SELDI-TOF-MS筛选差异蛋白在肺部良恶性疾病鉴别及临床决策中的研究
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摘要
背景:肺癌是人类最为常见的恶性肿瘤,近年来发病率逐年上升。尽管随着医疗手段的不断提高,肺癌患者早期诊断以及预后在不断改善,但是相对于其他肿瘤而言,肺癌的预后仍旧较差,总体5年生存率在美国约15%,在欧洲约10%,在发展中国家大约8.9%。肺癌容易发生转移和局部外侵,目前而言缺少有效的早期诊断方法,而患者的预后与肿瘤的分期密切相关,对于肿瘤局限的患者5年生存率在49.5%,对于局部外侵或者淋巴结转移的病例5年生存率为20.6%,对于远处转移的患者5年生存率仅为2.8%。因此,如何能够对肺癌做出准确的早期诊断已经成为影响肺癌治疗效果的重要因素。本研究利用SELDI-TOF-MS技术对肺部良恶性疾病的蛋白质表达进行分析,寻找肺癌特异性的蛋白质表达,以期为肺癌的早期诊断提供一定帮助。另外我们对不同临床病理特征的肺癌组别进行差异蛋白质表达的分析,以期寻找影响肺癌疾病进展和分期某些重要的蛋白质并且建立临床决策模型以希望为临床肺癌的诊治提供帮助。
第一部分蛋白质芯片SELDI-TOF-MS技术平台建立
目的:建立蛋白质芯片SELDI-TOF-MS技术平台,对其可靠性进行确认。
材料和方法:对同一血浆样品在6个不同芯片位点进行蛋白质指纹图谱检测,分析随机选取的蛋白质峰质荷比及峰强度的变异系数(CV)。
结果:同一血浆样品在6个不同芯片位点的质荷比(M/Z)CV值为4.88E-04(< 1‰),同一血浆样品在6个不同芯片位点的质荷比(M/Z)强度的CV值为0.12(<0.2)。
结论:SELDI-TOF-MS是重复性较好、较为可靠的蛋白质组学研究技术。
第二部分肺癌患者不同临床病理特征间蛋白质指纹图谱的比较
目的:通过测定肺癌患者血浆蛋白表达情况,筛选出与疾病诊断、分期和其它临床病理特征相关的差异蛋白。
材料和方法:应用表面增强激光解吸电离-飞行时间-质谱技术(SELDI-TOF-MS)测定108例肺腺癌、26例肺部良性疾病,分析与疾病的诊断、分期相关的差异蛋白表达。肺癌的分期为UICC/AJCC于2002年联合制定的第六版肺癌TMN国际分期。结果1.对肺癌患者和肺部良性疾病的血浆蛋白质指纹图谱进行分析,结果发现11个M/Z位点蛋白质表达存在显著差异(P<0.05),其中1450.24、1471.95、6794.65、8289.41、8381.62、14374.77位点的蛋白质表达强度在肺癌血浆中显著增高;7617.22、11457.74、15590.85、15813.76及22938.34位点的蛋白质表达强度在肺癌血浆中显著降低。
2.对于Ⅰ期肺癌、肺部良性疾病的血浆蛋白质指纹图谱分析,结果发现43个M/Z位点蛋白质表达存在显著差异(P<0.05),其中1411.4、1447.63、1471.95、1479.1、1496.96、1520.47、1548、1564.67、1641.54、2803.03、2841.1、4768.71、6802.1、7005.24、8181.59、8289.41、9573、9754.75、12054.43、14184.82、14374.77、16454.35、16678.98、22938.34、24283.03、24602.05、29125.61位点的蛋白质表达强度在肺癌血浆中显著增高;1217.46、2696.98、4874.59、6311.76、6520.82、7617.22、7983.73、8524.95、8788.99、9045.58、9176.61、11457.74、13483.64、15589.4、15813.76、27561.76位点的蛋白质表达强度在肺癌血浆中显著降低。
3.对108例肺癌患者的血浆蛋白质指纹图谱进行分析,结果发现在不同病理分期的患者血浆中有40个差异的蛋白质峰(P<0.05)。其中随着病期增加1203.76、1223.51、2695.20、4873.45、5181.75、6315.51、6350.75、6516.06、7627.34、8386.03、8530.45、8776.55、9045.58、9165.15、11456.55、13481.16、14840.41、15604.89、15836.05、22961.58及27548.47位点的蛋白质强度有升高趋势;1411.40、1450.24、1471.95、1479.10、2803.03、2841.10、6441.20、6576.90、8289.41、8918.22、、9573.00、9772.02、14184.82、14374.77、16454.35、16678.98、24283.03、24602.05、29125.61位点的蛋白质强度有下降趋势。
4.对肺癌患者的血浆蛋白质指纹图谱进行分析,结果发现不同分化程度的肺癌患者血浆中存在28个差异蛋白峰(P<0.05)。分别为:1203.76、1223.51、1411.4、1450.24、1471.95、1479.1、2695.2、2803.03、2841.1、4874.59、5181.75、6315.51、6509.97、6780.06、7627.34、8289.41、8776.55、9573、9772.02、11456.55、13495.44、14374.77、15604.89、15836.05、22961.58、24283.03、24602.05、29125.61。
5.1对108例肺癌患者不同淋巴结转移状态进行差异蛋白质的分析,结果发现40个血浆蛋白质峰差异(P<0.05)。其中1203.76、1223.51、2695.2、4873.45、5181.75、6315.51、6350.75、6441.2、6516.06、6576.9、7627.34、8386.03、8530.45、8776.55、9165.15、11456.55、13481.16、14840.41、15604.89、15836.05、22961.58、27548.47位点的蛋白在淋巴结转移组中显著增加;1411.4、1450.24、1471.95、1479.1、2803.03、2841.1、6780.06、8289.41、8918.22、9573、9772.02、14184.82、14374.77、16454.35、16678.98、24283.03、24602.05、29125.61位点的蛋白在淋巴结转移组中显著降低。
5.2进一步在淋巴结转移的病例中分析肺内淋巴结转移和纵隔淋巴结转移的情况,结果发现8个位点的蛋白质表达存在差异(P<0.05)。其中1203.76、1223.32、4515.97、9022.82、9175.79、9233.91位点的蛋白强度在纵隔淋巴结转移的病例中显著升高,11445.96、23551.51位点的蛋白强度在纵隔淋巴结转移的病例中显著下降。
5.3另外在淋巴结转移的肺癌患者中分析单个淋巴结转移和2个以上淋巴结转移患者血浆蛋白质表达差异,结果发现8409.17这个位点的血浆蛋白质表达差异(P<0.05)。对转移淋巴结≤3个和>3个的肺癌患者的血浆蛋白质表达进行分析,结果发现8409.17和8606.14位点的血浆蛋白质表达存在差异(P<0.05)
6.对108例肺癌患者的不同T分期进行蛋白质指纹图谱的分析,结果发现32个差异蛋白质峰存在差异(P<0.05)。其中1203.76、1223.51、2695.20、4874.59、5181.75、6315.51、6350.75、6509.97、7627.34、8386.03、8530.45、11456.55、13495.44、14840.41、22961.58、27548.47位点的蛋白质强度随着T分期的增加而增加;1411.40、1450.24、1471.95、1479.10、2803.03、2841.10、8918.22、9573.00、9772.02、14184.82、14374.77、16454.35、16678.98、24283.03、24602.05、29125.61位点的蛋白质强度随着T分期的增加而降低。
7.在肿块≤2cm和>2cm的两组中有12个差异蛋白(P<0.05),它们分别是1203.76、1450.24、1471.95、1479.10、2803.03、4874.59、5181.75、8289.41、16454.35、16678.98、22961.58、27548.47。在肿块≤3cm和>3cm组中有9个差异蛋白表达(P<0.05),它们分别是1411.40、1450.24、1471.95、2803.03、5181.75、6780.06、11456.55、15604.89、15836.05。在肿块≤5cm和>5cm的两组中有11个差异蛋白质表达(P<0.05),它们分别是1411.40、1450.24、1471.95、2803.03、5181.75、6576.90、6780.06、8918.22、14840.41、15604.89、15836.05。在肿块≤7cm和>7cm两组中未发现任何有差异的血浆蛋白质表达(P>0.05)。
8.对≤50岁和>50岁的两组肺癌患者进行差异蛋白质的比较,结果未发现任何有差异的蛋白质表达;对≤60岁和>60岁的两组肺癌患者进行分析,结果发现仅有24283.03这个位点的蛋白质表达存在差异(P<0.05);对≤70岁和>70岁的两组肺癌患者进行分析,结果发现存在6个差异蛋白表达,他们分别是2841.10、6576.90、6690.84、6887.11、8289.41、15604.89。
9.对不同性别的肺癌患者进行血浆差异蛋白质分析,结果未发现任何有差异的蛋白表达(P>0.05)。
结论:肺癌、肺部良性疾病的血浆蛋白质谱有着显著差异。筛选出的差异蛋白可能在肿瘤的发生、发展过程中起着重要作用。肿瘤的大小、淋巴结转移、疾病分期、细胞分化、年龄均是影响血浆蛋白质谱的重要因素。性别不影响肺癌血浆蛋白质谱的表达。对淋巴结转移细化中发现是否发生纵隔淋巴结转移对肿瘤患者血浆蛋白质谱的影响较大,而淋巴结转移个数对于蛋白质谱的影响较小。不同年龄组别中,年龄70岁上下组对血浆蛋白质谱的影响最大。2、3、5cm不同组别肺癌血浆中均存在较多的差异蛋白表达。
第三部分肺癌临床模型的研究
目的:建立肺癌诊断分期相关的模型,为临床决策提供依据。
材料和方法:利用筛选出的差异蛋白建立早期肺癌诊断、肺癌淋巴结转移、肺癌早晚期鉴别的决策树模型,并且对所建立的决策树模型进行盲法验证。结果:一、肺部孤立性结节的诊断
1.软件自动选出M/Z为1217.45和2696.98的2个蛋白峰,由54例Ⅰ期肺癌和26例肺部良性疾病建立决策树判断模型来诊断早期肺癌,模型的敏感性为96.30%,特异性为100%。
2.选择35例临床上体检发现直径<2cm的肺部小结节对上述模型进行盲法验证,该模型盲法验证准确性为82.9%(29/35),灵敏度为81.3%(13/16),特异性为84.2%(16/19),阳性预测值为81.3%(13/16),阴性预测值为84.2%(16/19)。
二、肺癌淋巴结转移的判断
1.软件自动选出M/Z为1479.10、6576.89、6780.05、8776.55的4个蛋白峰,由108例肺癌建立决策树模型来诊断肺癌淋巴结转移,模型的敏感性为97.83%,特异性为91.94%。
2.35例肺癌病例进行盲法验证,结果显示该模型盲法验证准确性为74.3%(26/35),灵敏度为72.2%(13/18),特异性为76.5%(13/17),阳性预测值为76.5%(13/17),阴性预测值为72.2%(13/18)。
三、早晚期肺癌病例的判断
1.软件自动选出M/Z为1224.05、1447.63、1479.10的3个蛋白质峰,由54例Ⅰ期和28例Ⅲ期肺癌建立决策树模型来判别Ⅰ期和Ⅲ期肺癌,该模型的敏感性为94.44%,特异性为100%。
2.32例Ⅰ期和Ⅲ期肺癌病例进行盲法验证,结果显示该模型盲法验证准确性为84.4%(27/32),灵敏度为80.0%(12/15),特异性为88.2%(15/17),阳性预测值为85.7%(12/14),阴性预测值为83.3%(15/18)。
结论:本研究建立的与肺癌早期诊断与临床分期相关的决策树模型具备较高的诊断效能,能为临床提供一定帮助。
PART ONE:Establish the reliability of SELDI-TOF-MS technique
Objective: To Establish the reliability of SELDI-TOF-MS technique.
