血清蛋白质指纹图谱在胰腺癌诊断预后和疗效评估中的应用

Application of serum protein profiling in diagnosis, prognosis and evaluation of curative effect of pancreatic adenocarcinoma

目的建立胰腺癌血清学诊断模型，探讨评估胰腺癌分期和治疗疗效的生物学标志物。方法采用强阴离子交换（SAX2）芯片和表面增强激光解吸电离飞行时间质谱仪（SELDI-TOF—MS）技术，测定58例胰腺癌患者和51例正常对照者的血清蛋白质指纹图谱；应用BiomarkerWizard统计软件以及决策树算法、Logistic回归和ROC曲线，建立决策树和二分类回归诊断模型。结果在质荷比（M／Z）值为2000～30000范围内，建立10个决策树诊断模型，其预测胰腺癌的正确率为92．9％（13／14），诊断胰腺癌的敏感性为83．3％，特异性为100％。应用Logistic回归联合多种差异蛋白峰诊断胰腺癌的曲线下面积（AUC）为0．976±0．011（P〈0．001），敏感性为77．6％～91．4％，特异性为92．2％～100％。联合6个差异蛋白峰诊断胰腺癌Ⅰ与Ⅱ期、Ⅱ与Ⅲ期以及Ⅲ与Ⅳ期的AUC值分别为0．897±0．054、0．978±0．021和0．792±0．107（P〈0．05）。胰腺癌组中高表达的差异蛋白峰（M／Z4016）在手术前后有下调趋势（P〈0．05）。结论应用SELDI-TOF—MS技术进行胰腺癌血清蛋白质指纹图谱分析，采用统计学方法建立胰腺癌决策树和二分类回归诊断模型，对胰腺癌的诊断和鉴别诊断有一定的价值；筛选出的差异蛋白峰对胰腺癌的预后和疗效评估有一定的应用价值。

Objective To establish decision tree and logistic regression classification models for diagnosing pancreatic adenocarcinoma （PaCa） and for screening serum biomarkers related to evaluation of different stages and curative effects. Methods Serum samples obtained from subjects with pancreatic adenocarcinoma （ n = 58 ） and normal pancreas （ n =51 ） were applied to strong anion exchange chromatography （SAX2） chips for protein profiling by SELDI-TOF-MS to screen multiple serum biomarkers. Biomarker Wizard software and several statistical methods including algorithm of decision tree, logistic regression and ROC curves were used to construct the decision tree or logistic regression classification models. Results Average of 61 mass peaks were detected at the molecular range of 2000-30 000, ten decision trees with the highest cross validation rate were chosen to construct the classification models, which can differentiate PaCa from normal pancreas with a sensitivity of 83.3% and a specificity of 100%. Logistic regression was used to achieve the AUC （0.976±0. 011 ,P 〈 0.001 ） with a sensitivity of 77.6%-91.4% and a specificity of 92.2% -100%. Six mass peaks were combined by logistic regression to achieve the AUC 0.897±0.054, 0. 978±0.021 and 0. 792±0.107 （ P 〈 0.05 ） in the three groups （ patients at stage Ⅰand Ⅱ, stage Ⅱ and Ⅲ, stage Ⅲ and Ⅳ）. One mass peak （M/Z 4 016） was screened （P 〈0.05） significantly between the preoperative and postoperative PaCa samples and the intensity decreased weeks after operation. Conclusion Decision tree and logistic regression classification models of the mass peaks screened by SELDI-TOF-MS serum profiling can be used to differentiate pancreatic adenocarcinoma from normal pancreas, and is superior to CA 199. The detected mass peaks are helpful for the evaluation of curative effect and prognosis of pancreatic adenocarcinoma.