摘要
目的:联合6项肿瘤标志及4项表观遗传学指标构建决策树C5. 0模型对肺癌进行判别,以寻找诊断肺癌的最优模型。方法:在180例肺癌患者及243例肺良性疾病患者中,采用放射免疫法应用血清癌胚抗原(CEA)试剂盒和胃泌素试剂盒测定患者CEA及胃泌素;采用双抗体夹心法应用神经元特异性烯醇化酶(NSE)测定试剂盒测定NSE;采用邻甲酚酞络合酮比色法应用钙(Ca)测定试剂盒测定血清Ca的浓度;采用分光光度法通过改进的对比色法测定唾液酸(SA)的浓度;采用原子吸收分光光度法(ABS)测定血清铜(Cu)、锌(Zn)的浓度;采用酶联免疫吸附试验(ELISA)测定血清样本中DNA甲基转移酶1(DNA methyltransferase 1,DNMT1)、DNA甲基转移酶3A(DNA methyltransferase 3A,DNMT3A)、DNA甲基转移酶3B(DNA methyltransferase 3B,DNMT3B)、组蛋白去乙酰化酶1(histone deacetylase,HDAC1)的含量,并构建诊断模型。结果:决策树C5. 0模型在纳入6项肿瘤标志、4项表观遗传学指标、10项肿瘤标志的灵敏度分别是0. 383、0. 378、0. 944,特异度分别为0. 905、0. 864、0. 934,准确度分别为0. 683、0. 657、0. 939,ROC曲线下面积(AUC)分别为0. 644、0. 621、0. 939。其中纳入10项肿瘤标志的决策树C5. 0模型的灵敏度、特异度、准确度及AUC均在90%以上,且优于只纳入6项肿瘤标志、4项表观遗传学指标,各组数据的差异均有统计学意义(P <0. 05)。结论:基于10项肿瘤标志的肺癌判别模型优于纳入6项生物肿瘤标志及4项表观遗传学标志的模型,提高了肺癌诊断率。
Objective:To distinguish lung cancer based on 6 tumor markers of lung cancer and 4 epigenetic tumor markers combined with classifying models of decision tree C5.0(CART C5.0),and develop the best model for the diagnosis of lung cancer.Methods:The levels of serum CEA,gastrin,NSE,sialic acid(SA),Ca,Cu/Zn,DNMT1,DNMT3A,DNMT3B,HDAC1 in 180 patients with lung cancer and 243 patients with lung benign disease were detected by means of radioimmunology,double antibody sandwich method,modified resorcinol chromogenic method,fully automated analyzer,atomic absorption spectrophotometry,enzyme linked immunosorbent assay(ELISA),respectively,and then developed CART C5.0 models.Results:The sensitivities of 6 tumor markers,4 epigenetic tumor markers and 10 tumor markers in the CART C5.0 model were 0.383,0.378,0.944,respectively.The specificities were 0.905,0.864,0.934,respectively.The accuracies were 0.683,0.657,0.939.The areas under receiver operating curve(AUC)were 0.644,0.621,0.939,respectively.The sensitivity,specificity,accuracy and AUC of 10 tumor markers were over 90%,and better than the sensitivities of 6 tumor markers,4 epigenetic tumor markers models,and there were statistical difference(P<0.05).Conclusion:The CART C5.0 model based on 10 tumor markers is obviously superior to that of the 6 biomarkers and 4 epigenetic markers,which improved the diagnosis rate of lung cancer.
作者
周晓蕾
王献红
李尊税
张曼林
袁彦丽
吴拥军
ZHOU Xiaolei;WANG Xianhong;LI Zunshui;ZHANG Manlin;YUAN Yanli;WU Yongjun(Department of Respiratory Medicine,Henan Chest Hospital,Henan Zhengzhou 450052,China;Zhengzhou Shuqing Medical College,Henan Zhengzhou 450001,China;School of Public Health,Zhengzhou University,Henan Zhengzhou 450001,China)
出处
《中国医药导刊》
2018年第11期641-645,共5页
Chinese Journal of Medicinal Guide
基金
国家自然科学基金(项目编号:81573203
项目名称:基于数据挖掘技术的蛋白标志物与基因突变联合检测的分子诊断系统构建及应用)
关键词
决策树
肺癌
诊断
肿瘤标志
表观遗传学
Decision tree
Lung cancer
Diagnosis
Tumor marker
Epigenetic
作者简介
周晓蕾,女,主治医师,研究方向:呼吸及结核疾病。E-mail:zhoulei1212@163.com;通讯作者:吴拥军,男,教授,研究方向:肺癌的诊断。E-mail:wuyongjun@zzu.edu.cn
