of Pathology, 2001, 158:1491-1502.
30. Adam B L, John W. Davis, Michael D, Clements M A, Cazares L H, Semmes O J, Paul F, YasuiY, Feng Z D, George L. Wright, J. Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Research, 2002, 62:3609-3614.
31. Bandera C A, Ye B, Mok S C. New Technologies for the identification of markers for early detection of ovarian cancer. Gynecologic Oncology and Pathology, 2003, 15(1): 51-55.
32. Cloud P. Paweletz B T, Pennanen M, Tsangaris T, Magnant C, Lance A, Emanuel F, Petricoin Ⅲ. Proteomic patterns of nipple aspirate fluids obtained by SELDI-TOF: potential for new biomarkers to aid in the diagnosis of breast cancer. Disease Markers, 2001, 17(4): 301-307.
33. Melle C, Schimmel B, Bleul A, Kaufmann R, Hommann M, Richter K K, Daffner W, Settmacher U, Claussen U, Eggeling F V. Characterization of pepsinogen C as a potential biomarker for gastric cancer using a histo-proteomic Approach. J. Proteome Res, 2005, 4(5): 1799-1804.
34．裴毅，郭素堂，王清馨，米振国，赵嘉训．LGT蛋白质组对肿瘤患者临床意义的首次观察．肿瘤研究与临床，2005，17(3)：156-158
35. Li H X, Tan S Y. Expression of tumor marker CDw75 and its clinical impact in gastric carcinoma. Chinese journal of cancer, 2003, 22(1): 83-85.
36. Chan A, Chu K M, Lam C M, Kwok E, Leung S Y, Yuen S T, Law S Y K, Hui W M, Lai K C, Wong C Y, Hu H C, Lai C L, Wong J. Soluble E-cadherin is a valid prognostic marker in gastric carcinoma. GUT, 2001, 48(6): 808-811.
37．贾汝梅，张玉印．胃癌与端粒酶活性表达及DNA倍体关系的研究．中国胃肠外科杂志，2000，3(2)：88-91．
38．庄小强，郑杰．C-met蛋白表达与胃粘膜病变的关系及预后意义．中华病理学杂志，2000，29(6)：409-411．
39．吴泰璜，苏忠学，穆庆岭．血管内皮生长因子在胃癌中的表达及其与血管生成和细胞增殖的关系．中华实验外科杂志，2000，17(1)：42-43．
40. Stewart BW, Kleihues P P. World cancer report. 2003.
41. Kensler T W, Chen J G, Groopman J D. Translational strategies for cancer prevention in liver. Nature Reviews. Cancer, 2003, 3(5): 321-329.
42．杨秉辉，任正刚．原发性肝癌诊断标准．中华肝脏病杂志．2000，8(3)：135．
43．田伏洲．肝癌肿瘤标记物．世界华人消化杂志，2000，8(4)：440-441．
44．张晓华．提高肝癌治疗效果的途径．中国普通外科杂志，2002，11(9)：513-514．
45．吴孟超．原发性肝癌外科综合治疗的现状和展望．中华外科杂志，2004，42(1)：13-15．
46. Hashem B, EI-Serag, MD, Andrew C. Risk Factors for the Rising Rates of Primary Liver Cancer in the United States. Arch Intern Med, 2000, 160: 3227-3230.
47. Chen J G, Song XM. An evaluation on incident cases of liver cancer in China. bulletin of Chinese cancer, 2005, 14(1): 28-31.
48．张思维，李连弟．中国1990—1992年原发性肝癌死亡调查分析．中华肿瘤杂志，1999，21(4)：245-249．
49. Poon T C, Johnson P J. Proteome analysis and its impact on the discovery of serological tumor markers. Clinica Chimica Acta, 2001, 313(1): 231-239.
50．冯钜涛，刘银坤，代智，周海君，宋海燕，陆豪杰，汤钏猷．双向电泳-质谱技术筛选肝癌血清标记物．生物化学与生物物理进展，2005，32(7)：673-677．
51. Lehrer S, Ding H, Zhao S., Diamond E J, Holland J F, Stone N N, Droller M J, Stock R G. Putative protein markers in the sera of men with prostatic neoplasms. BJU International, 2003, 92(3): 223.
