SELDI-TOF-MS联合LCM技术筛选大肠癌肝转移早期诊断标志蛋白

目的:应用表面增强激光解吸电离飞行时间质谱蛋白质芯片(SELDI-TOF-MS)联合激光显微切割(LCM)技术筛选大肠癌及其肝转移标志蛋白.方法:采用LCM技术获取24例大肠癌肝转移患者正常大肠、原发灶及肝转移灶癌细胞;应用SELDI-TOF-MS技术对其行蛋白质谱分析;采用Biomarker Wizard软件分析差异蛋白;通过查询蛋白库对特定分子质量所对应的标志蛋白进行初步确定.结果:比较3组细胞间的SELDI质谱图,发现大肠癌原发灶与正常大肠两组间存在15个标志蛋白,12个表达上调,3个表达下调;大肠癌肝转移灶与原发灶两组间存在9个标志蛋白,5个表达上调,4个表达下调.其中质荷比4676.63Da,11740.87Da,21063.59Da和22783.36Da,蛋白峰差异性最明显(P<0.01).通过查询ExPasy蛋白库筛选出20个差异蛋白,包括整合膜蛋白2C、DNA修复蛋白RAD51同系物4、细胞周期检查点蛋白RAD1、人附睾蛋白4、着丝粒蛋白R、Pleckstrin同源结构域家族成员3等.其中细胞凋亡调节Bax蛋白γ亚型、蛋白质S100A11(Protein S100-A11)、Raf激酶抑制蛋白(RKIP)和热休克蛋白27(HSP-27)在正常大肠、原发灶及肝转移灶癌细胞中均呈差异性表达,并且差异性最明显(P<0.01).结论:SELDI蛋白质芯片联合LCM技术有可能筛选出敏感性高、特异性强的大肠癌标志蛋白,筛选出的差异蛋白可能是大肠癌及其肝转移特异性生物标志物.     

应用SELDI-TOF-MS技术建立肝癌筛选血清蛋白质指纹图谱模型

目的:建立肝癌筛选血清蛋白质指纹图谱模型．方法:用表面加强激光解析电离飞行时间质谱技术(SELDI-TOF-MS)及WCX2蛋白芯片获得新发肝癌、肝硬化患者和正常人血清的蛋白质指纹图谱,用计算机软件进行比较分析,建立肝癌的筛选模型．结果:肝癌患者与健康对照组血清蛋白质指纹图谱之间有5个标志蛋白(4477,8943,5181, 8617,13 761 Da)在肝癌患者血清中高表达,肝癌患者与肝硬化患者血清蛋白质指纹图谱之间2个标志蛋白(4477,13 761 Da)在肝癌患者血清中高表达,1个标志蛋白(4097 Da)在肝癌患者血清中低表达．SELDI-TOF-MS技术的特异性(60／60,100％);敏感度(18／20,90％)．分析系统筛选出4477,8943,13 761,4097 Da标志蛋白建立的肝癌诊断模型．结论:建立的血清蛋白质指纹图谱模型能够区分肝癌与非肝癌患者,SELDI-TOF-MS在肝癌的诊断及肿瘤特异性蛋白质生物标志分子的筛选等方面具有一定价值．     

KAll基因在肝硬化和肝细胞癌组织中的表达及其意义

目的研究转移抑制基因KAIl在肝硬化和肝细胞癌组织中的表达及该基因与肝细胞癌患者临床资料间的关系.方法采用Northern blot方法研究20例肝细胞癌组织、14例肝硬化组织和10例正常肝组织中KAll基因mRNA的表达水平,经mRNA的定量分析及统计处理判定KAIl基因与肝细胞癌患者临床特征的关系.结果肝硬化和肝细胞癌组织中KAll mRNA表达水平(0.113±0.110;0.139±0 078)显著低于正常肝组织中的表达(0.316±0.121),(P=0.0003;P=0.0000);肝硬化和肝细胞癌组织中KAll基因mRNA的表达无显著差异(p=0.4152);KAIlmRNA在不同分化程度的肝细胞癌组织中的表达无显著差异(P=0.8261),而Ⅲ期肝细胞癌组织中的KAll基因表达显著低于Ⅱ期的肝细胞癌组织(P=0.0221)结论转移抑制基因KAI1在肝硬化阶段亦有低表达;肝癌组织中KAI1基因的低表达与肝癌的转移有关.     

