基于CLINPROT技术对结直肠癌血清学分子标志kininogen-1的鉴定
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摘要
研究背景与目的
结直肠癌是世界上最常见的恶性肿瘤之一,其发病率和死亡率逐年增高。提高结直肠癌的早期诊断率,做到早发现、早治疗,是提高生存率、改善预后的关键。当前,国内最常用的结直肠癌筛查诊断方法有2种：1.粪便隐血试验(Fecal Occult Blood Test, FOBT),具有简便、无创、经济,耐受性好等优势,但存在假阳性率及假阴性率较高等不足,虽可降低癌症死亡率,但对癌症发病率的影响小,这可能由于FOBT检测腺瘤敏感度低所致；2.全结肠镜或乙状结肠镜检查,是目前“金标准”检查方法,敏感性及特异性高,但需要专业的内镜医生、风险较大、费用高、患者接受性差。目前常用的这些方法常难以满足便捷、经济、安全、高效的大规模筛查需要,因此寻找新的更有效的结直肠癌早期诊断方法势在必行。研究者们一直致力于寻找一些能够串联或并联甚至替代现有的诊断的方法的新的检测手段。寻找结直肠癌早诊的分子标志谱／物是便是其中重要的方向之一,但这方面一直缺乏更广更深的基础与临床研究。
蛋白质组学的出现为寻找肿瘤标记物提供了一个新方向,从各种组织和体液中找出蛋白性分子标志物的方法主要都是基于二维聚丙烯酰胺凝胶电泳法(bi-dimensional polyacrylamide gel electrophoresis,2D-PAGE)和表面增强激光解吸电离质谱测定法(surface enhanced laser desorption ionization mass spectrometry, SELDI-MS)。它们被用来探究用于癌症检测的血清分子标志物。2D-PAGE能够使样本蛋白质组中的大部分蛋白通过显影方法被实时检测出来,然而,由于复杂的步骤要求、长时间的耗时以及高昂的价格,该技术并不适合于临床应用。同样地,由于存在稳定性和可重复性欠佳的问题,SELDI-TOF-MS不可能适于常规的临床应用,而且也不太可能利用这一技术去鉴定那些已被挑选出来的潜在的生物标记物。德国布鲁克公司于2004年成功的开发了一套CLINPROTTM系统,即磁珠(液体蛋白芯片)-MS/MS技术,又称为液体芯片-飞行时间质谱技术。该系统是以基质辅助激光解吸电离飞行时间质谱(Matrix-assisted laser desorption/ionization time of flight mass spectrometry, MALDI-TOF-MS)为基础,它可分为四个部分：磁珠分离系统、质谱系统、分析软件和可选的体液样品自动处理系统。CLINPROT技术利用了磁珠亲和纯化原理,具有高通量、特异性好、重复性高的特点,不仅可以发现差异表达的蛋白质峰,而且可以对差异表达蛋白质进行鉴定,捕获蛋白种类多,可适用于不同的疾病和蛋白。目前,CLINPROT技术已在越来越多的领域得到应用。它在美国和德国已广泛用于卵巢癌、乳腺癌、头颈鳞癌、前列腺癌等的早期诊断过程中,在我国也有这方面的研究,但还仅仅处于实验室阶段。
在结直肠癌的研究方面,目前国内外仅有2篇CLINPROT相关文献报道。1篇重在评价磁珠分离技术结合MALDI-TOF-MS技术本身在血清学检测中的应用,并没有提到有临床意义的筛查指标[44]。另1篇着重探讨利用统计学上的线性判别分析技术结合交叉验证法严格实验条件后评价CLINPROT技术在结直肠癌患者血清学诊断中的作用,预测的敏感性为95.2%,特异性为90.0%,但该多肽谱并没有得到大规模的临床评价,研究缺乏早期病变样本。
本研究以临床病例为基础,采用CLINPROTTM血清蛋白质组学筛查技术,筛选出结直肠癌相关的分子标志谱／物,评价其临床应用价值,并进行大样本、多层次临床验证,从而为进一步开展前瞻性研究、相关基础机制研究及临床检测试剂盒的研发提供依据和实验基础。最终促进结直肠癌预防和诊疗工作的开展,研究结果不仅可以有效提高结直肠癌早诊早治率,对结直肠癌的诊疗监测、个体化治疗也具有重要的推动作用。
材料和方法
1.利用CLINPROTTM技术对35例正常人、35例结直肠高危腺瘤患者、40例结直肠癌患者的血清样品进行差异表达谱分析,建立疾病组和正常组的血清质谱模型；
2.对各组间的差异多肽的二级鉴定,找出可作为结直肠癌早期诊断分子标志的血清蛋白；
3.运用酶联免疫吸附测定法对临床上85例正常人,80例结直肠高危腺瘤患者,143例结直肠癌术前患者,58例结直肠癌术后患者,50例溃疡性结肠炎患者,30例胃癌患者和40例肝细胞癌患者的血清进行验证,观察前期鉴定出的蛋白kininogen-1在各组血清中的差异表达情况。同时观察85例正常人,80例结直肠高危腺瘤患者,143例结直肠癌术前患者血清中CEA的表达情况,以便与kininogen-1进行对比分析；
4.对75例正常肠道粘膜,77例结直肠高危腺瘤和248例结直肠癌石蜡标本进行kininogen-1分子的免疫组织化学染色,观察该分子在肿瘤组织与正常组织之间的表达差别,进一步分析kininogen-1在结直肠癌发生发展中的相关作用。
5.统计方法：质谱数据通过Bruker公司ClinProTools2.2进行图谱绘制、校正和模型建立,以QC算法进行统计分析。ELISA实验和免疫组化实验的所有数据采用SPSS13.0统计软件进行分析,T Test测定各组之间患者年龄、病变大小的差异；Chi-square test检测各组之间性别、组织学类型、上皮内瘤变级别及kininogen-1的染色情况等的比较；Spearman rank-order correlation coefficient测定每组间kininogen-1表达差异的相关性；One-Way ANOVA及LSD方法分析ELSA数据；ROC曲线分析kininogen-1和CEA对于CRC的诊断效果；Kaplan-Meier (Log-rank test)用于生存分析。所有分析结果取双侧P值,P<0.05认为差异具有显著性。
结果
1. CLINPROT实验结果
(1).经过CLINPROT质谱软件分析,发现正常组与高危腺瘤组之间存在信噪比>10的差异蛋白峰70个,其中43个蛋白峰差异明显(P<0.001)；正常组与CRC组之间存在信噪比>10差异蛋白峰61个,其中54个蛋白峰差异明显(P<0.001)；高危腺瘤组与CRC组之间存在信噪比>10差异蛋白峰83个,其中66个蛋白峰差异明显(P<0.001)。
(2).成功建立了各组间最优模型图：正常组与高危腺瘤组之间最优模型图交叉验证(cross validation, CV)为100%,识别能力(recognition capability, RC)为100%：正常组与CRC组之间最优模型图CV为95.94%,RC为98.96%；高危腺瘤组与CRC组之间最优模型图CV为98.19%,RC为100%。
(3).差异多肽的鉴定结果：在CRC组与正常组之间54个差异明显的多肽中,根据多肽鉴定的条件,我们选定了质荷比为1943及2081的两个多肽进行鉴定。通过Mascot数据库搜寻,这2个多肽均来自于同一个蛋白—kininogen-1。
2. ELISA实验结果
(1).各组kininogen-1的血清学浓度：