52. Grizzle W E, Adam B L, Bigbee W L, Conrads T P, Carroll C, Feng Z D, Izbicka E, Jendoubi M, Verma M, Zhang Z, Zou Z Q. Serum protein expression profiling for cancer detection: validation of a SELDI-based approach for prostate cancer. Disease Markers, 2004, 19(4-5): 185-195.
53. Wu S P, Lai H C, Chu T Y, Kuo Y L, Liu H S. SELDI-TOF MS profiling of plasma proteins in ovarian cancer. Taiwanese Journal of Obstetrics & Gynecology, 2006, 45(1): 26-32.
54. Clarke C H, Buckley J A, Fung E T. SELDI-TOF-MS proteomics of breast cancer. Clinical Chemistry and Laboratory Medicine, 2005, 43(12): 1314-1320.
55. Zhang G Q, Pang D. Detection and clinical significance of serum proteomic patterns of breast cancers by surface enhanced laser desorption/ionization time of flight mass spectrometry. Chinese Journal of Oncology, 2006, 28(3): 204-207.
56．胡跃，余捷凯，刘建，郑树．乳腺癌患者血清蛋白质指纹图谱检测及其临床意义．中华检验医学杂志，2004，27(10)：646-648．
57. Lu X B, Wang J X. Application of serum protein fingerprint model in diagnosis of Thyroid cancer. Chinese Journal of Experimental Surgery, 2006, 23(6): 703-705.
58. Wang L, Zheng J Z. The application of surface enhanced laser desorption ionization technique in lung cancer research. International Journal of Respiration, 2006, 26(2): 146-148.
59. Xue W C, Meng D Y. A preliminary study of the serum protein fingerprint in patients with gastric cancer. Chinese Journal of Clinical Oncology, 2006, 33(11): 617-620.
60. LiangY, Fang M R, Li J C, Liu C B, Rudd J A, Kung H F, David TW. Serum proteomic patterns for gastric lesions as revealed by SELDI mass spectrometry Experimental and Molecular Pathology, 2006, 81: 176-180.
61. Cheng L, Tao L, Xiang C Q, Jia X D, Lu Y, Liao P. Preliminary study of serum proteome biomarkers of hypopharyngeal squamous cell carcinoma.National Medical Journal of China, 2006, 86(21): 1484-1488.
62．刘池波，孙灵芬，梁勇．血清蛋白质谱模型在肝硬化诊断中的应用．中华检验医学杂志，2006，29(6)：529-530．
63．刘建，余捷凯，俞学斌，沈宏，胡汛．胶质瘤脑脊液蛋白指纹图质谱仪分析及其在临床诊断中的应用．中华神经外科杂志，2004，20(5)：362-366．
64．吴登龙，关明，刘薇薇，徐月敏，金三宝，金重睿，张炯，吕元．蛋白质芯片技术筛选膀胱癌尿液标记物的研究．中华泌尿外科杂志，2004，25(7)：453-455．
65．黄成．蛋白质芯片SELDI-TOF-MS技术在肿瘤学研究中的应用．国外医学：肿瘤学分册，2004，3P(6)：434-437．
66. Conrads T P, Issaq H J, Veenstra T D. Proteomic patterns as a diagnostic tool for early-stage cancer: A review of its progress to a clinically relevant tool. Molecular Diagnosis, 2004, 8(2): 77-85.
67．宋森涛，王文静，罗治文，谢渭芬．应用SELDI-TOF MS技术分析肝癌血清差异表达蛋白．实用癌症杂志，2004，P9(6)：601-604．
68. Zheng Y H, Feng Kai, Cui Y, Zhang M, Li N, Xu Y Z, Hang J Z. Screening of serum biomarkers in Hepatocelular carcinoma by SELDI technique: A preliminary study. Chin J lab Med, 2005, 28(6): 628-631.
1. Seibert V, Buschmann T. Advances in clinical cancer proteomics: SELDI-TOF-mass spectrometry and biomarker discovery. Briefings in Functional Genomics and Proteomics, 2005, 4(1): 16-26.
