膀胱移行细胞癌早期诊断、两类癌分类及复发的预警系统
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摘要
目的
建立膀胱移行细胞癌(BTCC)预警系统,为BTCC的早期诊断、两类癌分类及复发的预测提供新的方法和策略,为制定合理的临床治疗方案提供有效的指导,以期达到提高BTCC患者生存率、改善预后的目的。
方法
1对尿液蛋白质组双向电泳的样品制备、上样量、水化方式、水化上样液成分、IPG胶条选择及电泳程序参数等进行比较研究和条件优化；凝胶经质谱兼容的硝酸银染色后,采用PDQuest8.02-DE凝胶图像分析软件进行点检测与匹配。
2利用二维电泳-质谱-生物信息学技术对40例BTCC患者(其中低恶性24例,高恶性16例)和16例健康志愿者的尿液进行比较蛋白质组学研究,对得到的差异蛋白进行鉴定和验证。使用受试者工作(ROC)曲线确定载脂蛋白A1对BTCC早期诊断和两类癌分类的最佳工作点(OOP),计算相应的特异度和灵敏度。
3应用蛋白芯片技术对BTCC患者(其中低恶性12例,高恶性12例)与14例健康志愿者的尿液进行比较蛋白质组学研究,应用支持向量机(SVM)进行数据挖掘,构建BTCC早期诊断和两类癌分类的判别模型,并对差异蛋白峰进行生物信息学分析。
4回顾性分析167例复发性BTCC患者的临床病理资料,结合病理标本中p53和FGFR3的蛋白表达水平,运用多因素二分类Logistic回归方法和Cox回归方法,寻找影响BTCC复发类型和复发时间的相关因素。
结果
1获得了分辨率高、重复性好的尿液双向电泳图谱；蛋白点数目为872+18个,匹配率达到76.26%。
2鉴定了5种在BTCC患者尿液中含量增高的蛋白质：纤维蛋白原、乳酸脱氢酶B、载脂蛋白A-I、丛集蛋白和触珠蛋白。ROC曲线提示以18.22为临界点可将BTCC患者与非患者进行较好区分,灵敏度为90%,特异度为81.25%；当以29.85为临界点时可将低恶性BTCC与高恶性BTCC进行较好区分,灵敏度为87.5%,特异度为75%。
3建立由质荷比为7267.417,2339.6423,1885.7951的差异蛋白峰组成的BTCC早期诊断判别模型可将BTCC患者与非患者进行较好区分,灵敏度为91.7%,特异度为78.6%。由质荷比为4249.1776,2676.1477,2831.5038,5499.2115的差异蛋白峰组成的BTCC两类癌分类的判别模型可将两类BTCC患者进行较好区分,特异度为75%,灵敏度为83.3%
4Logistic回归结果提示,与BTCC复发后类型相关的因素为初发分类,年龄和FGFR3; Cox回归结果提示影响BTCC术后无复发时间的独立因素为初发分类、血管浸润、多发性肿瘤和P53。
结论
1建立了稳定的尿液蛋白质双向凝胶电泳技术平台；
2初步建立BTCC早期诊断及两类癌分类的预警系统；
3SELDI-TOF-MS结合SVM构建的判别模型为BTCC早期诊断及两类癌分类提供了一种特异性强、敏感性高的新方法；
4初发分类为高恶性BTCC、年龄>65、FGFR3阴性的患者在复发时为高恶性BTCC的风险增大；初发分类为高恶性BTCC、血管浸润阳性、肿瘤为多发或P53阳性的患者易在短期内复发。两类癌分类方法对BTCC无复发时间和复发类型的预测有指导意义。
Objective
To Establish an early warning system of bladder transitional cell carcinoma(BTCC) for early diagnosis, classification of two-tie grading system and recurrence prediction, which may provide a guideline for clinical diagnostic and therapeutic targets. Elevating the survival rate and improving the prognostic of BTCC patient is the final objectives.
Methods
1Various conditions of2-DE for urine were optimized by comparative study, including sample preparation, volume of loading sample, composition of rehydration sample buffer, selection of IPG dry strip, program and parameters of electrophoresis. The2-DE gels were stained by using sliver dye and analyzed by ChemiDOC XRS and PDQuest8.02-DE analysis software.
2The urine proteome derived from low malignant BTCC patients, aggressive BTCC patients and healthy donors were compared by2DE-mass spectrometry-bioinfomatics technique.2-DE and ELISA were used to confirm the difference of APO-AI content in urine. The Optimal Operating Point(OOP) of APO-A1for early diagnosis and classification of two-tie grading system of BTCC was determined by using the Receiver Operating Characteristic(ROC) curve. Clinical value and discriminatory power were evaluated.