Material and methods: Data of one plasma sample in 6 randomize chip location was analyzed, to evaluated the reliability and stability of the technology. The coefficient variation (CV) of protein M/Z value and protein intensity value were calculated. .
Results: The coefficient variation (CV) of protein M/Z value was 4.88E-04(< 1‰). The coefficient variation (CV) of protein intensity was 0.12(<0.2).
Conclusion: SELDI-TOF-MS was an stable and reliable proteomic technique.
PART TWO:Detection Plasma Proteomic Patterns Of Lung Cancer With Different Pathological Features
Objective: To detect plasma proteomic patterns in lung cancer, screen the protein related with disease diagnosis、stage and other clinicopathological characters.
Material and methods: Surface enhanced laser desorption/ionization time of flight mass spectrometry technique and weak cation exchanger (WCX2) was used in detecting the plasma proteomic pattern of 108 lung cancer patients、26 benign lung disease. Biomarker Wizard Software was used to analyze the data, the protein expression related with disease diagnosis and staging was analyzed.
Results: 1. The difference of plasma proteomic patterns between lung cancer and benign lung disease cases was analyzed, 11 proteins with different M/Z values were found to be with statistical significance (P<0.05). Proteins with M/Z values 1450.24、1471.95、6794.65、8289.41、8381.62、14374.77 were upregulated in lung cancer cases; Proteins with M/Z values of 7617.22、11457.74、15590.85、15813.76 and 22938.34 were downregulated in lung cancer cases.
2. The difference of plasma proteomic patterns betweenⅠstage lung cancer and benign lung disease cases was analyzed, 43 proteins with different M/Z values were found to be with statistical significance (P<0.05). Proteins with M/Z values 1411.4、1447.63、1471.95、1479.1、1496.96、1520.47、1548、1564.67、1641.54、2803.03、2841.1、4768.71、6802.1、7005.24、8181.59、8289.41、9573、9754.75、12054.43、14184.82、14374.77、16454.35、16678.98、22938.34、24283.03、24602.05、29125.61 were upregulated in lung cancer cases, Proteins with M/Z values 1217.46、2696.98、4874.59、6311.76、6520.82、7617.22、7983.73、8524.95、8788.99、9045.58、9176.61、11457.74、13483.64、15589.4、15813.76、27561.76 were downregulated in lung cancer cases.
3. The difference of plasma proteomic patterns between different stage was analyzed, 40 proteins with different M/Z values were found to be with statistical significance (P<0.05). Proteins with M/Z values 1203.76、1223.51、2695.20、4873.45、5181.75、6315.51、6350.75、6516.06、7627.34、8386.03、8530.45、8776.55、9045.58、9165.15、11456.55、13481.16、14840.41、15604.89、15836.05、22961.58 and 27548.47 were upregulated in late stage cases. Proteins with M/Z values 1411.40、1450.24、1471.95、1479.10、2803.03、2841.10、6441.20、6576.90、8289.41、8918.22、、9573.00、9772.02、14184.82、14374.77、16454.35、16678.98、24283.03、24602.05、29125.61 were downregulated in late stage cases.
4. The difference of plasma proteomic patterns between different cell differentiation group were analyzed, 28 proteins with different M/Z values 1203.76、1223.51、1411.4、1450.24、1471.95、1479.1、2695.2、2803.03、2841.1、4874.59、5181.75、6315.51、6509.97、6780.06、7627.34、8289.41、8776.55、9573、9772.02、11456.55、13495.44、14374.77、15604.89、15836.05、22961.58、24283.03、24602.05、29125.61 were found to be with statistical significance (P<0.05).
5. The difference of plasma proteomic patterns between lymph node metastasis and without lymph node metastasis group were analyzed, 40 proteins with different M/Z values were found to be with statistical significance (P<0.05). Proteins with M/Z values 1203.76、1223.51、2695.2、4873.45、5181.75、6315.51、6350.75、6441.2、6516.06、6576.9、7627.34、8386.03、8530.45、8776.55、9165.15、11456.55、13481.16、14840.41、15604.89、15836.05、22961.58、27548.47 were upregulated in lymph node metastasis group. Proteins with M/Z values 1411.4、1450.24、1471.95、1479.1、2803.03、2841.1、6780.06、8289.41、8918.22、9573、9772.02、14184.82、14374.77、16454.35、16678.98、24283.03、24602.05、29125.61 were downregulated in lymph node metastasis group. Furthermore, The difference of plasma proteomic patterns between N1 and N2 metastasis group were analyzed, 8 proteins with different M/Z values were found to be with statistical significance (P<0.05). Proteins with M/Z values 1203.76、1223.32、4515.97、9022.82、9175.79、9233.91were upregulated in N2 metastasis group. Proteins with M/Z 11445.96、23551.51 were downregulated in lymph node metastasis group. At last, we found only one protein with M/Z value 8409.17 was to be with statistical significance (P<0.05) between single node metastasis group and group with more than 2 node metastasis;we found two proteins with M/Z value 8409.17 and 8606.14 were be with statistical significance (P<0.05) between group with≤3 node metastasis and group with more than >3 node metastasis.
6. The difference of plasma proteomic patterns between different T stage group were analyzed, 32 proteins with different M/Z values were found to be with statistical significance (P<0.05). Proteins with M/Z values 1203.76、1223.51、2695.20、4874.59、5181.75、6315.51、6350.75、6509.97、7627.34、8386.03、8530.45、11456.55、13495.44、14840.41、22961.58、27548.47 were upregulated in T3+T4 group. Proteins with M/Z values 1411.40、1450.24、1471.95、1479.10、2803.03、2841.10、8918.22、9573.00、9772.02、14184.82、14374.77、16454.35、16678.98、24283.03、24602.05、29125.61 were downregulated in T3+T4 group.