2. Liang Y, Fang M R, Li J C, Liu C B, Rudd JA, Kung HF, Divid T W Yew. Serum proteomic patterns for gastric lesions as revealed by SELDI mass spectrometry. Experimental and Molecular Pathology, 2006, 81(2):176-80.
3. Grossklaus D J, Shappell S B, Coffey C S, Chang S S, Cookson M S. The free/total prostate-specific antigen ratio (%fPSA) is the best predictor of tumor involvement in the radical prostatectomy specimen among men with an elevated PSA. Urol Oncol, 2002, 7(5): 195-8.
4. Morita S J, FukushimaY, MorimotoT, Hiraoka N, Shibata N. Does serum CA19-9 play a practical role in the management of patients with colorectal cancer. Diseases of the Colon & Retum, 2004, 47(2): 227-232.
5. Gion M, Leon A E, Dittadi R. Comparison of the diagnostic accuracy of CA27.29 and CA15.3 in primary breast cancer. Clinical Chemistry, 1999, 45:630-637.
6. Jacobs I J, Skates S J, MacDonald N. Screening for ovarian cancer: a pilot randomized controlled trial. Lancet, 1999, 353:1207-1210.
7. Petricoin E F. Proteomic approaches in cancer risk and response assessment. Trends Mol Med, 2004, 10(2): 59-64.
8. Tyers M, Mann M. From genomics to proteomics. Nature, 2003, 422: 193-197.
9. MacBeath G. Protein microarrays and proteomics. Nature Genetics, 2002, 32:526-532.
10. Figeys D. Adapting arrays and lab-on-a-chip technology for proteomics. Proteomics, 2002, 2(4): 373 - 382.
11. Aebersold R,Mann M. Mass spectrometry-based proteomics. Nature, 2003, 422: 198-207.
12. Weinberger S R, Fung E T. Current achievements using ProteinChip Array technology. Curr Opin Chem Biol, 2002, 6(1): 86-91.
13. Tang N, Weinberger S R. Current developments in SELDI affinity technology. Mass Spectrometry Reviews, 2004, 23(1): 34 - 44.
14. Seibert V, Buschmann T, Meuer J. Surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI TOF-MS) and ProteinChip technology in proteomics research. Pathol Res Pract, 2004, 200(2): 83-94.
15. Seibert V, Buschmann T. Advances in clinical cancer proteomics: SELDI-TOF-mass spectrometry and biomarker discovery. Briefings in Functional Genomics and Proteomics, 2005, 4(1): 16-26.
16. Blackstock W P, Weir M P. Proteomics: quantitative and physical mapping of cellular proteins. Trends Biotechnol. 1999, 17(3): 121-7.
17. N. Leigh Anderson, Anderson N G. The human plasma proteome history, character, and diagnostic prospects. Molecular & Cellular Proteomics, 2002, 1:845-867.
18. Li P, Wang H X, Zhang X M. Mass spectrometry-based clinical serum proteomics in tumor research: progress in its application. Bulletin of the academy of military medical science, 2006, 30(1): 91-94.
19. Wulfkuhle J, Espina V, Liotta L, Petricoin E. Genomic and proteomic technologies for individualisation and improvement of cancer treatment. European Journal of Cancer, 2004, 40:2623-2632.
20. Wilkins M R, Pasquali C, Appel R D. From proteins to proteomes: Large scale protein identification by two-dimensional electrophoresis and amino acid analysis. Biotechnology, 1996, 14:61-65.
21. Molloy M P, Witzmann FA. Proteomics: technologies and applications. Briefings in Functional Genomics and Proteomics, 2002, 1(1): 23-39.
22. Pandey A, Mann M. Proteomics to study genes and genomes. Nature, 2000, 405: 837-846.
23. Yates. Mass spectrometry and the age of the proteome. J. Mass Spectrom 1998, 33: 1-19.
24. Yuen H, Albrecht G, HeilbutA, Bader G D, Moore L, Adams S L, Millar A, Taylor P, Hogue C W, Figeys D, Tyers M. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature, 2002, 415(6868): 180-183.