各组患者之间性别无显著性差异(F=11.390,P=0.077),年龄存在显著性差异(F=8.759,P=0.000)。CRC组血清中,Dukes'A期14例,Dukes'B期63例,Dukes'C期37例,Dukes'D期29例。经one-way ANOVA统计发现,整体而言,kininogen-1浓度在各组间存在显著性差异(F=8.680,P=0.000)。健康志愿者的血清kininogen-1平均浓度为153.22±8.43μg/ml, CRC患者术前的血清kininogen-1平均浓度为215.62±7.63μg/ml,两者之间存在显著性差异(P<0.001)。高危腺瘤组的kininogen-1平均浓度明显高于健康志愿者组(194.26±10.14μg/ml vs153.22±8.43μg/ml,P=0.003)。CRC术前组与高危腺瘤组之间kininogen-1浓度无显著性差异(P=0.082)。一旦接受了手术,CRC患者的血清kininogen-1浓度明显下降(术前vs术后：215.62±7.63μg/ml vs188.04±11.70μg/ml,P=0.044).另一方面,GC组的kininogen-1平均浓度与健康志愿者组无显著性差异(171.25±17.17μg/ml vs194.26±10.14μg/ml,P=0.334)。UC组和LC组的血清kininogen-1浓度也明显高于健康志愿者组(P=0.000)。CRC术前患者的血清kininogen-1平均浓度与UC组、LC组无显著性差异。
(2).kininogen-1对于结直肠肿瘤的诊断价值：
诊断CRC时,kininogen-1的ROC曲线下面积为0.706；当合并CRC与高危腺瘤考虑(此时将CRC与高危腺瘤统称为结直肠肿瘤),我们发现kininogen-1的ROC曲线下面积为0.681；当合并CRC、高危腺瘤、UC共同考虑(此时将这三种疾病统称为大肠疾病),我们发现kininogen-1的ROC曲线下面积为0.701。
根据ROC曲线,血清kininogen-1浓度173.96μg/ml被认为是区分CRC患者和健康志愿者的截断值,用此截断值诊断CRC的敏感度、特异度、阳性预测值、阴性预测值和精确度分别是63.6%,65.9%,75.8%,51.9%,and64.5%。同样,根据ROC曲线,血清kininogen-1浓度161.69μg/ml被认为是区分结直肠肿瘤患者和健康志愿者的截断值,用此截断值诊断结直肠肿瘤(包括结直肠高危腺瘤和CRC)的敏感度、特异度、阳性预测值、阴性预测值和精确度分别是62.3%,63.5%,81.8%,39.1%,and62.7%。血清kininogen-1浓度173.56μg/ml被认为是区分大肠疾病(包括CRC、高危腺瘤、UC)患者和健康志愿者的截断值,用此截断值诊断CRC的敏感度、特异度、阳性预测值、阴性预测值和精确度分别是61.2%,65.9%,85.2%,34.6%,and62.3%。
(3).CEA在健康志愿者组、高危腺瘤组及CRC组中的血清学浓度：
在健康志愿者组、高危腺瘤组和CRC组,血清CEA的平均浓度分别是2.43±0.28,3.10±1.15和14.67±2.25μg/L(P<0.001)。诊断CRC时,CEA的ROC曲线下面积为0.695。以5μg/L作为CEA截断值诊断CRC的敏感度、特异度、阳性预测值、阴性预测值和精确度分别是38.5%,85.9%,82.1%,45.3%,and56.1%。较之单一分子表达增高,若kininogen-1和CEA中的任一分子高于相应截断值时,诊断CRC的敏感度、阴性预测值和精确度都得到了提高。
进一步,我们发现kininogen-1诊断Dukes'A期和B期CRC的敏感度、特异度、阳性预测值、阴性预测值和精确度分别是70.1%,65.9%,65.1%,70.9%,and67.9%：而CEA诊断Dukes'A期和B期CRC的敏感度、特异度、阳性预测值、阴性预测值和精确度分别是39.0%,85.9%,71.4%,60.8%,and63.6%。可以看出,除了特异度外,kininogen-1的其他参数值都明显高于CEA的各个参数值。
3.免疫组织化学染色实验结果
(1).在正常肠黏膜、高危腺瘤、CRC组织中kininogen-1的表达水平：
我们共分析了75例正常结直肠黏膜、77例高危腺瘤和248例CRC患者的石蜡组织,三组之间患者性别、年龄无显著性差异(P>0.05)。kininogen-1在正常结直肠黏膜中未发现阳性表达,而在高危腺瘤和CRC组织中的阳性表达体现为细胞浆积聚。经统计分析发现,高危腺瘤组织和CRC组织中kininogen-1的表达明显强于其在正常结直肠黏膜中的表达(48.39%vs15.58%vs0.00%,P<0.05)。
(2). kininogen-1胞浆表达水平与临床特点的相关性
通过Spearman相关分析我们发现,高危腺瘤中kininogen-1的表达水平与腺瘤的组织学类型具有负相关性(rs=-0.250,P=0.029),即管状腺瘤组织中更易出现kininogen-1的表达。kininogen-1与腺瘤的部位、大小及上皮内瘤变程度无明显相关性(P>0.05)。
在CRC中,kininogen-1的胞浆表达与Dukes分期和淋巴结转移情况具有正相关性(Duke's分期：rs=0.151,P=0.018；淋巴结转移情况：rs=0.128,P=0.045),即发生了淋巴结转移、Duke's分期越高的病例中越容易出现kininogen-1的阳性表达。而与肿瘤发生部位、大小、分化程度及远处转移无明显相关性(P>0.05)。
(3).生存分析
我们根据kininogen-1表达水平和患者的随访情况进行了生存曲线分析,共有110例CRC患者进行了完整的随访资料收集。虽然统计学上差异不显著(P=0.166),但是kininogen-1免疫组织化学染色显示阴性表达的CRC患者,较之kininogen-1阳性表达的CRC患者,生存时间更长(45.21±3.17月VS38.15±3.07月)。
结论
CLINPROTTM是诊断CRC的一个有用工具,具有较高的准确性和可重复性。kininogen-1可以作为一个潜在的CRC血清标记物,尤其在早期CRC的诊断方面具有一定的价值。
Background and aims
Colorectal cancer (CRC) is one leading cause of cancer death worldwide, and its incidence is rising in a number of Asian countries. The five year survival rate for CRC diagnosed at early stage is higher than90%, while the five year survival rate for those diagnosed with widespread cancer stage is less than10%. So, it is critical to advance early diagnosis for CRC before metastasis to distant organs occurs. There are evidences that screening of average-risk individuals can increase the detection of CRC in early stage and result in a reduction of mortality.