7. The difference of plasma proteomic patterns between tumor≤2cm and >2cm group were analyzed, 12 proteins with different M/Z values of 1203.76、1450.24、1471.95、1479.10、2803.03、4874.59、5181.75、8289.41、16454.35、16678.98、22961.58、27548.47were found to be with statistical significance (P<0.05); The difference of plasma proteomic patterns between tumor≤3cm and >3cm group were analyzed, 9 proteins with different M/Z values of 1203.76、1411.40、1450.24、1471.95、2803.03、5181.75、6780.06、11456.55、15604.89、15836.05 were found to be with statistical significance (P<0.05); The difference of plasma proteomic patterns between tumor≤5cm and >5cm group were analyzed, 11 proteins with different M/Z values of 1411.40、1450.24、1471.95、2803.03、5181.75、6576.90、6780.06、8918.22、14840.41、15604.89、15836.05 were found to be with statistical significance (P<0.05); There was no difference between tumor≤7cm and >7cm group.
8. The difference of plasma proteomic patterns between different age group was analyzed. There was no difference found to be with statistical significance between age≤50 and age >50 years old group. One protein with M/Z value of 24283.03 was found to be with statistical significance (P<0.05) between age≤60 and age >60 years old group. 6 protein with M/Z values of 2841.10、6576.90、6690.84、6887.11、8289.41、15604.89 were found to be with statistical significance (P<0.05) between age≤70 and age >70 years old group.
9. There was no difference of plasma proteomic patterns between different gender group (P>0.05).
Conclusion: Plasma proteomic patterns were detected to be with great difference between lung cancer cases and benign lung disease cases. These screened proteins may be of great importance in tumor generating and developing. Tumor diameter、lymph node metastasis、tumor stage、cell differentiation and age were important factors influencing plasma proteomic patterns. Furthermore, whether there were mediastinal lymph node metastasis had greater importance than metastasis lymph node number in influencing plasma proteomic patterns. There were greater plasma proteomic patterns differences between≤70 and >70 years old group than other group. There were many plasma protein differences in different tumor diameter groups (≤2cm or >2cm,≤3cm or >3cm,≤5cm or >5cm).
PART THREE:Establish The Clinical Model Related With Lung Cancer
Objective: To establish the diagnostic pattern model for lung cancer diagnosis and staging in clinical work.
Material and methods: Biomarker Pattern Software was use in establishing the diagnostic pattern of detecting early lung cancer、differentiating lymph node metastasis and differentiating the stage of disease.
Results: 1.Diagnostic model of early stage lung caner detection 1) Plasma protein obtained from 54Ⅰstage lung cancer and 26 benign lung diseases were used to generate decision tree by Biomarker Pattern Software. Proteins with M/Z value of 1217.45and 2696.98 were automatically selected to constructed the model. Its sensitivity reached 96.296%, specificity reached 100%.
2) 35 cases of pulmonary nodule≤2cm were used as blind assessment, an accuracy of 82.9%(29/35),sensitivity of 81.3%(13/16),specificity of 84.2%(16/19),positive predictive value of 81.3%(13/16),negative predictive value of 84.2%(16/19)were obtained.
2. Diagnostic model of lymph node metastasis
1) Plasma protein obtained from 108 lung cancer cases were used to generate decision tree by Biomarker Pattern Software. Proteins with M/Z value of 1479.10、6576.89、6780.05、8776.55 were automatically selected to constructed the model. Its sensitivity reached 97.83%, specificity reached 91.94%.
2) Another 35 cases of lung cancer were used as blind assessment, an accuracy of 74.3%(26/35),sensitivity of 72.2%(13/18), specificity of 76.5%(13/17),positive predictive value of 76.5%(13/17),negative predictive value of 72.2%(13/18)were obtained.
3. Diagnostic model to distinguish early and later stage of lung cancer
1) Plasma protein obtained from 54Ⅰstage and 28Ⅲstage lung cancer were used to generate decision tree by Biomarker Pattern Software. Proteins with M/Z value of 1224.05、1447.63、1479.10 were automatically selected to constructed the model. Its sensitivity reached 94.44%, specificity reached 100%.
2) 32 cases ofⅠstage andⅢstage among 35 cases were used to used as blind assessment. An accuracy of 84.4%(27/32),sensitivity of 80.0%(12/15), specificity of 88.2%(15/17),positive predictive value of 85.7%(12/14),negative predictive value of 83.3%(15/18)were obtained.
Conclusion: The constructed diagnostic models were helpful to early lung cancer detection. The constructed diagnostic model of lymph node metastasis, and the model of distinguish early and late lung cancer were of great importance in clinical work.
第一部分蛋白质芯片SELDI-TOF-MS技术平台建立
目的:建立蛋白质芯片SELDI-TOF-MS技术平台,对其可靠性进行确认。
材料和方法:对同一血浆样品在6个不同芯片位点进行蛋白质指纹图谱检测,分析随机选取的蛋白质峰质荷比及峰强度的变异系数(CV)。
结果:同一血浆样品在6个不同芯片位点的质荷比(M/Z)CV值为4.88E-04(< 1‰),同一血浆样品在6个不同芯片位点的质荷比(M/Z)强度的CV值为0.12(<0.2)。
结论:SELDI-TOF-MS是重复性较好、较为可靠的蛋白质组学研究技术。
第二部分肺癌患者不同临床病理特征间蛋白质指纹图谱的比较
目的:通过测定肺癌患者血浆蛋白表达情况,筛选出与疾病诊断、分期和其它临床病理特征相关的差异蛋白。
材料和方法:应用表面增强激光解吸电离-飞行时间-质谱技术(SELDI-TOF-MS)测定108例肺腺癌、26例肺部良性疾病,分析与疾病的诊断、分期相关的差异蛋白表达。肺癌的分期为UICC/AJCC于2002年联合制定的第六版肺癌TMN国际分期。结果1.对肺癌患者和肺部良性疾病的血浆蛋白质指纹图谱进行分析,结果发现11个M/Z位点蛋白质表达存在显著差异(P<0.05),其中1450.24、1471.95、6794.65、8289.41、8381.62、14374.77位点的蛋白质表达强度在肺癌血浆中显著增高;7617.22、11457.74、15590.85、15813.76及22938.34位点的蛋白质表达强度在肺癌血浆中显著降低。
2.对于Ⅰ期肺癌、肺部良性疾病的血浆蛋白质指纹图谱分析,结果发现43个M/Z位点蛋白质表达存在显著差异(P<0.05),其中1411.4、1447.63、1471.95、1479.1、1496.96、1520.47、1548、1564.67、1641.54、2803.03、2841.1、4768.71、6802.1、7005.24、8181.59、8289.41、9573、9754.75、12054.43、14184.82、14374.77、16454.35、16678.98、22938.34、24283.03、24602.05、29125.61位点的蛋白质表达强度在肺癌血浆中显著增高;1217.46、2696.98、4874.59、6311.76、6520.82、7617.22、7983.73、8524.95、8788.99、9045.58、9176.61、11457.74、13483.64、15589.4、15813.76、27561.76位点的蛋白质表达强度在肺癌血浆中显著降低。
3.对108例肺癌患者的血浆蛋白质指纹图谱进行分析,结果发现在不同病理分期的患者血浆中有40个差异的蛋白质峰(P<0.05)。其中随着病期增加1203.76、1223.51、2695.20、4873.45、5181.75、6315.51、6350.75、6516.06、7627.34、8386.03、8530.45、8776.55、9045.58、9165.15、11456.55、13481.16、14840.41、15604.89、15836.05、22961.58及27548.47位点的蛋白质强度有升高趋势;1411.40、1450.24、1471.95、1479.10、2803.03、2841.10、6441.20、6576.90、8289.41、8918.22、、9573.00、9772.02、14184.82、14374.77、16454.35、16678.98、24283.03、24602.05、29125.61位点的蛋白质强度有下降趋势。