缺氧诱导因子-1α蛋白在肝癌组织中的表达及临床意义

目的 检测肝癌及癌旁组织中缺氧诱导因子-1 α(HIF-1 α)基因蛋白的表达及临床意义。方法 用免疫组织化学、western blot和RT—PCR技术检测35例肝细胞癌、26例肝硬化组织及15例正常肝组织中HIF-1 α蛋白和基因的表达情况,并分析其与临床病理特点之间的关系。 结果 免疫组织化学显示HIF-1α蛋白在肝硬化和肝癌组织中普遍表达,在肝硬化组织中的表达明显高于正常肝脏组织中的表达;但肝癌组织中因大片状坏死后伴大量纤维结缔组织增生的肝细胞条索中HIF-1 α蛋白的表达强度高于肝硬化组织,肝硬化组织HIF-1α蛋白的表达强度明显高于肝癌组织(54％与31％,x2=4.09,P<0.05);westernblot和RT-PCR结果与免疫组化结果相似。HIF-1α蛋白在肝癌组织中的表达强度与分化程度有关(x2=4.64,P<0.05),与有无肝内外转移有关(x2=7.15,P<0.05);但HIF-1α的表达与门静脉有无癌栓、预后及HBsAg表达无关。 结论 HIF-1 α蛋白在肝癌和肝硬化组织中普遍表达,且只受缺氧因素的影响,与肿瘤的分化程度和肝癌的转移有关,但与有无门静脉癌栓、HBsAg表达及预后无关,为肝癌的治疗提供新的思路。     

Piwil2基因在肝癌组织中mRNA及蛋白的表达

目的:检测肝癌组织中Piwil2基因mRNA和蛋白的表达,进而探讨Piwil2基因在临床诊断中的意义.方法:采用反转录-聚合酶链反应(RT-PCR)和Western blot印迹技术,检测了70例肝癌及其对应癌旁组织中PiWil2基因mRNA及其蛋白的表达并结合临床资料进行分析.结果:PiWil2 mRNA在肝癌中的表达量相对值高于癌旁的肝脏组织的表达量(0.91±0.04vs0.32±0.04,P<0.05).Piwil2基因mRNA的表达与患者的肝内转移、分化程度及癌栓等病理因素方面的差异有统计学意义(P<0.05).蛋白在肝癌中的表达均高于对应的癌旁组织.Piwil2的蛋白质表达与患者的肝内转移及癌栓等病理因素方面的差异有统计学意义(P<0.05).Piwil2基因mRNA的表达与Piwil2的蛋白质的表达有相关性(P<0.05).结论:Piwil2有可能成为肝癌的检测及治疗的一种分子标志物.     

乙型肝炎病毒相关性肝细胞癌患者肝硬化组织与癌组织蛋白质组的差异

目的分析乙型肝炎病毒(HBV)相关性肝细胞癌患者肝硬化组织与癌组织蛋白质组之间的差异,鉴定其中的差异蛋白质点。方法应用双向聚丙烯酰胺凝胶电泳,对12例肝细胞癌患者的肝细胞癌组织与肝硬化组织的蛋白质进行差异分析,应用基质辅助激光解吸离子化飞行时间质谱仪对差异蛋白质点进行了鉴定。结果经肝细胞癌绀织与癌周组织的差异对比分析,共发现有表达水平存在显著差异的35个蛋白质点,14个差异蛋白质点得到了初步鉴定,其中5个蛋白质既往文献中曾报道其与肝细胞癌变相关。结论HBV相关性肝细胞癌蛋白质组与肝硬化组织蛋白质组之间存在差异;本实验研究中初步鉴定的差异蛋白质,可能有助于HBV相关性肝细胞癌的癌变机制、肿瘤标记和治疗靶标的进一步研究。     