According to suggestions from Asia Pacific consensus recommendations for colorectal cancer screening, fecal occult blood test (FOBT), flexible sigmoidoscopy and colonoscopy are recommended for CRC screening. Although FOBT is the first choice for CRC screening in many resource-limited countries, it lacks sufficient sensitivity. Because of the visualization of precancerous and cancerous lesions, flexible sigmoidoscopy and colonoscopy are thought to be the most sensitive approaches for early detection. However, they are invasive, inconvenient and involve significant costs, which limited their use in CRC screening. It is necessary to find a non-or less-invasive approach, with improved safety, accuracy and patient compliance for CRC screening.
Because serologic biomarkers could be analyzed relatively noninvasively and economically, they are far more acceptable than the current screening options and have the potential ability to increase the percentage of the population screened. Serum is expected to be an excellent source of protein biomarkers because it circulates through, or comes in contact with, all tissues. During this contact it is likely to pick up proteins secreted or shed by tissues, so human serum holds immense diagnostic potential. But serum is a complex body fluid containing a large diversity of proteins. More than10,000different proteins are present in human serum and many of them are secreted or shed by cells during different physiological or pathological processes. So it is difficult to identify a specific serum marker.
In recent years, the developments in proteomics instrumentation and computational methodologies offer an unique chance to rapidly identify the new candidate markers or pattern of markers for cancer. Many studies show that matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) is the most powerful relative method for separating complex mixtures of proteins and comparing variations of protein expression in normal and cancerous serum. ClinprotTM technology, which is based on the MALDI-TOF-MS, has many advantages for clinical application:it is sensitive, easy to use, and can perform high-throughput analysis. ClinprotTM technology has opened up new perspectives for establishing specific protein profiles and identifying new tumor biomarkers.
The aim of this study was to investigate the use of ClinprotTM technology for the early diagnosis and monitoring of disease progression of CRC, and identify the valuable biomarkers for CRC. In the present study, serum protein profiles between35healthy volunteers,35advanced colorectal adenoma (ACA) patients and40CRC patients were established and the molecule Kininogen-1was identified. Kininogen-1was further studied by enzyme-linked immunosorbent assay (ELISA) and the immunohistochemistry to validate its value in CRC diagnosis and tumor carcinogenesis.
Materials and Methods
1. The serum proteome profiles of35healthy volunteers,35ACA patients and40CRC patients were compared by CLINPROTTM technology.
2.After statistical analysis, the differently expressed peptides were identified with the Ultraflex MALDI-TOF/TOF mass spectrometer.
3. ELISAs were conducted to confirm the protein identification and differential expression of kininogen-1in486sera from85healthy volunteers,80patients with ACA,143preoperative patients with CRC,58postoperative patients with CRC,50patients with UC,30patients with GC and40patients with LC. Meanwhile, serum CEA levels among healthy volunteers, ACA patients and CRC patients were determined using commercially available enzyme immunoassay kit.
4. The immunohistochemistry test for kininogen-1in colorectal tissues was carried out.75normal colorectal mucosae,77ACAs and248CRCs were included in this test.
5. The ClinProTools software2.2(Bruker, Daltonik) was used for analysis of all spectra data derived from serum samples of different groups. Significant different peptides were determined by means of Welch's T-tests. Class prediction model was set up by QC algorithms. To learn the accuracy of the class prediction, a cross-validation was implemented. Statistical analysis for ELISA and immunohistochemistry test was performed with SPSS13.0software. To test the significance of differences in clinicopathological parameters between groups, the student's t-test was used for age and tumor size, and the chi-square test was used for the remaining parameters. Correlations between the expression of kininogen-1(immunohistochemical scores) with clinicopathological parameters were evaluated by the Spearman rank-order correlation coefficient. The serum levels of kininogen-1and CEA were normally distributed, so the data were compared using a one-way analysis of variance (ANOVA) test, and multiple comparison were analyzed by LSD methods. Data were expressed as the mean±standard error of the mean (SEM). Receiver operating characteristic (ROC) curve analysis determined the cutoff values of serum kininogen-1and CEA for the diagnosis of colorectal tumor. Kaplan-Meier (Log-rank test) was used for survival analysis. All significance levels were defined as P<0.05.
Results
l.CLINPROTTMest
(1). MALDI-TOF analysis of the healthy volunteers and ACA patients resulted in70distinguishable peaks in the1,000to10,000m/z range, out of which43peaks having differential expression and statistical significance (P<0.01). There were61distinguishable peaks between the healthy volunteers and CRC patients, with54peaks having statistical significance (P<0.01).
