[1]王威),段晓冉),谭善娟),等.基于3种基因启动子甲基化联合端粒长度构建肺癌筛查神经网络模型*[J].郑州大学学报(医学版),2014,(02):176.
 WANG Wei),DUAN Xiaoran),TAN Shanjuan),et al.Establishment of neural network model of lung cancer screening based on three gene promoter methylation combined with telomere length[J].JOURNAL OF ZHENGZHOU UNIVERSITY(MEDICAL SCIENCES),2014,(02):176.
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基于3种基因启动子甲基化联合端粒长度构建肺癌筛查神经网络模型*
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《郑州大学学报(医学版)》[ISSN:1671-6825/CN:41-1340/R]

卷:
期数:
2014年02期
页码:
176
栏目:
论著
出版日期:
2014-03-20

文章信息/Info

Title:
Establishment of neural network model of lung cancer screening based on three gene promoter methylation combined with telomere length
作者:
王威1)段晓冉1)谭善娟2)姚永成1)吴逸明1)吴拥军2)#
1)郑州大学公共卫生学院劳动卫生与职业病学教研室 郑州 4500012)郑州大学公共卫生学院卫生毒理学教研室 郑州 450001
Author(s):
WANG Wei1) DUAN Xiaoran1) TAN Shanjuan2) YAO Yongcheng1) WU Yiming1) WU Yongjun2)
1)Department of Occupational Health and Occupational Diseases,College of Public Health,Zhengzhou University,Zhengzhou 450001 2)Department of Health Toxicology, College of Public Health, Zhengzhou University, Zhengzhou 450001
关键词:
DNA甲基化端粒肺癌神经网络
Keywords:
DNA methylation telomere lung cancer neural network
分类号:
A
文献标志码:
R734.2
摘要:
目的:联合检测外周血DNA中p16、RASSF1A和FHIT基因启动子甲基化水平以及DNA端粒相对长度4项生物标志,应用BP神经网络和判别模型筛查肺癌。方法:采用实时荧光定量甲基化特异PCR方法测定200例肺癌患者和200例正常对照外周血DNA中p16、RASSF1A和FHIT基因启动子甲基化水平,实时荧光定量PCR方法测定外周血DNA端粒相对长度,用BP神经网络和Fisher判别分析建立肺癌判别诊断模型,并比较预测效果。结果:肺癌组p16、RASSF1A和FHIT基因启动子甲基化平均水平高于对照组[Z(P)分别为2.641(0.008)、2.075(0.038)和3.044(0.002)],肺癌组端粒相对长度低于对照组[(0.93±0.32)、(1.16±0.57),t=4.072,P<0.001]。Fisher判别分析、C神经网络、M神经网络对预测集的ROC曲线下面积(AUC)及95%CI分别为0.670(0.569~0.761)、0.760(0.664~0.840)和0.780(0.686~0.857)。结论:人外周血DNA p16、RASSF1A、FHIT基因启动子甲基化及端粒相对长度与肺癌有关;应用神经网络模型和判别模型成功构建了基于基因启动子甲基化和端粒损伤生物标志的肺癌判别模型。
Abstract:
Aim: To apply BP neural network and discrimination model for lung cancer screening by the combined detection of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation level and the relative telomere length in peripheral blood DNA and compare their prediction results. Methods: Realtime quantitative methylation specific PCR was used to test p16, RASSF1A and FHIT gene promoter methylation level and realtime PCR was used to determine the relative telomere length in peripheral blood DNA among 200 patients with lung cancer and 200 normal controls. BP neural network and Fisher discrimination analysis was used to establish the discrimination diagnosis model of lung cancer. Results: In the lung cancer and control groups, p16, RASSF1A and FHIT gene promoter methylation level of patients with lung cancer was significantly higher than those of the normal controls, Z(P) was 2.641(0.008),2.075(0.038),and 3.044(0.002) respectively. The relative telomere length of patients with lung cancer was significantly lower than those of the normal controls[(0.93±0.32),(1.16±0.57),t=4.072,P<0.001]. The area under the ROC curve and 95%CI of prediction set from Fisher discrimination analysis, Cneural network and Mneural network was 0.670(0.569-0.761),0.760(0.664-0.840) and 0.780(0.686-0.857). Conclusion: p16, RASSF1A, FHIT gene promoter methylation and relative telomere length in peripheral blood DNA are associated with lung cancer. BP neural network model and Fisher discrimination analysis for lung cancer prediction have a certain significance in screening for lung cancer.

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备注/Memo

备注/Memo:
*国家自然科学基金资助项目30972457,81001239;河南省重大科技攻关基金资助项目112102310102;河南省医学科技攻关计划基金资助 项目2011020082 #通讯作者,男,1968年1月生,博士,教授,研究方向:肺癌的早期诊断,Email: wuyongjun@zzu.edu.cn
更新日期/Last Update: 2014-04-23