4.对肺癌患者的血浆蛋白质指纹图谱进行分析,结果发现不同分化程度的肺癌患者血浆中存在28个差异蛋白峰(P<0.05)。分别为:1203.76、1223.51、1411.4、1450.24、1471.95、1479.1、2695.2、2803.03、2841.1、4874.59、5181.75、6315.51、6509.97、6780.06、7627.34、8289.41、8776.55、9573、9772.02、11456.55、13495.44、14374.77、15604.89、15836.05、22961.58、24283.03、24602.05、29125.61。
5.1对108例肺癌患者不同淋巴结转移状态进行差异蛋白质的分析,结果发现40个血浆蛋白质峰差异(P<0.05)。其中1203.76、1223.51、2695.2、4873.45、5181.75、6315.51、6350.75、6441.2、6516.06、6576.9、7627.34、8386.03、8530.45、8776.55、9165.15、11456.55、13481.16、14840.41、15604.89、15836.05、22961.58、27548.47位点的蛋白在淋巴结转移组中显著增加;1411.4、1450.24、1471.95、1479.1、2803.03、2841.1、6780.06、8289.41、8918.22、9573、9772.02、14184.82、14374.77、16454.35、16678.98、24283.03、24602.05、29125.61位点的蛋白在淋巴结转移组中显著降低。
5.2进一步在淋巴结转移的病例中分析肺内淋巴结转移和纵隔淋巴结转移的情况,结果发现8个位点的蛋白质表达存在差异(P<0.05)。其中1203.76、1223.32、4515.97、9022.82、9175.79、9233.91位点的蛋白强度在纵隔淋巴结转移的病例中显著升高,11445.96、23551.51位点的蛋白强度在纵隔淋巴结转移的病例中显著下降。
5.3另外在淋巴结转移的肺癌患者中分析单个淋巴结转移和2个以上淋巴结转移患者血浆蛋白质表达差异,结果发现8409.17这个位点的血浆蛋白质表达差异(P<0.05)。对转移淋巴结≤3个和>3个的肺癌患者的血浆蛋白质表达进行分析,结果发现8409.17和8606.14位点的血浆蛋白质表达存在差异(P<0.05)
6.对108例肺癌患者的不同T分期进行蛋白质指纹图谱的分析,结果发现32个差异蛋白质峰存在差异(P<0.05)。其中1203.76、1223.51、2695.20、4874.59、5181.75、6315.51、6350.75、6509.97、7627.34、8386.03、8530.45、11456.55、13495.44、14840.41、22961.58、27548.47位点的蛋白质强度随着T分期的增加而增加;1411.40、1450.24、1471.95、1479.10、2803.03、2841.10、8918.22、9573.00、9772.02、14184.82、14374.77、16454.35、16678.98、24283.03、24602.05、29125.61位点的蛋白质强度随着T分期的增加而降低。
7.在肿块≤2cm和>2cm的两组中有12个差异蛋白(P<0.05),它们分别是1203.76、1450.24、1471.95、1479.10、2803.03、4874.59、5181.75、8289.41、16454.35、16678.98、22961.58、27548.47。在肿块≤3cm和>3cm组中有9个差异蛋白表达(P<0.05),它们分别是1411.40、1450.24、1471.95、2803.03、5181.75、6780.06、11456.55、15604.89、15836.05。在肿块≤5cm和>5cm的两组中有11个差异蛋白质表达(P<0.05),它们分别是1411.40、1450.24、1471.95、2803.03、5181.75、6576.90、6780.06、8918.22、14840.41、15604.89、15836.05。在肿块≤7cm和>7cm两组中未发现任何有差异的血浆蛋白质表达(P>0.05)。
8.对≤50岁和>50岁的两组肺癌患者进行差异蛋白质的比较,结果未发现任何有差异的蛋白质表达;对≤60岁和>60岁的两组肺癌患者进行分析,结果发现仅有24283.03这个位点的蛋白质表达存在差异(P<0.05);对≤70岁和>70岁的两组肺癌患者进行分析,结果发现存在6个差异蛋白表达,他们分别是2841.10、6576.90、6690.84、6887.11、8289.41、15604.89。
9.对不同性别的肺癌患者进行血浆差异蛋白质分析,结果未发现任何有差异的蛋白表达(P>0.05)。
结论:肺癌、肺部良性疾病的血浆蛋白质谱有着显著差异。筛选出的差异蛋白可能在肿瘤的发生、发展过程中起着重要作用。肿瘤的大小、淋巴结转移、疾病分期、细胞分化、年龄均是影响血浆蛋白质谱的重要因素。性别不影响肺癌血浆蛋白质谱的表达。对淋巴结转移细化中发现是否发生纵隔淋巴结转移对肿瘤患者血浆蛋白质谱的影响较大,而淋巴结转移个数对于蛋白质谱的影响较小。不同年龄组别中,年龄70岁上下组对血浆蛋白质谱的影响最大。2、3、5cm不同组别肺癌血浆中均存在较多的差异蛋白表达。
第三部分肺癌临床模型的研究
目的:建立肺癌诊断分期相关的模型,为临床决策提供依据。
材料和方法:利用筛选出的差异蛋白建立早期肺癌诊断、肺癌淋巴结转移、肺癌早晚期鉴别的决策树模型,并且对所建立的决策树模型进行盲法验证。结果:一、肺部孤立性结节的诊断
1.软件自动选出M/Z为1217.45和2696.98的2个蛋白峰,由54例Ⅰ期肺癌和26例肺部良性疾病建立决策树判断模型来诊断早期肺癌,模型的敏感性为96.30%,特异性为100%。
2.选择35例临床上体检发现直径<2cm的肺部小结节对上述模型进行盲法验证,该模型盲法验证准确性为82.9%(29/35),灵敏度为81.3%(13/16),特异性为84.2%(16/19),阳性预测值为81.3%(13/16),阴性预测值为84.2%(16/19)。
二、肺癌淋巴结转移的判断
1.软件自动选出M/Z为1479.10、6576.89、6780.05、8776.55的4个蛋白峰,由108例肺癌建立决策树模型来诊断肺癌淋巴结转移,模型的敏感性为97.83%,特异性为91.94%。
2.35例肺癌病例进行盲法验证,结果显示该模型盲法验证准确性为74.3%(26/35),灵敏度为72.2%(13/18),特异性为76.5%(13/17),阳性预测值为76.5%(13/17),阴性预测值为72.2%(13/18)。
三、早晚期肺癌病例的判断
1.软件自动选出M/Z为1224.05、1447.63、1479.10的3个蛋白质峰,由54例Ⅰ期和28例Ⅲ期肺癌建立决策树模型来判别Ⅰ期和Ⅲ期肺癌,该模型的敏感性为94.44%,特异性为100%。
2.32例Ⅰ期和Ⅲ期肺癌病例进行盲法验证,结果显示该模型盲法验证准确性为84.4%(27/32),灵敏度为80.0%(12/15),特异性为88.2%(15/17),阳性预测值为85.7%(12/14),阴性预测值为83.3%(15/18)。
结论:本研究建立的与肺癌早期诊断与临床分期相关的决策树模型具备较高的诊断效能,能为临床提供一定帮助。
PART ONE:Establish the reliability of SELDI-TOF-MS technique
Objective: To Establish the reliability of SELDI-TOF-MS technique.
Material and methods: Data of one plasma sample in 6 randomize chip location was analyzed, to evaluated the reliability and stability of the technology. The coefficient variation (CV) of protein M/Z value and protein intensity value were calculated. .
Results: The coefficient variation (CV) of protein M/Z value was 4.88E-04(< 1‰). The coefficient variation (CV) of protein intensity was 0.12(<0.2).
Conclusion: SELDI-TOF-MS was an stable and reliable proteomic technique.
PART TWO:Detection Plasma Proteomic Patterns Of Lung Cancer With Different Pathological Features
Objective: To detect plasma proteomic patterns in lung cancer, screen the protein related with disease diagnosis、stage and other clinicopathological characters.
Material and methods: Surface enhanced laser desorption/ionization time of flight mass spectrometry technique and weak cation exchanger (WCX2) was used in detecting the plasma proteomic pattern of 108 lung cancer patients、26 benign lung disease. Biomarker Wizard Software was used to analyze the data, the protein expression related with disease diagnosis and staging was analyzed.
Results: 1. The difference of plasma proteomic patterns between lung cancer and benign lung disease cases was analyzed, 11 proteins with different M/Z values were found to be with statistical significance (P<0.05). Proteins with M/Z values 1450.24、1471.95、6794.65、8289.41、8381.62、14374.77 were upregulated in lung cancer cases; Proteins with M/Z values of 7617.22、11457.74、15590.85、15813.76 and 22938.34 were downregulated in lung cancer cases.
2. The difference of plasma proteomic patterns betweenⅠstage lung cancer and benign lung disease cases was analyzed, 43 proteins with different M/Z values were found to be with statistical significance (P<0.05). Proteins with M/Z values 1411.4、1447.63、1471.95、1479.1、1496.96、1520.47、1548、1564.67、1641.54、2803.03、2841.1、4768.71、6802.1、7005.24、8181.59、8289.41、9573、9754.75、12054.43、14184.82、14374.77、16454.35、16678.98、22938.34、24283.03、24602.05、29125.61 were upregulated in lung cancer cases, Proteins with M/Z values 1217.46、2696.98、4874.59、6311.76、6520.82、7617.22、7983.73、8524.95、8788.99、9045.58、9176.61、11457.74、13483.64、15589.4、15813.76、27561.76 were downregulated in lung cancer cases.
3. The difference of plasma proteomic patterns between different stage was analyzed, 40 proteins with different M/Z values were found to be with statistical significance (P<0.05). Proteins with M/Z values 1203.76、1223.51、2695.20、4873.45、5181.75、6315.51、6350.75、6516.06、7627.34、8386.03、8530.45、8776.55、9045.58、9165.15、11456.55、13481.16、14840.41、15604.89、15836.05、22961.58 and 27548.47 were upregulated in late stage cases. Proteins with M/Z values 1411.40、1450.24、1471.95、1479.10、2803.03、2841.10、6441.20、6576.90、8289.41、8918.22、、9573.00、9772.02、14184.82、14374.77、16454.35、16678.98、24283.03、24602.05、29125.61 were downregulated in late stage cases.