(2). Genetic algorithms in Clinprot were trained with the detected peaks to generate cross-validated classification models between different groups. The best predicting models resulted in a recognition capability of98.96%,100%and100%between CRC and healthy volunteers, between ACA and healthy volunteers, between ACA and CRC, respectively. Every five MALDI-TOF peaks were used in these best classification models. The algorithm calculated the cross-validity estimate, which was95.49%,100%and98.19%, respectively.
(3). In all the CRC cases, the overexpression of the peptides at m/z1943and2081was evident (P<0.0001). By MALDI-TOF/TOF tandem mass spectrometer, sequence analyses of them had been done. The results showed that MS/MS Fragmentation of m/z1943was NLGHGHKHERDQGHGHQ, and MS/MS Fragmentation of m/z2081was HNLGHGHKHERDQGHGHQ. It indicated that both of them belonged to the same protein, the kininogen-1.
2. ELISA test
(1). The serum concentrations of kininogen-1in different groups
The serum kininogen-1concentrations were215.62±7.63μg/mL and153.22±8.43μg/mL in preoperative CRC patients and healthy volunteers (P<0.001), respectively. Likewise, the kininogen-1concentration in ACA patients was significantly higher than that in healthy volunteers (194.26±10.14μg/mL vs153.22±8.43μg/mL, P=0.003). There was no significant difference between preoperative CRCs and ACAs (P=0.082). The kininogen-1concentration decreased signifcantly in CRC patients after surgery (preoperative vs postoperative:215.62±7.63μg/mL vs188.04±11.70μg/mL, P=0.044). Interestingly, the kininogen-1concentration in GC patients was similar to that in healthy volunteers (171.25±17.17μg/mL vs194.26±10.14μg/mL, P=0.334). On the other hand, the kininogen-1concentrations in UC patients and LC patients were significantly higher than that in healthy volunteers (both P=0.000). There was no significant difference between preoperative CRCs and UCs and LCs.
(2). Diagnostic value of serum kininogen-1levels for colorectal tumor patients
The area under the ROC curve for serum kininogen-1in diagnosing CRC was0.706(95%CI,0.635-0.777); the area in diagnosing colorectal tumors including CRCs and ACAs was0.681(95%CI,0.613-0.749); the area in diagnosing enteric diseases including CRCs, ACAs and UCs was0.701(95%CI,0.636-0.766).
According to the ROC curve, the serum kininogen-1concentration of173.96μg/mL was defined to be the optimal cutoff value for differentiating patients with CRC and healthy volunteers. With this cutoff value, the sensitivity, specificity, positive and negative predictive values, and accuracy were calculated to be63.6%,65.9%,75.8%,51.9%, and64.5%, respectively. Similarly, the serum kininogen-1concentration of161.69μg/mL was defined to be the optimal cutoff value for differentiating patients with colorectal tumor and healthy volunteers, with the sensitivity, specificity, positive and negative predictive values, and accuracy being62.3%,63.5%,81.8%,39.1%, and62.7%, respectively. The serum kininogen-1concentration of173.56μg/mL was defined to be the optimal cutoff value for differentiating patients with enteric diseases and healthy volunteers, with the sensitivity, specificity, positive and negative predictive values, and accuracy being61.2%,65.9%,85.2%,34.6%, and62.3%, respectively.
(3). The serum concentrations of CEA in CRCs, ACAs and healthy volunteers
Serum CEA concentrations were14.67±2.25μg/L,3.10±1.15μg/L and2.43±0.28μg/L in preoperative CRC patients, ACA patients and healthy volunteers, respectively (P<0.001).
The area under the ROC curve for serum CEA in diagnosing CRC was0.695(95%CI,0.627-0.767), which was close to that for serum kininogen-1(0.706[95%CI,0.635-0.777]).5μg/L of serum CEA was used for cutoff value in this study, the sensitivity, specificity, positive and negative predictive values, and accuracy were38.5%,85.9%,82.1%,45.3%, and56.1%, respectively. Sensitivity, negative predictive value and accuracy were improved when patients with either serum kininogen-1or serum CEA positive were considered to have a positive test, while the specificity substantially decreased.
Furthermore, the sensitivity, specificity, positive and negative predictive values, and accuracy of kininogen-1and CEA were assessed in Duke's stage A and B CRC patients. The results for kininogen-1were70.1%,65.9%,65.1%,70.9%and67.9%, respectively. On the other hand, the values of aforementioned five parameters for CEA were39.0%,85.9%,71.4%,60.8%and63.6%, respectively. Except the specificity and positive predictive value, other parameters for kininogen-1were better than those for CEA.
3. Immunohistochemistry
(1). The expression levels of kininogen-1in normal colonic tissues, ACAs and CRCs
No significant difference was found in gender among three groups (P>0.05). The average ages of healthy volunteers, patients bearing ACA and patients bearing CRC were54.03±1.44,59.52±1.34and58.06±0.86years, respectively (P>0.05).
Immunoreactivity of kininogen-1was found in the cytoplasm of ACA and CRC tumor cells. The expression level of kininogen-1was significantly higher in CRCs than that in ACAs and normal colorectal mucosae (48.39%vs15.58%vs0.00%, P<0.05).
(2). Correlation of kininogen-1expression with clinicopathological features in advanced adenomas and colorectal carcinomas
Cytoplasmic accumulation of kininogen-1was significantly correlated with tissue histology in ACAs, and interestingly, this correlation was negative (rs=-0.250, P=0.029). However, it was not correlated with tumor location, tumor size and grade of intraepithelial neoplasia (all P>0.05).
Cytoplasmic accumulation of kininogen-1was significantly correlated with Duke's stage and status of lymph node metastasis in CRCs (rs=0.151, P=0.018for duke's stage; rs=0.128, P=0.045for status of lymph node metastasis). It was not correlated with tumor location, tumor size, tumor cell differentiation and distant metastasis (all P>0.05).
(3). Survival analysis for CRC patients
All110CRC patients were further analyzed to determine the association of the immunoreactivity of kininogen-1with survival. Patients with negative kininogen-1expression had better survival than those who had positive kininogen-1expression, though the difference was not significant (P=0.166). The mean survival time of CRC patients with negative kininogen-1expression was45.21±3.17months, and the mean survival time of CRC patients with positive kininogen-1expression was38.15±3.07months.
Conclusions
Our results showed CLINPROTTM is highly accurate and reproducible. kininogen-1appeared to be a potential CRC serum biomarker, which may be valuable for early detection of CRC.