胎盘型谷胱甘肽S-转移酶在人乙型肝炎肝硬化肝癌中的表达及其意义

应用抗胎盘型谷胱甘肽S-转移酶(GST-π)单克隆抗体,PAP免疫组化染色,对肝癌、肝硬化及乙型肝炎进行了研究。42例肝癌(包括肝细胞型肝癌和胆管型肝癌),GST-π阳性率为85.7%,乙型肝炎和肝硬化阳性率分别为30%,65%。高分化肝细胞癌和胆管型肝癌全部呈阳性表达,而低分化肝细胞癌呈阴性表达。正常肝组织GST-π仅在部分胆管上皮呈弱阳性表达。结果提示:GST-π可能是高分化肝细胞型肝癌和胆管型肝癌的标志酶,GST-π用来鉴别低分化肝细胞癌和胆管型肝癌可能有一定价值。     

细胞角质素19及消减基因P02在肝细胞癌组织及卵圆细胞中的表达

目的:观察细胞角质素19(CK19)及消减基因P02在肝细胞癌,肝硬化组织及卵圆细胞中的表达情况.方法:采用免疫组化SP法观察8例正常肝组织,27例肝硬化组织和43例肝细胞癌组织中CK19的表达.采用DIG-probe synthesis kit,聚合酶链反应方法制备P02探针,通过原位杂交方法检测P02在肝细胞癌,肝硬化组织及卵圆细胞中的表达.结果:CK19在肝硬化组与正常肝组织之间表达率无明显差异,但在肝细胞癌组与肝硬化组之间差异有显著性(69.77% vs 25.93%,P<0.01).肝硬化组织与肝细胞癌组织中CK19标记的卵圆细胞数量有显著性差异(5.74±1.05 vs 10.51±1.78,P<0.01).P02在肝硬化和肝细胞癌中的阳性表达率分别为26.7%和80%,二者之间表达有显著性差异(P<0.01).结论:CK19可能参与了肝硬化到肝细胞癌的癌变过程.卵圆细胞与肝脏损伤后再生及癌变有关,可能是P02通过促进卵圆细胞的增殖来介导肝细胞癌的发生.     

Ki-67、VEGF、Cyclin D1的表达与肝细胞癌生物学行为的关系

目的:探讨肝细胞癌(hepatocellular carcinoma,HCC)、胆管癌、肝硬化及正常肝组织中增殖细胞核抗原(Ki-67)、血管内皮生长因子(vascular endothelial growth factor,VEGF)及周期素D1(Cyclin D1)蛋白表达的意义及其与肝癌生物学行为关系.方法:对HCC组织52例、胆管癌组织10例、肝硬化组织10例及正常肝组织10例,采用免疫组织化学S-P法检测Ki-67、VEGF及Cyclin D1蛋白.结果:Ki-67、VEGF及Cyclin D1在HCC的强阳性表达率分别为48.1%、55.8%及55.8%,均显著高于胆管癌、肝硬化及正常肝组织(?2=15.672、15.524、14.812,P<0.001).Ki-67与肿瘤大小、血管侵犯、分化程度有关;VEGF、Cyclin D1表达与肿瘤包膜完整、血管侵犯及肿瘤分化程度明显有关;Ki-67与VEGF及Cyclin D1间无相关.VEGF与Cyclin D1间的蛋白表达呈正相关(r=0.374,P<0.01).结论:Ki-67、VEGF及Cyclin D1表达与肝细胞癌生物学行为密切相关,与肝癌的发生和发展密切相关.关键词:肝细胞癌;增殖细胞核抗原;血管内皮生长因子;周期素D1;免疫组织化学     

肝细胞癌患者FOXM1的表达及临床意义

目的探讨FOXM1基因在肝细胞癌及对应癌旁组织中的表达水平及临床意义。方法采用qRT-PCR技术检测FOXM1 mRNA在肝细胞癌及配对癌旁组织中的表达。结果肝细胞癌组织中FOXM1 mRNA表达高于癌旁组织(P0.01);FOXM1 mRNA在低分化肝细胞癌组织中的表达量显著高于中-高分化组(P0.01);FOXM1 mRNA高表达与肝细胞癌患者肿瘤数目(P=0.010)、肿瘤分化程度(P=0.002)、脉管浸润(P=0.029)、血清AFP水平(P=0.004)、临床分期(P=0.003)显著相关;FOXM1mRNA过表达的肝细胞癌患者生存率下降(P=0.010)。结论 FOXM1在肝细胞癌组织中呈高表达,FOXM1基因有可能成为肝细胞癌早期诊断和预后判断的分子标记物。     