4. The difference of plasma proteomic patterns between different cell differentiation group were analyzed, 28 proteins with different M/Z values 1203.76、1223.51、1411.4、1450.24、1471.95、1479.1、2695.2、2803.03、2841.1、4874.59、5181.75、6315.51、6509.97、6780.06、7627.34、8289.41、8776.55、9573、9772.02、11456.55、13495.44、14374.77、15604.89、15836.05、22961.58、24283.03、24602.05、29125.61 were found to be with statistical significance (P<0.05).
5. The difference of plasma proteomic patterns between lymph node metastasis and without lymph node metastasis group were analyzed, 40 proteins with different M/Z values were found to be with statistical significance (P<0.05). Proteins with M/Z values 1203.76、1223.51、2695.2、4873.45、5181.75、6315.51、6350.75、6441.2、6516.06、6576.9、7627.34、8386.03、8530.45、8776.55、9165.15、11456.55、13481.16、14840.41、15604.89、15836.05、22961.58、27548.47 were upregulated in lymph node metastasis group. Proteins with M/Z values 1411.4、1450.24、1471.95、1479.1、2803.03、2841.1、6780.06、8289.41、8918.22、9573、9772.02、14184.82、14374.77、16454.35、16678.98、24283.03、24602.05、29125.61 were downregulated in lymph node metastasis group. Furthermore, The difference of plasma proteomic patterns between N1 and N2 metastasis group were analyzed, 8 proteins with different M/Z values were found to be with statistical significance (P<0.05). Proteins with M/Z values 1203.76、1223.32、4515.97、9022.82、9175.79、9233.91were upregulated in N2 metastasis group. Proteins with M/Z 11445.96、23551.51 were downregulated in lymph node metastasis group. At last, we found only one protein with M/Z value 8409.17 was to be with statistical significance (P<0.05) between single node metastasis group and group with more than 2 node metastasis;we found two proteins with M/Z value 8409.17 and 8606.14 were be with statistical significance (P<0.05) between group with≤3 node metastasis and group with more than >3 node metastasis.
6. The difference of plasma proteomic patterns between different T stage group were analyzed, 32 proteins with different M/Z values were found to be with statistical significance (P<0.05). Proteins with M/Z values 1203.76、1223.51、2695.20、4874.59、5181.75、6315.51、6350.75、6509.97、7627.34、8386.03、8530.45、11456.55、13495.44、14840.41、22961.58、27548.47 were upregulated in T3+T4 group. Proteins with M/Z values 1411.40、1450.24、1471.95、1479.10、2803.03、2841.10、8918.22、9573.00、9772.02、14184.82、14374.77、16454.35、16678.98、24283.03、24602.05、29125.61 were downregulated in T3+T4 group.
7. The difference of plasma proteomic patterns between tumor≤2cm and >2cm group were analyzed, 12 proteins with different M/Z values of 1203.76、1450.24、1471.95、1479.10、2803.03、4874.59、5181.75、8289.41、16454.35、16678.98、22961.58、27548.47were found to be with statistical significance (P<0.05); The difference of plasma proteomic patterns between tumor≤3cm and >3cm group were analyzed, 9 proteins with different M/Z values of 1203.76、1411.40、1450.24、1471.95、2803.03、5181.75、6780.06、11456.55、15604.89、15836.05 were found to be with statistical significance (P<0.05); The difference of plasma proteomic patterns between tumor≤5cm and >5cm group were analyzed, 11 proteins with different M/Z values of 1411.40、1450.24、1471.95、2803.03、5181.75、6576.90、6780.06、8918.22、14840.41、15604.89、15836.05 were found to be with statistical significance (P<0.05); There was no difference between tumor≤7cm and >7cm group.
8. The difference of plasma proteomic patterns between different age group was analyzed. There was no difference found to be with statistical significance between age≤50 and age >50 years old group. One protein with M/Z value of 24283.03 was found to be with statistical significance (P<0.05) between age≤60 and age >60 years old group. 6 protein with M/Z values of 2841.10、6576.90、6690.84、6887.11、8289.41、15604.89 were found to be with statistical significance (P<0.05) between age≤70 and age >70 years old group.
9. There was no difference of plasma proteomic patterns between different gender group (P>0.05).
Conclusion: Plasma proteomic patterns were detected to be with great difference between lung cancer cases and benign lung disease cases. These screened proteins may be of great importance in tumor generating and developing. Tumor diameter、lymph node metastasis、tumor stage、cell differentiation and age were important factors influencing plasma proteomic patterns. Furthermore, whether there were mediastinal lymph node metastasis had greater importance than metastasis lymph node number in influencing plasma proteomic patterns. There were greater plasma proteomic patterns differences between≤70 and >70 years old group than other group. There were many plasma protein differences in different tumor diameter groups (≤2cm or >2cm,≤3cm or >3cm,≤5cm or >5cm).
PART THREE:Establish The Clinical Model Related With Lung Cancer
Objective: To establish the diagnostic pattern model for lung cancer diagnosis and staging in clinical work.
Material and methods: Biomarker Pattern Software was use in establishing the diagnostic pattern of detecting early lung cancer、differentiating lymph node metastasis and differentiating the stage of disease.
Results: 1.Diagnostic model of early stage lung caner detection 1) Plasma protein obtained from 54Ⅰstage lung cancer and 26 benign lung diseases were used to generate decision tree by Biomarker Pattern Software. Proteins with M/Z value of 1217.45and 2696.98 were automatically selected to constructed the model. Its sensitivity reached 96.296%, specificity reached 100%.
2) 35 cases of pulmonary nodule≤2cm were used as blind assessment, an accuracy of 82.9%(29/35),sensitivity of 81.3%(13/16),specificity of 84.2%(16/19),positive predictive value of 81.3%(13/16),negative predictive value of 84.2%(16/19)were obtained.
2. Diagnostic model of lymph node metastasis
1) Plasma protein obtained from 108 lung cancer cases were used to generate decision tree by Biomarker Pattern Software. Proteins with M/Z value of 1479.10、6576.89、6780.05、8776.55 were automatically selected to constructed the model. Its sensitivity reached 97.83%, specificity reached 91.94%.
2) Another 35 cases of lung cancer were used as blind assessment, an accuracy of 74.3%(26/35),sensitivity of 72.2%(13/18), specificity of 76.5%(13/17),positive predictive value of 76.5%(13/17),negative predictive value of 72.2%(13/18)were obtained.
3. Diagnostic model to distinguish early and later stage of lung cancer
1) Plasma protein obtained from 54Ⅰstage and 28Ⅲstage lung cancer were used to generate decision tree by Biomarker Pattern Software. Proteins with M/Z value of 1224.05、1447.63、1479.10 were automatically selected to constructed the model. Its sensitivity reached 94.44%, specificity reached 100%.
2) 32 cases ofⅠstage andⅢstage among 35 cases were used to used as blind assessment. An accuracy of 84.4%(27/32),sensitivity of 80.0%(12/15), specificity of 88.2%(15/17),positive predictive value of 85.7%(12/14),negative predictive value of 83.3%(15/18)were obtained.
Conclusion: The constructed diagnostic models were helpful to early lung cancer detection. The constructed diagnostic model of lymph node metastasis, and the model of distinguish early and late lung cancer were of great importance in clinical work.
引文
1. Hanash, S.M., et al., Integrating cancer genomics and proteomics in the post-genome era. Proteomics, 2002. 2(1): p. 69-75.
2. Wilkins, M.R., et al., From proteins to proteomes: large scale protein identification by two-dimensional electrophoresis and amino acid analysis. Biotechnology (N Y), 1996. 14(1): p. 61-5.
3. Petricoin, E.F. and L.A. Liotta, Proteomic analysis at the bedside: early detection of cancer. Trends Biotechnol, 2002. 20(12 Suppl): p. S30-4.
4. Petricoin, E.F., 3rd, et al., Serum proteomic patterns for detection of prostate cancer. J Natl Cancer Inst, 2002. 94(20): p. 1576-8.
5. Qu, Y., et al., Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. Clin Chem, 2002. 48(10): p. 1835-43.
6. Petricoin, E.F., et al., Clinical proteomics: translating benchside promise into bedside reality. Nat Rev Drug Discov, 2002. 1(9): p. 683-95.
7. Wu, W., W. Hu, and J.J. Kavanagh, Proteomics in cancer research. Int J Gynecol Cancer, 2002. 12(5): p. 409-23.
8. Weinberger, S.R., T.S. Morris, and M. Pawlak, Recent trends in protein biochip technology. Pharmacogenomics, 2000. 1(4): p. 395-416.
9. Seibert, V., et al., 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): p. 83-94.
10. Bischoff, R. and T.M. Luider, Methodological advances in the discovery of protein and peptide disease markers. J Chromatogr B Analyt Technol Biomed Life Sci, 2004. 803(1): p. 27-40.
11. Chapman, K., The ProteinChip Biomarker System from Ciphergen Biosystems: a novel proteomics platform for rapid biomarker discovery and validation. Biochem Soc Trans, 2002. 30(2): p. 82-7.