结直肠癌是世界上最常见的恶性肿瘤之一,其发病率和死亡率逐年增高。提高结直肠癌的早期诊断率,做到早发现、早治疗,是提高生存率、改善预后的关键。当前,国内最常用的结直肠癌筛查诊断方法有2种：1.粪便隐血试验(Fecal Occult Blood Test, FOBT),具有简便、无创、经济,耐受性好等优势,但存在假阳性率及假阴性率较高等不足,虽可降低癌症死亡率,但对癌症发病率的影响小,这可能由于FOBT检测腺瘤敏感度低所致；2.全结肠镜或乙状结肠镜检查,是目前“金标准”检查方法,敏感性及特异性高,但需要专业的内镜医生、风险较大、费用高、患者接受性差。目前常用的这些方法常难以满足便捷、经济、安全、高效的大规模筛查需要,因此寻找新的更有效的结直肠癌早期诊断方法势在必行。研究者们一直致力于寻找一些能够串联或并联甚至替代现有的诊断的方法的新的检测手段。寻找结直肠癌早诊的分子标志谱／物是便是其中重要的方向之一,但这方面一直缺乏更广更深的基础与临床研究。
蛋白质组学的出现为寻找肿瘤标记物提供了一个新方向,从各种组织和体液中找出蛋白性分子标志物的方法主要都是基于二维聚丙烯酰胺凝胶电泳法(bi-dimensional polyacrylamide gel electrophoresis,2D-PAGE)和表面增强激光解吸电离质谱测定法(surface enhanced laser desorption ionization mass spectrometry, SELDI-MS)。它们被用来探究用于癌症检测的血清分子标志物。2D-PAGE能够使样本蛋白质组中的大部分蛋白通过显影方法被实时检测出来,然而,由于复杂的步骤要求、长时间的耗时以及高昂的价格,该技术并不适合于临床应用。同样地,由于存在稳定性和可重复性欠佳的问题,SELDI-TOF-MS不可能适于常规的临床应用,而且也不太可能利用这一技术去鉴定那些已被挑选出来的潜在的生物标记物。德国布鲁克公司于2004年成功的开发了一套CLINPROTTM系统,即磁珠(液体蛋白芯片)-MS/MS技术,又称为液体芯片-飞行时间质谱技术。该系统是以基质辅助激光解吸电离飞行时间质谱(Matrix-assisted laser desorption/ionization time of flight mass spectrometry, MALDI-TOF-MS)为基础,它可分为四个部分：磁珠分离系统、质谱系统、分析软件和可选的体液样品自动处理系统。CLINPROT技术利用了磁珠亲和纯化原理,具有高通量、特异性好、重复性高的特点,不仅可以发现差异表达的蛋白质峰,而且可以对差异表达蛋白质进行鉴定,捕获蛋白种类多,可适用于不同的疾病和蛋白。目前,CLINPROT技术已在越来越多的领域得到应用。它在美国和德国已广泛用于卵巢癌、乳腺癌、头颈鳞癌、前列腺癌等的早期诊断过程中,在我国也有这方面的研究,但还仅仅处于实验室阶段。
在结直肠癌的研究方面,目前国内外仅有2篇CLINPROT相关文献报道。1篇重在评价磁珠分离技术结合MALDI-TOF-MS技术本身在血清学检测中的应用,并没有提到有临床意义的筛查指标[44]。另1篇着重探讨利用统计学上的线性判别分析技术结合交叉验证法严格实验条件后评价CLINPROT技术在结直肠癌患者血清学诊断中的作用,预测的敏感性为95.2%,特异性为90.0%,但该多肽谱并没有得到大规模的临床评价,研究缺乏早期病变样本。
本研究以临床病例为基础,采用CLINPROTTM血清蛋白质组学筛查技术,筛选出结直肠癌相关的分子标志谱／物,评价其临床应用价值,并进行大样本、多层次临床验证,从而为进一步开展前瞻性研究、相关基础机制研究及临床检测试剂盒的研发提供依据和实验基础。最终促进结直肠癌预防和诊疗工作的开展,研究结果不仅可以有效提高结直肠癌早诊早治率,对结直肠癌的诊疗监测、个体化治疗也具有重要的推动作用。
材料和方法
1.利用CLINPROTTM技术对35例正常人、35例结直肠高危腺瘤患者、40例结直肠癌患者的血清样品进行差异表达谱分析,建立疾病组和正常组的血清质谱模型；
2.对各组间的差异多肽的二级鉴定,找出可作为结直肠癌早期诊断分子标志的血清蛋白；
3.运用酶联免疫吸附测定法对临床上85例正常人,80例结直肠高危腺瘤患者,143例结直肠癌术前患者,58例结直肠癌术后患者,50例溃疡性结肠炎患者,30例胃癌患者和40例肝细胞癌患者的血清进行验证,观察前期鉴定出的蛋白kininogen-1在各组血清中的差异表达情况。同时观察85例正常人,80例结直肠高危腺瘤患者,143例结直肠癌术前患者血清中CEA的表达情况,以便与kininogen-1进行对比分析；
4.对75例正常肠道粘膜,77例结直肠高危腺瘤和248例结直肠癌石蜡标本进行kininogen-1分子的免疫组织化学染色,观察该分子在肿瘤组织与正常组织之间的表达差别,进一步分析kininogen-1在结直肠癌发生发展中的相关作用。
5.统计方法：质谱数据通过Bruker公司ClinProTools2.2进行图谱绘制、校正和模型建立,以QC算法进行统计分析。ELISA实验和免疫组化实验的所有数据采用SPSS13.0统计软件进行分析,T Test测定各组之间患者年龄、病变大小的差异；Chi-square test检测各组之间性别、组织学类型、上皮内瘤变级别及kininogen-1的染色情况等的比较；Spearman rank-order correlation coefficient测定每组间kininogen-1表达差异的相关性；One-Way ANOVA及LSD方法分析ELSA数据；ROC曲线分析kininogen-1和CEA对于CRC的诊断效果；Kaplan-Meier (Log-rank test)用于生存分析。所有分析结果取双侧P值,P<0.05认为差异具有显著性。
结果
1. CLINPROT实验结果
(1).经过CLINPROT质谱软件分析,发现正常组与高危腺瘤组之间存在信噪比>10的差异蛋白峰70个,其中43个蛋白峰差异明显(P<0.001)；正常组与CRC组之间存在信噪比>10差异蛋白峰61个,其中54个蛋白峰差异明显(P<0.001)；高危腺瘤组与CRC组之间存在信噪比>10差异蛋白峰83个,其中66个蛋白峰差异明显(P<0.001)。
(2).成功建立了各组间最优模型图：正常组与高危腺瘤组之间最优模型图交叉验证(cross validation, CV)为100%,识别能力(recognition capability, RC)为100%：正常组与CRC组之间最优模型图CV为95.94%,RC为98.96%；高危腺瘤组与CRC组之间最优模型图CV为98.19%,RC为100%。
(3).差异多肽的鉴定结果：在CRC组与正常组之间54个差异明显的多肽中,根据多肽鉴定的条件,我们选定了质荷比为1943及2081的两个多肽进行鉴定。通过Mascot数据库搜寻,这2个多肽均来自于同一个蛋白—kininogen-1。
2. ELISA实验结果
(1).各组kininogen-1的血清学浓度：