原发性胆汁性肝硬化血清蛋白质谱的初步研究

目的筛选与原发性胆汁性肝硬化（PBC）相关的血清蛋白质分子标志物并建立指纹诊断模型。方法收集30例PBC患者血清,30例年龄、性别相匹配正常人,采用弱阳离子磁珠（MB-WCX）为检测介质,基质辅助激光解吸电离质谱（MADIL-TOF-MS）检测得到蛋白质谱,再应用基于磁珠的多维纳升级高效液相色谱及液体蛋白芯片指纹图谱（CLINPROT）系统比较两组血清蛋白质谱。结果在m/z100～10000范围内,检测出的129个蛋白峰中11个蛋白峰差异有统计学意义（P〈0.05）,其中m/z为4963.6、5805.33、1544.45、2292.16、2466.45、2845.85在PBC中表达上调,而m/z为3262.24、3191.2、4090.5、4072.43、3277.46的蛋白峰在PBC中下调;选择m/z为3262.24、3191.2这2个蛋白峰建立的蛋白芯片指纹图谱（CLINPROT）系统对PBC诊断的敏感性为90%,特异性为95%。结论筛选出的11个蛋白分子标志物可能与PBC有关,其中m/z4963在PBC中表达最明显;蛋白芯片指纹图谱（CLINPROT）系统可能对PBC的诊断具有一定临床意义。     

Surface enhanced laser desorption/ionization profiling: New diagnostic method of HBV-related hepatocellular carcinoma

Background and Aim:  To screen for serum biomarkers of HBV-related hepatocellular carcinoma (HCC) and HBV-related liver cirrhosis (LC) in an attempt to seek a new method for differential diagnosis of HCC and LC using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) techniques.
Methods:  Using SELDI-TOF-MS, serum proteins/peptide profiles on the immobilized metal ion affinity capture (IMAC) protein chips were obtained from 29 HCC patients and 30 LC patients. Discriminant analysis was carried out to establish new diagnostic methods using protein/peptide peaks with or without α-fetoprotein (AFP).
Results:  Forty-five protein/peptide peaks changed much more in the HCC group than they did in the LC group. Discriminant analysis using the Wilcoxon rank-sum test showed high sensitivity and specificity in distinguishing HCC from LC. The most significantly differentiating peak, 3892, offered 69.0% sensitivity, 83.3% specificity and 80% positive predictive value in distinguishing HCC and LC. Interestingly, six HCC patients with negative serum AFP were confirmed by peak 3892. The combination of multi-protein peaks (m/z = 9297, 29 941) with AFP offered an 82.8% sensitivity, 93.3% specificity and 92.3% positive predictive value, which was much better than AFP alone ( P  = 0.013).
Conclusions:  Special proteins/peptides of serum may differentiate HBV-related HCC and HBV-related LC, indicating that SELDI-TOF-MS may be useful to distinguish HCC from LC with the proper discriminant analytical method. SELDI peak 3892 may be a complementary diagnostic marker to positive AFP for HCC and a potential marker for the diagnosis of AFP-negative HCC as well.     

肝癌、肝硬化患者前期处理后腹水中差异蛋白质组学分析及意义

目的 分析肝癌、肝硬化患者腹水前期处理后的蛋白质组学变化,并探讨其意义.方法 采用表面加强激光解析电离飞行时间质谱技术（SELDI-TOF-MS技术）对27例肝癌（肝癌组）、46例肝硬化患者（肝硬化组）腹水前期处理后蛋白质进行检测,对蛋白质波谱进行分析.结果 肝癌组在7 852 Da、11 467 Da蛋白质表达量高于肝硬化组,在4 475 Da蛋白质表达量低于肝硬化组（P均＜0.05）.SELDI-TOF-MS技术检测7 852 Da、11 467 Da、4 475 Da区分肝癌和肝硬化灵敏度为81.48％ （22/27）,特异性为86.96％ （40/46）.结论 腹水经前期处理后,肝癌、肝硬化患者在7 852 Da、11 467 Da、4 475 Da蛋白质峰有显著差异,通过这三个蛋白质峰检测可以区分肝癌与肝硬化.     

