【摘要】 目的:在免疫组化分型的基础上,探讨弥漫性大B细胞淋巴瘤患者石蜡标本基因表达与免疫组化分型之间的关系。方法:收集弥漫性大B细胞淋巴瘤患者石蜡切片,常规免疫组化检测CD10、Bcl-6和MUM1,按Hans分类标准进行亚型分类。石蜡组织中提取RNA,real-time PCR法检测Bcl-2、CCND2、LMO2、FOXP1、Bcl-6、HGAL、FN1、CCL3、MME、MUM1和REL基因的表达。利用Logistic回归建立预测模型,并用ROC曲线对其应用价值进行评价。结果:免疫组化结果显示,CD10阳性率为17.07%,Bcl-6阳性率为78.05%,MUM1阳性率为60.98%,Hans标准分型GCB型24.39%,non-GCB型75.61%。Logistic回归模型单因素分析结果表明,MME、LMO2和Bcl-2基因与DLBCL的免疫分型有关,P<0.05;多因素逐步回归分析显示,MME、LMO2和Bcl-2有显著回归效果,建立预测模型公式P=e-3.946-0.687×Bcl-2+0.199×MME+0.421×LMO2/(1+e-3.946-0.687×Bcl-2+0.199×MME+0.421×LMO2)。ROC曲线显示,该模型的曲线下面积为0.970,灵敏度为0.900,特异度为0.968。该模型判断DLBCL分型与免疫组化的总符合率为95.1%。结论:从石蜡中提取RNA,real-time PCR检测基因表达,建立Logistic回归模型,利用该模型对DLBCL分型是可行的。更多还原

【Abstract】 OBJECTIVE:To detect the expression of mRNA and protein in diffuse large-B-cell lymphoma patients samples,and study the relationship between the expression of mRNA and subgroups base on the immunohistochemisty.METHODS:Collect the paraffin samples of diffuse large-B-cell lymphoma patients.Divide the patients into two subgroups according to the immunohischemisty of CD10,Bcl-6 and MUM1.Extract RNA from the paraffin-embedded tissues,Measure the expression of Bcl-2,CCND2,LMO2,FOXP1,Bcl-6,HGAL,FN1,CCL3,MME,MUM1 and REL by real-time RCR.Construct a predictive model through Logistic regression and evaluate the application value by ROC curve.RESULTS:According to the results of immunohistochemisty,the positive rate of CD10 was 17.07%,the positive rate of Bcl-6 is 78.05%,and the positive rate of MUM1 was 60.98%.The percent of GCB was 24.39% and non-GCB was 75.61% according to the algorithm described by Hans.The results of univariate Logistic regression indicated that the expressions of MME,LMO2 and Bcl-2 were associated with DLBCL immunohistochemical classification(P<0.05),and the results of multivariate stepwise regression showed that the regression effect of MME,LMO2 and Bcl-2 were significance.Constructed the predictive model formula:P=e-3.946-0.687*Bcl-2+0.199*MME+0.421*LMO2/(1+e-3.946-0.687*Bcl-2+0.199*MME+0.421*LMO2).According to ROC curve AUC of the model was 0.970,sensitivity was 0.900 and specificity was 0.968.The model predicted DLBCL classification of 95.1% in accordance with the result of immunohistochemisty.CONCLUSION:The constructed Logistic regression model is valuable for DLBCL classification.更多还原