25．刘建，郑树．蛋白质组学在寻找肿瘤生物标记物中的技术与应用．实用肿瘤杂志，2004，19(6)：547-551．
26. Kang X X, Xu Y, Wu X Y, Liang Y, Wang C, Guo J H, Song Y Z, Zhang L Q, Suo F S, Chen T Y, Huang Z Y, Zhao Y Z, Lu H, Pan C Q, Tang H. Proteomic fingerprints for potential application to early diagnosis of severe acute respiratory syndrome. Clinical Chemistry, 2005, 51: 56-64.
27. Steven P, Gygi, B R, Timothy J G, Jimmy E, Ruedi A. proteome analysis of low-abundance proteins using multidimensional chromatography and isotope-coded affinity tags. journal of proteome research, 2002, 1(1): 47-54.
28. Regnier F, Huang G. Future potential of targeted component analysis by multidimensional liquid chromatography-mass spectrometry. J. Chromatogr A. 1996, 750(1-2): 3-10.
29. Wiesner A. Detection of tumor markers with proteinchip technology. Current Pharmaceutical Biotechnology, 2004, 5(1): 45-67.
30. Edward R, SauterS S, John E, Hewett, Speckman P, Garrett C, Bois D. Early Detection and Diagnosis Proteomic analysis of nipple aspirate fluid using SELDI-TOF-MS. International Journal of Cancer, 2005, 114(5): 791-796.
31. Petricoin III E, Ardekani A, HittB, Levine P, FusaroV, Steinberg S, Mills G, Simone C, Fishman D, Kohn E. Use of proteomic patterns in serum to identify ovarian cancer. The Lancet, 2002, 359(9306): 572-577.
32. Zhang Z, Robert C, Yu Y H, Li J N, Lori J, Sokoll, Alex J R, Jason M R, Cameron B, Wang Y Y, Fung E T, Chan D W. Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer. cancer research, 2004, 64: 5882-5890.
33. Wang Y, Wang J F, Guo S T. Progress in the early stage diagnosis of tumor using SELDI-TOF-MS protein chip technique. Cancer research and clinic, 2006, 18(1): 60-63.
34. Petricoin E F, Ornstein D K, Paweletz C P, Ardekani A. Serum proteomic patterns for detection of prostate cancer. Journal of the National Cancer Institute, 2002, 94(20): 1576-1578.
35. Bandera, Christina A, Bin Y, Mok, Samuel C. New technologies for the identification of markers for early detection of ovarian cancer. Gynecologic oncology and pathology. Current Opinion in Obstetrics & Gynecology, 2003, 15(1): 51-55.
36. Katherine R K, Malaika W A, Suzanne M P, Su F, Luong M N, Luong S A, Reddy S T, Robin F E. Identification of biomarkers for ovarian cancer using strong anion-exchange ProteinChips: Potential use in diagnosis and prognosis. Medical sciences , 2003 , 100(21): 12343-12348.
37. Petricoin E, Ardekani A., Hitt B, Levine P, Fusaro V, Steinberg S, Mills G, Simone C, Fishman D, Kohn E. Use of proteomic patterns in serum to identify ovarian cancer. The Lancet, 9306(359): 572-577.
38. Wu S P, Lin Y W, Lai H C, Chu T Y, Kuo Y L, Liu H S. SELDI-TOF-MS Profiling of plasma proteins in ovarian cancer. Taiwanese Journal of Obstetrics & Gynecology. 2006, 45(1): 26-32.
39. Matthias P A E, Meuer J, Jan C W, Schulz H U, Reymond M A, Traugott U, Malfertheiner P, Rocken C. Identification of gastric cancer patients by serum protein profiling. J. Proteome Res, 2004, 3(6): 1261-1266.
40. Cadieux P A, Darren T, Watterson J D, Burton J P, Howard J C, Knudsen B E, Gan B S, John K M, Chambers A F, Denstedt J D. Surface-enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS): A new proteomic urinary test for patients with urolithiasis. Journal of Clinical Laboratory Analysis, 2004, 3(18): 170-175.
41. Xue W C, Feng k, Meng D Y. Apreliminary study of the serum protein fingerprint in patients with gastric cancer, hinese Journal of clinical oncology, 2006, 33(11): 617-620.