338urine samples, including12low malignant BTCC,12aggressive BTCC and14healthy donors, were tested by H4protein chip and surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS).The differentially expressed proteins peaks were analyzed by BioMarker Wizard Software, support vector machine(SVM) was applied to construct identify protein profiles which can distinguish BTCC from health,low malignant BTUC from aggressive BTCC. The differentially expressed proteins peaks were identified initially with bioinfomatics.
4The clinical data of167patients with recurring transitional cell carcinoma of bladder were collected and analyzed retrospectively. Combined with protein expression of p53and FGFR3in their paraffin samples detected by immunohistochemistry. A multivariate analysis on recurrence type was performed by the binary logistic regression analysis and a analysis on relapse-free time-related factors was made by Cox's proportional hazard model analysis.
Results
1Two-dimensional electrophoregrams with higher resolution and better reproducibility were obtained,872±18protein spots were detected, the match ratio was76.26%.
2Five proteins that increased in urine of BTCC patients were identified through the database search, including fibrinogen, lactate dehydrogenase B, apoA1, clusterin and haptoglobin. The ROC curve indicate BTCC patients will be well discriminated from the controls by using18.22as an OPP. The sensitivity was90%and the specificity was81.25%. Patients with low malignant BTCC will be well discriminated from aggressive BTCC using29.85as an OPP. The sensitivity was87.5%and the specificity was75%.
3We have established a3-peptide profile (7267.417,2339.642,1885.7951) which can separate patients with BTCC from the controls with the sensitivity of91.7%and the specificity of78.6%. Another4-peptide profile (4249.1776,2676.1477,2831.5038,5499.2115) which can separate patients with low malignant BTCC from aggressive BTCC with the sensitivity of83.3%and the specificity of75%.
4Logistic regression analysis indicated recurrent type of BTCC patients is correlated with primary type, age and FGFR3. Cox regression analysis showed primary type, blood vessel infiltrate, multiple tumor and P53influenced the relapse-free time.
Conclusions
1A stable technical platform of2-DE for urine proteome was established.
2An early warning system of BTCC for early diagnosis, classification of two-tie grading system was established
3The identify protein profiles which combined SELDI-TOF-MS with SVM provides a new approach with high specificity and sensitivity for early diagnosis, classification of two-tie grading of BTCC.
4Patients with aggressive BTCC, older than65and negative expression of FGFR3might have a high risk to aggressive BTCC as recurrence; Patients with aggressive BTCC, multiple tumors, without blood vessel infiltration and P53expression trend to recur in short time. Classification of two-tie grading system provides guidance for prediction of relapse-free time and recurrence type of BTCC.
建立膀胱移行细胞癌(BTCC)预警系统,为BTCC的早期诊断、两类癌分类及复发的预测提供新的方法和策略,为制定合理的临床治疗方案提供有效的指导,以期达到提高BTCC患者生存率、改善预后的目的。
方法
1对尿液蛋白质组双向电泳的样品制备、上样量、水化方式、水化上样液成分、IPG胶条选择及电泳程序参数等进行比较研究和条件优化；凝胶经质谱兼容的硝酸银染色后,采用PDQuest8.02-DE凝胶图像分析软件进行点检测与匹配。
2利用二维电泳-质谱-生物信息学技术对40例BTCC患者(其中低恶性24例,高恶性16例)和16例健康志愿者的尿液进行比较蛋白质组学研究,对得到的差异蛋白进行鉴定和验证。使用受试者工作(ROC)曲线确定载脂蛋白A1对BTCC早期诊断和两类癌分类的最佳工作点(OOP),计算相应的特异度和灵敏度。
3应用蛋白芯片技术对BTCC患者(其中低恶性12例,高恶性12例)与14例健康志愿者的尿液进行比较蛋白质组学研究,应用支持向量机(SVM)进行数据挖掘,构建BTCC早期诊断和两类癌分类的判别模型,并对差异蛋白峰进行生物信息学分析。
4回顾性分析167例复发性BTCC患者的临床病理资料,结合病理标本中p53和FGFR3的蛋白表达水平,运用多因素二分类Logistic回归方法和Cox回归方法,寻找影响BTCC复发类型和复发时间的相关因素。
结果
1获得了分辨率高、重复性好的尿液双向电泳图谱；蛋白点数目为872+18个,匹配率达到76.26%。
2鉴定了5种在BTCC患者尿液中含量增高的蛋白质：纤维蛋白原、乳酸脱氢酶B、载脂蛋白A-I、丛集蛋白和触珠蛋白。ROC曲线提示以18.22为临界点可将BTCC患者与非患者进行较好区分,灵敏度为90%,特异度为81.25%；当以29.85为临界点时可将低恶性BTCC与高恶性BTCC进行较好区分,灵敏度为87.5%,特异度为75%。
3建立由质荷比为7267.417,2339.6423,1885.7951的差异蛋白峰组成的BTCC早期诊断判别模型可将BTCC患者与非患者进行较好区分,灵敏度为91.7%,特异度为78.6%。由质荷比为4249.1776,2676.1477,2831.5038,5499.2115的差异蛋白峰组成的BTCC两类癌分类的判别模型可将两类BTCC患者进行较好区分,特异度为75%,灵敏度为83.3%
4Logistic回归结果提示,与BTCC复发后类型相关的因素为初发分类,年龄和FGFR3; Cox回归结果提示影响BTCC术后无复发时间的独立因素为初发分类、血管浸润、多发性肿瘤和P53。
结论
1建立了稳定的尿液蛋白质双向凝胶电泳技术平台；
2初步建立BTCC早期诊断及两类癌分类的预警系统；
3SELDI-TOF-MS结合SVM构建的判别模型为BTCC早期诊断及两类癌分类提供了一种特异性强、敏感性高的新方法；
4初发分类为高恶性BTCC、年龄>65、FGFR3阴性的患者在复发时为高恶性BTCC的风险增大；初发分类为高恶性BTCC、血管浸润阳性、肿瘤为多发或P53阳性的患者易在短期内复发。两类癌分类方法对BTCC无复发时间和复发类型的预测有指导意义。
Objective
To Establish an early warning system of bladder transitional cell carcinoma(BTCC) for early diagnosis, classification of two-tie grading system and recurrence prediction, which may provide a guideline for clinical diagnostic and therapeutic targets. Elevating the survival rate and improving the prognostic of BTCC patient is the final objectives.