1. Parkin, D.M., et al., Global cancer statistics, 2002. CA Cancer J Clin, 2005. 55(2): p. 74-108.
2. Kanmura, S., et al., Early diagnostic potential for hepatocellular carcinoma using the SELDI ProteinChip system. Hepatology, 2007. 45(4): p. 948-56.
3. Paradis, V., et al., Identification of a new marker of hepatocellular carcinoma by serum protein profiling of patients with chronic liver diseases. Hepatology, 2005. 41(1): p. 40-7.
4. Zhang, J., et al., Proteomic Profiling of Hepatitis B Virus-related Hepatocellular Carcinoma in China: a SELDI-TOF-MS Study. Int J Clin Exp Pathol, 2008. 1(4): p. 352-61.
5. Nakagawa, T., et al., Proteomic profiling of primary breast cancer predicts axillary lymph node metastasis. Cancer Res, 2006. 66(24): p. 11825-30.
6. Gast, M.C., et al., SELDI-TOF MS serum protein profiles in breast cancer: assessment of robustness and validity. Cancer Biomark, 2006. 2(6): p. 235-48.
7. Sauter, E.R., et al., Proteomic analysis of nipple aspirate fluid using SELDI-TOF-MS. Int J Cancer, 2005. 114(5): p. 791-6.
8. Hu, Y., et al., [Diagnostic application of serum protein pattern and artificial neural network software in breast cancer.]. Ai Zheng, 2005. 24(1): p. 67-71.
9. McLerran, D., et al., SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer. Clin Chem, 2008. 54(1): p. 53-60.
10. Le, L., et al., Identification of serum amyloid A as a biomarker to distinguish prostate cancer patients with bone lesions. Clin Chem, 2005. 51(4): p. 695-707.
11. Kohli, M., et al., Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) for determining prognosis in advanced stage hormone relapsing prostate cancer. Cancer Biomark, 2006. 2(6): p. 249-58.
12. Skytt, A., et al., SELDI-TOF MS versus prostate specific antigen analysis ofprospective plasma samples in a nested case-control study of prostate cancer. Int J Cancer, 2007. 121(3): p. 615-20.
13. Wang, D.C., et al., [The application of surface-enhanced laser desorption/ionization-time of flight-mass spectrometry in diagnosing dysplasia and chronic atrophic gastric-carditis in population with high risk of gastric-cardia adenocarcinoma]. Zhonghua Nei Ke Za Zhi, 2005. 44(8): p. 573-6.
14. Ebert, M.P., et al., Identification of gastric cancer patients by serum protein profiling. J Proteome Res, 2004. 3(6): p. 1261-6.
15. Qian, H.G., et al., Preliminary study on proteomics of gastric carcinoma and its clinical significance. World J Gastroenterol, 2005. 11(40): p. 6249-53.
16. Wang, L.D., et al., [Serum proteomic profiles of the subjects with esophageal precancerous and cancerous lesions from Linzhou, an area with high incidence of esophageal cancer in Henan Province, Northern China]. Ai Zheng, 2006. 25(5): p. 549-54.
17. Nomura, F., [Clinical proteomics in laboratory medicine]. Rinsho Byori, 2006. 54(4): p. 413-20.
18. Wang, S.J., et al., [Establishment of a diagnostic model of serum protein fingerprint pattern for esophageal cancer screening in high incidence area and its clinical value]. Zhonghua Zhong Liu Za Zhi, 2007. 29(6): p. 441-3.
19. Luo, X.Y., et al., [Serum proteomic patterns may assist the diagnosis of solitary pulmonary nodules]. Zhonghua Zhong Liu Za Zhi, 2006. 28(12): p. 907-10.
20. Yang, S.Y., et al., [Application of serum surface-enhanced laser desorption/ionization proteomic patterns in distinguishing non-small cell lung cancer patients from healthy people]. Zhonghua Jie He He Hu Xi Za Zhi, 2006. 29(1): p. 31-4.
21. Yang, S., et al., Study of distinct protein profiles for early diagnosis of NSCLC using LCM and SELDI-TOF-MS. Med Oncol, 2008.
22. Au, J.S., et al., Deep proteome profiling of sera from never-smoked lung cancer patients. Biomed Pharmacother, 2007. 61(9): p. 570-7.
23. Cheng, L., et al., Preliminary study of proteomic shift from normal to premalignant laryngeal lesions and to laryngeal squamous cell carcinoma. Acta Otolaryngol, 2008: p. 1-5.
24. Wei, Y.S., et al., Identification of serum biomarkers for nasopharyngeal carcinoma by proteomic analysis. Cancer, 2008. 112(3): p. 544-51.
1. Ost, D. and A. Fein, Evaluation and management of the solitary pulmonary nodule. Am J Respir Crit Care Med, 2000. 162(3 Pt 1): p. 782-7.
2. Arita, T., et al., Is it possible to differentiate malignant mediastinal nodes from benign nodes by size? Reevaluation by CT, transesophageal echocardiography, and nodal specimen. Chest, 1996. 110(4): p. 1004-8.
3. Arita, T., et al., Bronchogenic carcinoma: incidence of metastases to normal sized lymph nodes. Thorax, 1995. 50(12): p. 1267-9.
4. McLoud, T.C., et al., Bronchogenic carcinoma: analysis of staging in themediastinum with CT by correlative lymph node mapping and sampling. Radiology, 1992. 182(2): p. 319-23.
5. Schrevens, L., et al., The role of PET scan in diagnosis, staging, and management of non-small cell lung cancer. Oncologist, 2004. 9(6): p. 633-43.
6. Gould, M.K., et al., Test performance of positron emission tomography and computed tomography for mediastinal staging in patients with non-small-cell lung cancer: a meta-analysis. Ann Intern Med, 2003. 139(11): p. 879-92.
7. Yang, S.Y., et al., [Application of serum surface-enhanced laser desorption/ionization proteomic patterns in distinguishing non-small cell lung cancer patients from healthy people]. Zhonghua Jie He He Hu Xi Za Zhi, 2006. 29(1): p. 31-4.
8. Au, J.S., et al., Proteomic approach to biomarker discovery in cancer tissue from lung adenocarcinoma among nonsmoking Chinese women in Hong Kong. Cancer Invest, 2008. 26(2): p. 128-35.
9. Xia, T., et al., [Application of SELDI-TOF serum proteome profiling in cervical squamous cell carcinoma]. Ai Zheng, 2008. 27(3): p. 279-82.
10. Wei, Y.S., et al., Identification of serum biomarkers for nasopharyngeal carcinoma by proteomic analysis. Cancer, 2008. 112(3): p. 544-51.
11. Kohli, M., et al., Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) for determining prognosis in advanced stage hormone relapsing prostate cancer. Cancer Biomark, 2006. 2(6): p. 249-58.
12. Zhang, G.Q., J. Du, and D. Pang, [Detection and clinical significance of serum proteomic patterns of breast cancers by surface enhanced laser desorption/ionization time of flight mass spectrometry]. Zhonghua Zhong Liu Za Zhi, 2006. 28(3): p. 204-7.
13. Hu, Y., et al., [Diagnostic application of serum protein pattern and artificial neural network software in breast cancer.]. Ai Zheng, 2005. 24(1): p. 67-71.
14. Xu, W.H., et al., Preoperatively molecular staging with CM10 ProteinChip and SELDI-TOF-MS for colorectal cancer patients. J Zhejiang Univ Sci B, 2006. 7(3): p. 235-40.
15. Qian, H.G., et al., Preliminary study on proteomics of gastric carcinoma and its clinical significance. World J Gastroenterol, 2005. 11(40): p. 6249-53.
16. Ebert, M.P., et al., Identification of gastric cancer patients by serum protein profiling. J Proteome Res, 2004. 3(6): p. 1261-6.
17. Wang, J., et al., Proteomic studies of early-stage and advanced ovarian cancer patients. Gynecol Oncol, 2008. 111(1): p. 111-9.
18. Kozak, K.R., et al., Characterization of serum biomarkers for detection of earlystage ovarian cancer. Proteomics, 2005. 5(17): p. 4589-96.
19. Geng, X., et al., SELDI-TOF MS proteinchip technology for screening of serum markers of HBV-induced hepatocellular carcinoma. J Exp Clin Cancer Res, 2007. 26(4): p. 505-8.
20. Ho, D.W., et al., Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry serum protein profiling to identify nasopharyngeal carcinoma. Cancer, 2006. 107(1): p. 99-107.
21. Hu, Y., et al., SELDI-TOF-MS: the proteomics and bioinformatics approaches in the diagnosis of breast cancer. Breast, 2005. 14(4): p. 250-5.
22. Han, M., et al., Detection and significance of serum protein markers of small-cell lung cancer. J Clin Lab Anal, 2008. 22(2): p. 131-7.
23. Petricoin, E.F., et al., Use of proteomic patterns in serum to identify ovarian cancer. Lancet, 2002. 359(9306): p. 572-7.