SELDI-TOF-MS技术在肝癌诊断和介入治疗评价中的价值

目的:建立肝癌血清学诊断模型,探讨评估SELDI-TOF-MS技术在肝癌诊断和介入治疗评价中的价值.方法:用弱阳离子交换芯片(CM10芯片)和表面增强激光解吸电离飞行时间质谱仪(surface-enhanced laser desorption ionization time-of-flight mass spectrometry,SELDI-TOF-MS)技术,测定60例肝癌患者和60例正常对照者的血清蛋白质指纹图谱,应用BiomarkerWizard统计软件比较肝癌组和正常对照组血清蛋白质表达的差异性,采用Biomarker Pattern软件分析数据建立肝癌诊断模型,比较介入治疗前后血清蛋白质指纹图谱的差异性.结果:在质荷比(M/Z)为2000-10000范围内,和正常血清比较,肝癌的差异峰有3个(M/Z为4182Da、5710Da、6992Da;P<0.01),4182Da和5710Da下调,6992Da上调.用这3个差异蛋白峰建立肝癌诊断模型,诊断肝癌的灵敏度为93.3%(28/30),特异度为90.0%(27/30),正确率为91.7%(55/60),约登指数为0.833.差异蛋白峰(M/Z4182Da)在介入术后1mo明显上调(P<0.05).结论:应用SELDI-TOF-MS技术进行肝癌血清蛋白质指纹图谱分析,建立肝癌诊断树模型,对肝癌的诊断有一定的价值;筛选出的差异蛋白峰对肝癌的介入治疗评估有一定的应用价值.     

New multi protein patterns differentiate liver fibrosis stages and hepatocellular carcinoma in chronic hepatitis C serum samples

~~New multi protein patterns differentiate liver fibrosis stages and hepatocellular carcinoma in chronic hepatitis C serum samples@Thomas Gbel$Klinik für Gastroenterologie,Hepatologie und Infektiologie,Heinrich-Heine-UniversitatDusseldorf,Moorenstr.e,D…     

Prediction of chronic hepatitis B, liver cirrhosis and hepatocellular carcinoma by SELDI-based serum decision tree classification

PURPOSE: To screen potential serological biomarkers and develop decision tree classifications of chronic hepatitis B, liver cirrhosis (LC) and hepatocellular carcinoma (HCC), respectively, with high prediction score for improving diagnosis of liver diseases. METHODS: The total serum samples were randomly divided into three training sets (41 HBV and 35 health; 36 LC and 35 health; 39 HCC and 35 health) and three testing groups (34 HBV and 38 health; 18 LC and 52 health; 42 HCC and 47 health). Selected WCX2 protein chip capture followed by SELDI-TOF-MS analysis was applied to generate the serum protein profiles. Subsequently serum protein spectra were normalized and aligned by Ciphergen SELDI Software 3.1.1 with Biomarker Wizard including baseline subtraction, mass accuracy calibration, automatic peak detection. Once the intensities of selected significant peaks from the training data set were transferred to further BPS analysis, an optimized classification tree with sequence-decision was established to divide training data set into disease group and control group successfully. A double blind test was employed to determine the clinical sensitivity and clinical specificity of three models. RESULTS: After comparative analysis of SELDI based serum protein profile between the cases of disease and healthy, a HCC decision tree classification with sensitivity of 94.872% and specificity of 94.286%; a LC decision tree classification with sensitivity of 91.667% and specificity of 94.286% and a HBV decision tree classification with sensitivity of 95.122% and specificity of 94.286% were produced by BPS respectively. When three decision tree models were challenged by the double-blind test samples, clinical sensitivity and clinical specificity of these models were predicted in diagnosis of three liver diseases (HCC: 90.48 and 89.36%; cirrhosis: 100 and 86.5%; HBV: 85.29 and 84.21%). CONCLUSION: SELDI-based decision tree classifications showed great advantages over conventional serological biomarkers in the diagnosis of chronic hepatitis B, LC as well as HCC.     