Methods
1Various conditions of2-DE for urine were optimized by comparative study, including sample preparation, volume of loading sample, composition of rehydration sample buffer, selection of IPG dry strip, program and parameters of electrophoresis. The2-DE gels were stained by using sliver dye and analyzed by ChemiDOC XRS and PDQuest8.02-DE analysis software.
2The urine proteome derived from low malignant BTCC patients, aggressive BTCC patients and healthy donors were compared by2DE-mass spectrometry-bioinfomatics technique.2-DE and ELISA were used to confirm the difference of APO-AI content in urine. The Optimal Operating Point(OOP) of APO-A1for early diagnosis and classification of two-tie grading system of BTCC was determined by using the Receiver Operating Characteristic(ROC) curve. Clinical value and discriminatory power were evaluated.
338urine samples, including12low malignant BTCC,12aggressive BTCC and14healthy donors, were tested by H4protein chip and surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS).The differentially expressed proteins peaks were analyzed by BioMarker Wizard Software, support vector machine(SVM) was applied to construct identify protein profiles which can distinguish BTCC from health,low malignant BTUC from aggressive BTCC. The differentially expressed proteins peaks were identified initially with bioinfomatics.
4The clinical data of167patients with recurring transitional cell carcinoma of bladder were collected and analyzed retrospectively. Combined with protein expression of p53and FGFR3in their paraffin samples detected by immunohistochemistry. A multivariate analysis on recurrence type was performed by the binary logistic regression analysis and a analysis on relapse-free time-related factors was made by Cox's proportional hazard model analysis.
Results
1Two-dimensional electrophoregrams with higher resolution and better reproducibility were obtained,872±18protein spots were detected, the match ratio was76.26%.
2Five proteins that increased in urine of BTCC patients were identified through the database search, including fibrinogen, lactate dehydrogenase B, apoA1, clusterin and haptoglobin. The ROC curve indicate BTCC patients will be well discriminated from the controls by using18.22as an OPP. The sensitivity was90%and the specificity was81.25%. Patients with low malignant BTCC will be well discriminated from aggressive BTCC using29.85as an OPP. The sensitivity was87.5%and the specificity was75%.
3We have established a3-peptide profile (7267.417,2339.642,1885.7951) which can separate patients with BTCC from the controls with the sensitivity of91.7%and the specificity of78.6%. Another4-peptide profile (4249.1776,2676.1477,2831.5038,5499.2115) which can separate patients with low malignant BTCC from aggressive BTCC with the sensitivity of83.3%and the specificity of75%.
4Logistic regression analysis indicated recurrent type of BTCC patients is correlated with primary type, age and FGFR3. Cox regression analysis showed primary type, blood vessel infiltrate, multiple tumor and P53influenced the relapse-free time.
Conclusions
1A stable technical platform of2-DE for urine proteome was established.
2An early warning system of BTCC for early diagnosis, classification of two-tie grading system was established
3The identify protein profiles which combined SELDI-TOF-MS with SVM provides a new approach with high specificity and sensitivity for early diagnosis, classification of two-tie grading of BTCC.
4Patients with aggressive BTCC, older than65and negative expression of FGFR3might have a high risk to aggressive BTCC as recurrence; Patients with aggressive BTCC, multiple tumors, without blood vessel infiltration and P53expression trend to recur in short time. Classification of two-tie grading system provides guidance for prediction of relapse-free time and recurrence type of BTCC.
引文
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