各组患者之间性别无显著性差异(F=11.390,P=0.077),年龄存在显著性差异(F=8.759,P=0.000)。CRC组血清中,Dukes'A期14例,Dukes'B期63例,Dukes'C期37例,Dukes'D期29例。经one-way ANOVA统计发现,整体而言,kininogen-1浓度在各组间存在显著性差异(F=8.680,P=0.000)。健康志愿者的血清kininogen-1平均浓度为153.22±8.43μg/ml, CRC患者术前的血清kininogen-1平均浓度为215.62±7.63μg/ml,两者之间存在显著性差异(P<0.001)。高危腺瘤组的kininogen-1平均浓度明显高于健康志愿者组(194.26±10.14μg/ml vs153.22±8.43μg/ml,P=0.003)。CRC术前组与高危腺瘤组之间kininogen-1浓度无显著性差异(P=0.082)。一旦接受了手术,CRC患者的血清kininogen-1浓度明显下降(术前vs术后：215.62±7.63μg/ml vs188.04±11.70μg/ml,P=0.044).另一方面,GC组的kininogen-1平均浓度与健康志愿者组无显著性差异(171.25±17.17μg/ml vs194.26±10.14μg/ml,P=0.334)。UC组和LC组的血清kininogen-1浓度也明显高于健康志愿者组(P=0.000)。CRC术前患者的血清kininogen-1平均浓度与UC组、LC组无显著性差异。
(2).kininogen-1对于结直肠肿瘤的诊断价值：
诊断CRC时,kininogen-1的ROC曲线下面积为0.706；当合并CRC与高危腺瘤考虑(此时将CRC与高危腺瘤统称为结直肠肿瘤),我们发现kininogen-1的ROC曲线下面积为0.681；当合并CRC、高危腺瘤、UC共同考虑(此时将这三种疾病统称为大肠疾病),我们发现kininogen-1的ROC曲线下面积为0.701。
根据ROC曲线,血清kininogen-1浓度173.96μg/ml被认为是区分CRC患者和健康志愿者的截断值,用此截断值诊断CRC的敏感度、特异度、阳性预测值、阴性预测值和精确度分别是63.6%,65.9%,75.8%,51.9%,and64.5%。同样,根据ROC曲线,血清kininogen-1浓度161.69μg/ml被认为是区分结直肠肿瘤患者和健康志愿者的截断值,用此截断值诊断结直肠肿瘤(包括结直肠高危腺瘤和CRC)的敏感度、特异度、阳性预测值、阴性预测值和精确度分别是62.3%,63.5%,81.8%,39.1%,and62.7%。血清kininogen-1浓度173.56μg/ml被认为是区分大肠疾病(包括CRC、高危腺瘤、UC)患者和健康志愿者的截断值,用此截断值诊断CRC的敏感度、特异度、阳性预测值、阴性预测值和精确度分别是61.2%,65.9%,85.2%,34.6%,and62.3%。
(3).CEA在健康志愿者组、高危腺瘤组及CRC组中的血清学浓度：
在健康志愿者组、高危腺瘤组和CRC组,血清CEA的平均浓度分别是2.43±0.28,3.10±1.15和14.67±2.25μg/L(P<0.001)。诊断CRC时,CEA的ROC曲线下面积为0.695。以5μg/L作为CEA截断值诊断CRC的敏感度、特异度、阳性预测值、阴性预测值和精确度分别是38.5%,85.9%,82.1%,45.3%,and56.1%。较之单一分子表达增高,若kininogen-1和CEA中的任一分子高于相应截断值时,诊断CRC的敏感度、阴性预测值和精确度都得到了提高。
进一步,我们发现kininogen-1诊断Dukes'A期和B期CRC的敏感度、特异度、阳性预测值、阴性预测值和精确度分别是70.1%,65.9%,65.1%,70.9%,and67.9%：而CEA诊断Dukes'A期和B期CRC的敏感度、特异度、阳性预测值、阴性预测值和精确度分别是39.0%,85.9%,71.4%,60.8%,and63.6%。可以看出,除了特异度外,kininogen-1的其他参数值都明显高于CEA的各个参数值。
3.免疫组织化学染色实验结果
(1).在正常肠黏膜、高危腺瘤、CRC组织中kininogen-1的表达水平：
我们共分析了75例正常结直肠黏膜、77例高危腺瘤和248例CRC患者的石蜡组织,三组之间患者性别、年龄无显著性差异(P>0.05)。kininogen-1在正常结直肠黏膜中未发现阳性表达,而在高危腺瘤和CRC组织中的阳性表达体现为细胞浆积聚。经统计分析发现,高危腺瘤组织和CRC组织中kininogen-1的表达明显强于其在正常结直肠黏膜中的表达(48.39%vs15.58%vs0.00%,P<0.05)。
(2). kininogen-1胞浆表达水平与临床特点的相关性
通过Spearman相关分析我们发现,高危腺瘤中kininogen-1的表达水平与腺瘤的组织学类型具有负相关性(rs=-0.250,P=0.029),即管状腺瘤组织中更易出现kininogen-1的表达。kininogen-1与腺瘤的部位、大小及上皮内瘤变程度无明显相关性(P>0.05)。
在CRC中,kininogen-1的胞浆表达与Dukes分期和淋巴结转移情况具有正相关性(Duke's分期：rs=0.151,P=0.018；淋巴结转移情况：rs=0.128,P=0.045),即发生了淋巴结转移、Duke's分期越高的病例中越容易出现kininogen-1的阳性表达。而与肿瘤发生部位、大小、分化程度及远处转移无明显相关性(P>0.05)。
(3).生存分析
我们根据kininogen-1表达水平和患者的随访情况进行了生存曲线分析,共有110例CRC患者进行了完整的随访资料收集。虽然统计学上差异不显著(P=0.166),但是kininogen-1免疫组织化学染色显示阴性表达的CRC患者,较之kininogen-1阳性表达的CRC患者,生存时间更长(45.21±3.17月VS38.15±3.07月)。
结论
CLINPROTTM是诊断CRC的一个有用工具,具有较高的准确性和可重复性。kininogen-1可以作为一个潜在的CRC血清标记物,尤其在早期CRC的诊断方面具有一定的价值。
Background and aims
Colorectal cancer (CRC) is one leading cause of cancer death worldwide, and its incidence is rising in a number of Asian countries. The five year survival rate for CRC diagnosed at early stage is higher than90%, while the five year survival rate for those diagnosed with widespread cancer stage is less than10%. So, it is critical to advance early diagnosis for CRC before metastasis to distant organs occurs. There are evidences that screening of average-risk individuals can increase the detection of CRC in early stage and result in a reduction of mortality.