SELDI-TOF MS profiling of serum for detection of the progression of chronic hepatitis C to hepatocellular carcinoma

Proteomic profiling of serum is an emerging technique to identify new biomarkers indicative of disease severity and progression. The objective of our study was to assess the use of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) to identify multiple serum protein biomarkers for detection of liver disease progression to hepatocellular carcinoma (HCC). A cohort of 170 serum samples obtained from subjects in the United States with no liver disease (n = 39), liver diseases not associated with cirrhosis (n = 36), cirrhosis (n = 38), or HCC (n = 57) were applied to metal affinity protein chips for protein profiling by SELDI-TOF MS. Across the four test groups, 38 differentially expressed proteins were used to generate multiple decision classification trees to distinguish the known disease states. Analysis of a subset of samples with only hepatitis C virus (HCV)-related disease was emphasized. The serum protein profiles of control patients were readily distinguished from each HCV-associated disease state. Two-way comparisons of chronic hepatitis C, HCV cirrhosis, or HCV-HCC versus healthy had a sensitivity/specificity range of 74% to 95%. For distinguishing chronic HCV from HCV-HCC, a sensitivity of 61% and a specificity of 76% were obtained. However, when the values of known serum markers alpha fetoprotein, des-gamma carboxyprothrombin, and GP73 were combined with the SELDI peak values, the sensitivity and specifity improved to 75% and 92%, respectively. In conclusion, SELDI-TOF MS serum profiling is able to distinguish HCC from liver disease before cirrhosis as well as cirrhosis, especially in patients with HCV infection compared with other etiologies.     

Trends in hepatocellular carcinoma due to non-alcoholic fatty liver disease

Introduction: Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide and is associated with hepatocellular carcinoma (HCC), the most frequent malignant liver tumor. The increasing prevalence of obesity and diabetes is influencing the epidemiology of HCC with the most dramatic increases in NAFLD-related HCC seen in Western countries. Although cirrhosis is the major risk factor for HCC in NAFLD, there is increasing recognition that NAFLD-HCC occurs in the absence of cirrhosis.

Areas covered: The epidemiology of NAFLD related HCC and its impact on changing the incidence of HCC globally. We overview risk factors for NAFLD-HCC in the presence and absence of cirrhosis and examine trends in liver transplantation (LT) related to NAFLD-HCC.

Expert commentary: The incidence of NAFLD-related cirrhosis will continue to rise globally in parallel with risk factors of obesity and diabetes. Consequently, NAFLD-related HCC will become an increasingly important cause of liver-related morbidity and mortality and a common indication for LT worldwide. Further identification of risk factors for NAFLD-HCC and effective treatments for NAFLD are required to reduce this future burden of disease.     

Application of Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry Coupled With an Artificial Neural Network Model for the Diagnosis of Hepatocellular Carcinoma

Background/Aims: There are no satisfactory biomarkers for hepatocellular carcinoma (HCC). The surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technique has been used to identify biomarkers for cancer. Methodology: Four hundred thirty five serum samples were tested by SELDI-TOF-MS matching on a gold chip. Samples were assigned to a training set and a testing set according to collection order. The training set was used to identify statistically significant peaks and to develop the artificial neural network (ANN) model for diagnosing HCC. The testing set was used in a blind test to validate the diagnostic efficiency of the ANN model. Results: A total of 75 proteins that differed between patients and controls were identified (p<0.05). Seven of these proteins (p<0.01; m/z at 4207Da, 6604Da, 7734Da, 8106Da, 8545Da, 8599Da, 8894Da) were chosen to develop the ANN model. The model was subjected to a blind test using the testing set for HCC diagnosis. Sensitivity and specificity were 84.00% and 81.25%, respectively, and the accuracy was 81.90%. Conclusions: These results suggest that patients with HCC may have serum proteins that differ from healthy controls. The ANN is a new method for diagnosing and identifying HCC.