24. Cheng, L., et al., SELDI-TOF MS profiling of serum for detection of laryngeal squamous cell carcinoma and the progression to lymph node metastasis. J Cancer Res Clin Oncol, 2008. 134(7): p. 769-76.
25. Nakagawa, T., et al., Proteomic profiling of primary breast cancer predicts axillary lymph node metastasis. Cancer Res, 2006. 66(24): p. 11825-30.
1. Chapman, K., The ProteinChip Biomarker System from Ciphergen Biosystems: a novel proteomics platform for rapid biomarker discovery and validation. Biochem Soc Trans, 2002. 30(2): p. 82-7.
2. Fung, E.T., et al., Protein biochips for differential profiling. Curr Opin Biotechnol, 2001. 12(1): p. 65-9.
3. Issaq, H.J., et al., The SELDI-TOF MS approach to proteomics: protein profiling and biomarker identification. Biochem Biophys Res Commun, 2002. 292(3): p. 587-92.
4. Yanagisawa, K., et al., Proteomic patterns of tumour subsets in non-small-cell lung cancer. Lancet, 2003. 362(9382): p. 433-9.
5. Weinberger, S.R., T.S. Morris, and M. Pawlak, Recent trends in protein biochip technology. Pharmacogenomics, 2000. 1(4): p. 395-416.
6. Han, M., et al., Detection and significance of serum protein markers of small-cell lung cancer. J Clin Lab Anal, 2008. 22(2): p. 131-7.
7. Wang, S.J., et al., [Establishment of a diagnostic model of serum protein fingerprint pattern for esophageal cancer screening in high incidence area and its clinical value]. Zhonghua Zhong Liu Za Zhi, 2007. 29(6): p. 441-3.
8. Liu, C.Z., et al., [Serum proteomic spectra of esophageal squamous cell carcinoma patients analyzed with IMAC3 protein chip]. Ai Zheng, 2008. 27(3): p. 272-8.
9. Qian, H.G., et al., Preliminary study on proteomics of gastric carcinoma and its clinical significance. World J Gastroenterol, 2005. 11(40): p. 6249-53.
10. Tian, Y.X., et al., [Use of laser capture microdissection and surface-enhanced laser desorption ionization time-of-flight mass spectrometry to screen differential proteins in lung adenocarcinoma and lung squamous carcinoma]. Zhonghua Yi Xue Za Zhi, 2008. 88(3): p. 145-8.
11. Nakagawa, T., et al., Proteomic profiling of primary breast cancer predicts axillary lymph node metastasis. Cancer Res, 2006. 66(24): p. 11825-30.
12. Zhang, G.Q., J. Du, and D. Pang, [Detection and clinical significance of serum proteomic patterns of breast cancers by surface enhanced laser desorption/ionization time of flight mass spectrometry]. Zhonghua Zhong Liu Za Zhi, 2006. 28(3): p. 204-7.
13. Cheng, L., et al., Preliminary study of proteomic shift from normal to premalignant laryngeal lesions and to laryngeal squamous cell carcinoma. Acta Otolaryngol, 2008: p. 1-5.
14. Xu, W.H., et al., Preoperatively molecular staging with CM10 ProteinChip and SELDI-TOF-MS for colorectal cancer patients. J Zhejiang Univ Sci B, 2006. 7(3): p. 235-40.
15. Trocme, C., et al., Apolipoprotein A-I and platelet factor 4 are biomarkers for Infliximab response in rheumatoid arthritis. Ann Rheum Dis, 2008.
16. Britten, R.A., et al., Identification of reproducible low mass SELDI protein profiles specific to cisplatin resistance in human ovarian cancer cells. Oncol Rep, 2005. 14(5): p. 1323-30.
2. Wilkins, M.R., et al., From proteins to proteomes: large scale protein identification by two-dimensional electrophoresis and amino acid analysis. Biotechnology (N Y), 1996. 14(1): p. 61-5.
3. Petricoin, E.F. and L.A. Liotta, Proteomic analysis at the bedside: early detection of cancer. Trends Biotechnol, 2002. 20(12 Suppl): p. S30-4.
4. Petricoin, E.F., 3rd, et al., Serum proteomic patterns for detection of prostate cancer. J Natl Cancer Inst, 2002. 94(20): p. 1576-8.
5. Qu, Y., et al., Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. Clin Chem, 2002. 48(10): p. 1835-43.
6. Petricoin, E.F., et al., Clinical proteomics: translating benchside promise into bedside reality. Nat Rev Drug Discov, 2002. 1(9): p. 683-95.
7. Wu, W., W. Hu, and J.J. Kavanagh, Proteomics in cancer research. Int J Gynecol Cancer, 2002. 12(5): p. 409-23.
8. Weinberger, S.R., T.S. Morris, and M. Pawlak, Recent trends in protein biochip technology. Pharmacogenomics, 2000. 1(4): p. 395-416.
9. Seibert, V., et al., 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): p. 83-94.
10. Bischoff, R. and T.M. Luider, Methodological advances in the discovery of protein and peptide disease markers. J Chromatogr B Analyt Technol Biomed Life Sci, 2004. 803(1): p. 27-40.
11. Chapman, K., The ProteinChip Biomarker System from Ciphergen Biosystems: a novel proteomics platform for rapid biomarker discovery and validation. Biochem Soc Trans, 2002. 30(2): p. 82-7.
1. Parkin, D.M., et al., Global cancer statistics, 2002. CA Cancer J Clin, 2005. 55(2): p. 74-108.
2. Kanmura, S., et al., Early diagnostic potential for hepatocellular carcinoma using the SELDI ProteinChip system. Hepatology, 2007. 45(4): p. 948-56.
3. Paradis, V., et al., Identification of a new marker of hepatocellular carcinoma by serum protein profiling of patients with chronic liver diseases. Hepatology, 2005. 41(1): p. 40-7.
4. Zhang, J., et al., Proteomic Profiling of Hepatitis B Virus-related Hepatocellular Carcinoma in China: a SELDI-TOF-MS Study. Int J Clin Exp Pathol, 2008. 1(4): p. 352-61.
5. Nakagawa, T., et al., Proteomic profiling of primary breast cancer predicts axillary lymph node metastasis. Cancer Res, 2006. 66(24): p. 11825-30.
6. Gast, M.C., et al., SELDI-TOF MS serum protein profiles in breast cancer: assessment of robustness and validity. Cancer Biomark, 2006. 2(6): p. 235-48.
7. Sauter, E.R., et al., Proteomic analysis of nipple aspirate fluid using SELDI-TOF-MS. Int J Cancer, 2005. 114(5): p. 791-6.
8. Hu, Y., et al., [Diagnostic application of serum protein pattern and artificial neural network software in breast cancer.]. Ai Zheng, 2005. 24(1): p. 67-71.
9. McLerran, D., et al., SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer. Clin Chem, 2008. 54(1): p. 53-60.
10. Le, L., et al., Identification of serum amyloid A as a biomarker to distinguish prostate cancer patients with bone lesions. Clin Chem, 2005. 51(4): p. 695-707.
11. Kohli, M., et al., Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) for determining prognosis in advanced stage hormone relapsing prostate cancer. Cancer Biomark, 2006. 2(6): p. 249-58.
12. Skytt, A., et al., SELDI-TOF MS versus prostate specific antigen analysis ofprospective plasma samples in a nested case-control study of prostate cancer. Int J Cancer, 2007. 121(3): p. 615-20.
13. Wang, D.C., et al., [The application of surface-enhanced laser desorption/ionization-time of flight-mass spectrometry in diagnosing dysplasia and chronic atrophic gastric-carditis in population with high risk of gastric-cardia adenocarcinoma]. Zhonghua Nei Ke Za Zhi, 2005. 44(8): p. 573-6.
14. Ebert, M.P., et al., Identification of gastric cancer patients by serum protein profiling. J Proteome Res, 2004. 3(6): p. 1261-6.
15. Qian, H.G., et al., Preliminary study on proteomics of gastric carcinoma and its clinical significance. World J Gastroenterol, 2005. 11(40): p. 6249-53.
16. Wang, L.D., et al., [Serum proteomic profiles of the subjects with esophageal precancerous and cancerous lesions from Linzhou, an area with high incidence of esophageal cancer in Henan Province, Northern China]. Ai Zheng, 2006. 25(5): p. 549-54.
17. Nomura, F., [Clinical proteomics in laboratory medicine]. Rinsho Byori, 2006. 54(4): p. 413-20.
18. Wang, S.J., et al., [Establishment of a diagnostic model of serum protein fingerprint pattern for esophageal cancer screening in high incidence area and its clinical value]. Zhonghua Zhong Liu Za Zhi, 2007. 29(6): p. 441-3.
19. Luo, X.Y., et al., [Serum proteomic patterns may assist the diagnosis of solitary pulmonary nodules]. Zhonghua Zhong Liu Za Zhi, 2006. 28(12): p. 907-10.