According to suggestions from Asia Pacific consensus recommendations for colorectal cancer screening, fecal occult blood test (FOBT), flexible sigmoidoscopy and colonoscopy are recommended for CRC screening. Although FOBT is the first choice for CRC screening in many resource-limited countries, it lacks sufficient sensitivity. Because of the visualization of precancerous and cancerous lesions, flexible sigmoidoscopy and colonoscopy are thought to be the most sensitive approaches for early detection. However, they are invasive, inconvenient and involve significant costs, which limited their use in CRC screening. It is necessary to find a non-or less-invasive approach, with improved safety, accuracy and patient compliance for CRC screening.
Because serologic biomarkers could be analyzed relatively noninvasively and economically, they are far more acceptable than the current screening options and have the potential ability to increase the percentage of the population screened. Serum is expected to be an excellent source of protein biomarkers because it circulates through, or comes in contact with, all tissues. During this contact it is likely to pick up proteins secreted or shed by tissues, so human serum holds immense diagnostic potential. But serum is a complex body fluid containing a large diversity of proteins. More than10,000different proteins are present in human serum and many of them are secreted or shed by cells during different physiological or pathological processes. So it is difficult to identify a specific serum marker.
In recent years, the developments in proteomics instrumentation and computational methodologies offer an unique chance to rapidly identify the new candidate markers or pattern of markers for cancer. Many studies show that matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) is the most powerful relative method for separating complex mixtures of proteins and comparing variations of protein expression in normal and cancerous serum. ClinprotTM technology, which is based on the MALDI-TOF-MS, has many advantages for clinical application:it is sensitive, easy to use, and can perform high-throughput analysis. ClinprotTM technology has opened up new perspectives for establishing specific protein profiles and identifying new tumor biomarkers.
The aim of this study was to investigate the use of ClinprotTM technology for the early diagnosis and monitoring of disease progression of CRC, and identify the valuable biomarkers for CRC. In the present study, serum protein profiles between35healthy volunteers,35advanced colorectal adenoma (ACA) patients and40CRC patients were established and the molecule Kininogen-1was identified. Kininogen-1was further studied by enzyme-linked immunosorbent assay (ELISA) and the immunohistochemistry to validate its value in CRC diagnosis and tumor carcinogenesis.
Materials and Methods
1. The serum proteome profiles of35healthy volunteers,35ACA patients and40CRC patients were compared by CLINPROTTM technology.
2.After statistical analysis, the differently expressed peptides were identified with the Ultraflex MALDI-TOF/TOF mass spectrometer.
3. ELISAs were conducted to confirm the protein identification and differential expression of kininogen-1in486sera from85healthy volunteers,80patients with ACA,143preoperative patients with CRC,58postoperative patients with CRC,50patients with UC,30patients with GC and40patients with LC. Meanwhile, serum CEA levels among healthy volunteers, ACA patients and CRC patients were determined using commercially available enzyme immunoassay kit.
4. The immunohistochemistry test for kininogen-1in colorectal tissues was carried out.75normal colorectal mucosae,77ACAs and248CRCs were included in this test.
5. The ClinProTools software2.2(Bruker, Daltonik) was used for analysis of all spectra data derived from serum samples of different groups. Significant different peptides were determined by means of Welch's T-tests. Class prediction model was set up by QC algorithms. To learn the accuracy of the class prediction, a cross-validation was implemented. Statistical analysis for ELISA and immunohistochemistry test was performed with SPSS13.0software. To test the significance of differences in clinicopathological parameters between groups, the student's t-test was used for age and tumor size, and the chi-square test was used for the remaining parameters. Correlations between the expression of kininogen-1(immunohistochemical scores) with clinicopathological parameters were evaluated by the Spearman rank-order correlation coefficient. The serum levels of kininogen-1and CEA were normally distributed, so the data were compared using a one-way analysis of variance (ANOVA) test, and multiple comparison were analyzed by LSD methods. Data were expressed as the mean±standard error of the mean (SEM). Receiver operating characteristic (ROC) curve analysis determined the cutoff values of serum kininogen-1and CEA for the diagnosis of colorectal tumor. Kaplan-Meier (Log-rank test) was used for survival analysis. All significance levels were defined as P<0.05.
Results
l.CLINPROTTMest
(1). MALDI-TOF analysis of the healthy volunteers and ACA patients resulted in70distinguishable peaks in the1,000to10,000m/z range, out of which43peaks having differential expression and statistical significance (P<0.01). There were61distinguishable peaks between the healthy volunteers and CRC patients, with54peaks having statistical significance (P<0.01).
(2). Genetic algorithms in Clinprot were trained with the detected peaks to generate cross-validated classification models between different groups. The best predicting models resulted in a recognition capability of98.96%,100%and100%between CRC and healthy volunteers, between ACA and healthy volunteers, between ACA and CRC, respectively. Every five MALDI-TOF peaks were used in these best classification models. The algorithm calculated the cross-validity estimate, which was95.49%,100%and98.19%, respectively.