42．宋森涛，王文静，罗治文，谢渭汾．应用SELDI-TOF-MS技术分析肝癌血清差异表达蛋白．实用癌症杂志，2004，19(6)．601-604．
43. ClarkeC H, Buckley J, Fung E T. SELDI-TOF-MS proteomics of breast cancer. Clinical chemistry and laboratory medicine. 2005, 43(12):1314-1320.
44. Zhang G Q, Du J, Pang D. Detection and clinical significance of serum proteomic patterns of breast cancers by surface enhanced laser desorption/ionization time of flight mass spectrometry. Chinese Journal of clinical oncology, 2006, 28(3): 204-207.
45. Li J N, Zhang Z, Rosenzweig J, Wang Y Y and Chan D W. Proteomics and Bioinformatics Approaches for identification of serum biomarkers to detect breast cancer. Clinical Chemistry, 2002, 48:1296-1304.
46. Lin D, Lay J O, Bryant M S, Malaveille C, Friesen M, Bartsch H, Lang N P and Kadlubar F F. Analysis of 4-aminobiphenyl-DNA adducts in human urinary bladder and lung by alkaline hydrolysis and negative ion gas chromatography-mass spectrometry. Environ Health Perspect.,1994, 10(102):11-16.
47. Lu X B, Wang L, Wang J X. Application of serum protein fingerprint model in diagnosis of thyroid cancer. Chinese journal of experimental surgery, 2006, 23(6): 703-705.
48. Lehrer S, Roboz J, Ding H, Zhao S, Diamond E J, Holland J F, Stone N N, Droller M J, Stock R G. Putative protein markers in the sera of men with prostatic neoplasms. BJU International, 2003, 92(3): 223.
49. Grizzle W E, Bigbee W L, Srivastava S, Thompsonl M, Thornquist M D, Verma M, Zhang Z, Zou Z Q. Serum protein expression profiling for cancer detection: Validation of a SELDl-based approach for prostate cancer. Disease Markers, 2004, 19(4-5): 185-195.
50．闫志勇，钱冬萌，丁守怡，宋旭霞．应用SELDI—TOF—MS 技术建立直肠癌筛选血清蛋白质指纹图谱模型．世界华人消化杂志，2005，13(19)：2395-2398．
51. Wang L, Du Y C, Zheng J Z. The application of surface enhanced laser desorption ionization technique in lung cancer research. International Journal of Respiration, 2006, 26(2): 146-148.
52. Cheng L, Zhou L, Tao L, Xiang C Q, Jia X D, Lu Y, Liao P. Preliminary study of serum proteome biomarkers of hypopharyngeal squamous cell carcinoma. National medical joural of Chia, 2006, 86(21): 1484-1488.
53. Sarah A S, Robert J W, Reid C T, Yu S, Jason H M, Steven A T, Mahlon D J and Richard M C. Proteomic-based prognosis of brain tumor patients using direct-tissue matrix-assisted laser desorption ionization mass spectrometry. Cancer Research, 2005, 65(9):7674-7681.
54．刘池波，潘春琴，孙灵芬，梁勇．血清蛋白质谱模型在肝硬化诊断中的应用．中华检验医学杂志，2006，29(6)：529-530．
55. Wiemer J C, Prokudin A. Bioinformatics in proteomics: application, terminology, and pitfalls. Pathol Res Pract, 2004, 200(2): 173-8.
56. Diamandis, E.leftherios P. Proteomic patterns in biological fluids: do they represent the future of cancer diagnostics? Clinical Chemistry, 2003, 49: 1272-1275.
57. Qu Y s, Yutaka Y, Michael D W, Lisa H C, Schellhammer P F, Feng Z d, Semmes O J, Wright G L. Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. Clinical Chemistry, 2002, 48: 1835-1843.
58. Wright G L, Cazares L H, Leung S M, Nasim S, Adam B L, Yip T T, Schellhammer P F, Gong L and VlahouA. Proteinchip surface enhanced laser desorptionionization (SELDI) mass spectrometry: a novel protein biochip technology for detection of prostate cancer biomarkers in complex protein mixtures. Nature, 1999, 5(2): 264-276.