20. Yang, S.Y., et al., [Application of serum surface-enhanced laser desorption/ionization proteomic patterns in distinguishing non-small cell lung cancer patients from healthy people]. Zhonghua Jie He He Hu Xi Za Zhi, 2006. 29(1): p. 31-4.
21. Yang, S., et al., Study of distinct protein profiles for early diagnosis of NSCLC using LCM and SELDI-TOF-MS. Med Oncol, 2008.
22. Au, J.S., et al., Deep proteome profiling of sera from never-smoked lung cancer patients. Biomed Pharmacother, 2007. 61(9): p. 570-7.
23. Cheng, L., et al., Preliminary study of proteomic shift from normal to premalignant laryngeal lesions and to laryngeal squamous cell carcinoma. Acta Otolaryngol, 2008: p. 1-5.
24. Wei, Y.S., et al., Identification of serum biomarkers for nasopharyngeal carcinoma by proteomic analysis. Cancer, 2008. 112(3): p. 544-51.
1. Ost, D. and A. Fein, Evaluation and management of the solitary pulmonary nodule. Am J Respir Crit Care Med, 2000. 162(3 Pt 1): p. 782-7.
2. Arita, T., et al., Is it possible to differentiate malignant mediastinal nodes from benign nodes by size? Reevaluation by CT, transesophageal echocardiography, and nodal specimen. Chest, 1996. 110(4): p. 1004-8.
3. Arita, T., et al., Bronchogenic carcinoma: incidence of metastases to normal sized lymph nodes. Thorax, 1995. 50(12): p. 1267-9.
4. McLoud, T.C., et al., Bronchogenic carcinoma: analysis of staging in themediastinum with CT by correlative lymph node mapping and sampling. Radiology, 1992. 182(2): p. 319-23.
5. Schrevens, L., et al., The role of PET scan in diagnosis, staging, and management of non-small cell lung cancer. Oncologist, 2004. 9(6): p. 633-43.
6. Gould, M.K., et al., Test performance of positron emission tomography and computed tomography for mediastinal staging in patients with non-small-cell lung cancer: a meta-analysis. Ann Intern Med, 2003. 139(11): p. 879-92.
7. Yang, S.Y., et al., [Application of serum surface-enhanced laser desorption/ionization proteomic patterns in distinguishing non-small cell lung cancer patients from healthy people]. Zhonghua Jie He He Hu Xi Za Zhi, 2006. 29(1): p. 31-4.
8. Au, J.S., et al., Proteomic approach to biomarker discovery in cancer tissue from lung adenocarcinoma among nonsmoking Chinese women in Hong Kong. Cancer Invest, 2008. 26(2): p. 128-35.
9. Xia, T., et al., [Application of SELDI-TOF serum proteome profiling in cervical squamous cell carcinoma]. Ai Zheng, 2008. 27(3): p. 279-82.
10. Wei, Y.S., et al., Identification of serum biomarkers for nasopharyngeal carcinoma by proteomic analysis. Cancer, 2008. 112(3): p. 544-51.
11. Kohli, M., et al., Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) for determining prognosis in advanced stage hormone relapsing prostate cancer. Cancer Biomark, 2006. 2(6): p. 249-58.
12. Zhang, G.Q., J. Du, and D. Pang, [Detection and clinical significance of serum proteomic patterns of breast cancers by surface enhanced laser desorption/ionization time of flight mass spectrometry]. Zhonghua Zhong Liu Za Zhi, 2006. 28(3): p. 204-7.
13. Hu, Y., et al., [Diagnostic application of serum protein pattern and artificial neural network software in breast cancer.]. Ai Zheng, 2005. 24(1): p. 67-71.
14. Xu, W.H., et al., Preoperatively molecular staging with CM10 ProteinChip and SELDI-TOF-MS for colorectal cancer patients. J Zhejiang Univ Sci B, 2006. 7(3): p. 235-40.
15. Qian, H.G., et al., Preliminary study on proteomics of gastric carcinoma and its clinical significance. World J Gastroenterol, 2005. 11(40): p. 6249-53.
16. Ebert, M.P., et al., Identification of gastric cancer patients by serum protein profiling. J Proteome Res, 2004. 3(6): p. 1261-6.
17. Wang, J., et al., Proteomic studies of early-stage and advanced ovarian cancer patients. Gynecol Oncol, 2008. 111(1): p. 111-9.
18. Kozak, K.R., et al., Characterization of serum biomarkers for detection of earlystage ovarian cancer. Proteomics, 2005. 5(17): p. 4589-96.
19. Geng, X., et al., SELDI-TOF MS proteinchip technology for screening of serum markers of HBV-induced hepatocellular carcinoma. J Exp Clin Cancer Res, 2007. 26(4): p. 505-8.
20. Ho, D.W., et al., Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry serum protein profiling to identify nasopharyngeal carcinoma. Cancer, 2006. 107(1): p. 99-107.
21. Hu, Y., et al., SELDI-TOF-MS: the proteomics and bioinformatics approaches in the diagnosis of breast cancer. Breast, 2005. 14(4): p. 250-5.
22. Han, M., et al., Detection and significance of serum protein markers of small-cell lung cancer. J Clin Lab Anal, 2008. 22(2): p. 131-7.
23. Petricoin, E.F., et al., Use of proteomic patterns in serum to identify ovarian cancer. Lancet, 2002. 359(9306): p. 572-7.
24. Cheng, L., et al., SELDI-TOF MS profiling of serum for detection of laryngeal squamous cell carcinoma and the progression to lymph node metastasis. J Cancer Res Clin Oncol, 2008. 134(7): p. 769-76.
25. Nakagawa, T., et al., Proteomic profiling of primary breast cancer predicts axillary lymph node metastasis. Cancer Res, 2006. 66(24): p. 11825-30.
1. Chapman, K., The ProteinChip Biomarker System from Ciphergen Biosystems: a novel proteomics platform for rapid biomarker discovery and validation. Biochem Soc Trans, 2002. 30(2): p. 82-7.
2. Fung, E.T., et al., Protein biochips for differential profiling. Curr Opin Biotechnol, 2001. 12(1): p. 65-9.
3. Issaq, H.J., et al., The SELDI-TOF MS approach to proteomics: protein profiling and biomarker identification. Biochem Biophys Res Commun, 2002. 292(3): p. 587-92.
4. Yanagisawa, K., et al., Proteomic patterns of tumour subsets in non-small-cell lung cancer. Lancet, 2003. 362(9382): p. 433-9.
5. Weinberger, S.R., T.S. Morris, and M. Pawlak, Recent trends in protein biochip technology. Pharmacogenomics, 2000. 1(4): p. 395-416.
6. Han, M., et al., Detection and significance of serum protein markers of small-cell lung cancer. J Clin Lab Anal, 2008. 22(2): p. 131-7.
7. Wang, S.J., et al., [Establishment of a diagnostic model of serum protein fingerprint pattern for esophageal cancer screening in high incidence area and its clinical value]. Zhonghua Zhong Liu Za Zhi, 2007. 29(6): p. 441-3.
8. Liu, C.Z., et al., [Serum proteomic spectra of esophageal squamous cell carcinoma patients analyzed with IMAC3 protein chip]. Ai Zheng, 2008. 27(3): p. 272-8.
9. Qian, H.G., et al., Preliminary study on proteomics of gastric carcinoma and its clinical significance. World J Gastroenterol, 2005. 11(40): p. 6249-53.
10. Tian, Y.X., et al., [Use of laser capture microdissection and surface-enhanced laser desorption ionization time-of-flight mass spectrometry to screen differential proteins in lung adenocarcinoma and lung squamous carcinoma]. Zhonghua Yi Xue Za Zhi, 2008. 88(3): p. 145-8.
11. Nakagawa, T., et al., Proteomic profiling of primary breast cancer predicts axillary lymph node metastasis. Cancer Res, 2006. 66(24): p. 11825-30.
12. Zhang, G.Q., J. Du, and D. Pang, [Detection and clinical significance of serum proteomic patterns of breast cancers by surface enhanced laser desorption/ionization time of flight mass spectrometry]. Zhonghua Zhong Liu Za Zhi, 2006. 28(3): p. 204-7.
13. Cheng, L., et al., Preliminary study of proteomic shift from normal to premalignant laryngeal lesions and to laryngeal squamous cell carcinoma. Acta Otolaryngol, 2008: p. 1-5.
14. Xu, W.H., et al., Preoperatively molecular staging with CM10 ProteinChip and SELDI-TOF-MS for colorectal cancer patients. J Zhejiang Univ Sci B, 2006. 7(3): p. 235-40.
15. Trocme, C., et al., Apolipoprotein A-I and platelet factor 4 are biomarkers for Infliximab response in rheumatoid arthritis. Ann Rheum Dis, 2008.
16. Britten, R.A., et al., Identification of reproducible low mass SELDI protein profiles specific to cisplatin resistance in human ovarian cancer cells. Oncol Rep, 2005. 14(5): p. 1323-30.