(3). In all the CRC cases, the overexpression of the peptides at m/z1943and2081was evident (P<0.0001). By MALDI-TOF/TOF tandem mass spectrometer, sequence analyses of them had been done. The results showed that MS/MS Fragmentation of m/z1943was NLGHGHKHERDQGHGHQ, and MS/MS Fragmentation of m/z2081was HNLGHGHKHERDQGHGHQ. It indicated that both of them belonged to the same protein, the kininogen-1.
2. ELISA test
(1). The serum concentrations of kininogen-1in different groups
The serum kininogen-1concentrations were215.62±7.63μg/mL and153.22±8.43μg/mL in preoperative CRC patients and healthy volunteers (P<0.001), respectively. Likewise, the kininogen-1concentration in ACA patients was significantly higher than that in healthy volunteers (194.26±10.14μg/mL vs153.22±8.43μg/mL, P=0.003). There was no significant difference between preoperative CRCs and ACAs (P=0.082). The kininogen-1concentration decreased signifcantly in CRC patients after surgery (preoperative vs postoperative:215.62±7.63μg/mL vs188.04±11.70μg/mL, P=0.044). Interestingly, the kininogen-1concentration in GC patients was similar to that in healthy volunteers (171.25±17.17μg/mL vs194.26±10.14μg/mL, P=0.334). On the other hand, the kininogen-1concentrations in UC patients and LC patients were significantly higher than that in healthy volunteers (both P=0.000). There was no significant difference between preoperative CRCs and UCs and LCs.
(2). Diagnostic value of serum kininogen-1levels for colorectal tumor patients
The area under the ROC curve for serum kininogen-1in diagnosing CRC was0.706(95%CI,0.635-0.777); the area in diagnosing colorectal tumors including CRCs and ACAs was0.681(95%CI,0.613-0.749); the area in diagnosing enteric diseases including CRCs, ACAs and UCs was0.701(95%CI,0.636-0.766).
According to the ROC curve, the serum kininogen-1concentration of173.96μg/mL was defined to be the optimal cutoff value for differentiating patients with CRC and healthy volunteers. With this cutoff value, the sensitivity, specificity, positive and negative predictive values, and accuracy were calculated to be63.6%,65.9%,75.8%,51.9%, and64.5%, respectively. Similarly, the serum kininogen-1concentration of161.69μg/mL was defined to be the optimal cutoff value for differentiating patients with colorectal tumor and healthy volunteers, with the sensitivity, specificity, positive and negative predictive values, and accuracy being62.3%,63.5%,81.8%,39.1%, and62.7%, respectively. The serum kininogen-1concentration of173.56μg/mL was defined to be the optimal cutoff value for differentiating patients with enteric diseases and healthy volunteers, with the sensitivity, specificity, positive and negative predictive values, and accuracy being61.2%,65.9%,85.2%,34.6%, and62.3%, respectively.
(3). The serum concentrations of CEA in CRCs, ACAs and healthy volunteers
Serum CEA concentrations were14.67±2.25μg/L,3.10±1.15μg/L and2.43±0.28μg/L in preoperative CRC patients, ACA patients and healthy volunteers, respectively (P<0.001).
The area under the ROC curve for serum CEA in diagnosing CRC was0.695(95%CI,0.627-0.767), which was close to that for serum kininogen-1(0.706[95%CI,0.635-0.777]).5μg/L of serum CEA was used for cutoff value in this study, the sensitivity, specificity, positive and negative predictive values, and accuracy were38.5%,85.9%,82.1%,45.3%, and56.1%, respectively. Sensitivity, negative predictive value and accuracy were improved when patients with either serum kininogen-1or serum CEA positive were considered to have a positive test, while the specificity substantially decreased.
Furthermore, the sensitivity, specificity, positive and negative predictive values, and accuracy of kininogen-1and CEA were assessed in Duke's stage A and B CRC patients. The results for kininogen-1were70.1%,65.9%,65.1%,70.9%and67.9%, respectively. On the other hand, the values of aforementioned five parameters for CEA were39.0%,85.9%,71.4%,60.8%and63.6%, respectively. Except the specificity and positive predictive value, other parameters for kininogen-1were better than those for CEA.
3. Immunohistochemistry
(1). The expression levels of kininogen-1in normal colonic tissues, ACAs and CRCs
No significant difference was found in gender among three groups (P>0.05). The average ages of healthy volunteers, patients bearing ACA and patients bearing CRC were54.03±1.44,59.52±1.34and58.06±0.86years, respectively (P>0.05).
Immunoreactivity of kininogen-1was found in the cytoplasm of ACA and CRC tumor cells. The expression level of kininogen-1was significantly higher in CRCs than that in ACAs and normal colorectal mucosae (48.39%vs15.58%vs0.00%, P<0.05).
(2). Correlation of kininogen-1expression with clinicopathological features in advanced adenomas and colorectal carcinomas
Cytoplasmic accumulation of kininogen-1was significantly correlated with tissue histology in ACAs, and interestingly, this correlation was negative (rs=-0.250, P=0.029). However, it was not correlated with tumor location, tumor size and grade of intraepithelial neoplasia (all P>0.05).
Cytoplasmic accumulation of kininogen-1was significantly correlated with Duke's stage and status of lymph node metastasis in CRCs (rs=0.151, P=0.018for duke's stage; rs=0.128, P=0.045for status of lymph node metastasis). It was not correlated with tumor location, tumor size, tumor cell differentiation and distant metastasis (all P>0.05).
(3). Survival analysis for CRC patients
All110CRC patients were further analyzed to determine the association of the immunoreactivity of kininogen-1with survival. Patients with negative kininogen-1expression had better survival than those who had positive kininogen-1expression, though the difference was not significant (P=0.166). The mean survival time of CRC patients with negative kininogen-1expression was45.21±3.17months, and the mean survival time of CRC patients with positive kininogen-1expression was38.15±3.07months.
Conclusions
Our results showed CLINPROTTM is highly accurate and reproducible. kininogen-1appeared to be a potential CRC serum biomarker, which may be valuable for early detection of CRC.
引文
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