Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer

被引:32
|
作者
Duan, Xiaoran [1 ]
Yang, Yongli [2 ]
Tan, Shanjuan [3 ]
Wang, Sihua [4 ]
Feng, Xiaolei [5 ]
Cui, Liuxin [1 ]
Feng, Feifei [6 ]
Yu, Songcheng [7 ]
Wang, Wei [5 ]
Wu, Yongjun [6 ]
机构
[1] Zhengzhou Univ, Dept Environm Hlth, Coll Publ Hlth, Zhengzhou, Henan, Peoples R China
[2] Zhengzhou Univ, Dept Epidemiol & Biostat, Coll Publ Hlth, Zhengzhou, Henan, Peoples R China
[3] Qingdao Municipal Hosp, Dept Hosp Infect Management, Qingdao, Peoples R China
[4] Henan Inst Occupat Hlth, Dept Occupat Hlth, Zhengzhou, Henan, Peoples R China
[5] Zhengzhou Univ, Dept Occupat Hlth & Occupat Med, Coll Publ Hlth, Zhengzhou, Henan, Peoples R China
[6] Zhengzhou Univ, Dept Hlth Toxicol, Coll Publ Hlth, Zhengzhou, Henan, Peoples R China
[7] Zhengzhou Univ, Dept Sanit Chem, Coll Publ Hlth, Zhengzhou, Henan, Peoples R China
基金
中国国家自然科学基金;
关键词
Artificial neural network; DNA methylation; Telomere; Lung cancer; Auxiliary diagnosis; METHYLATION MARKERS; PERIPHERAL-BLOOD; DNA METHYLATION; BLADDER-CANCER; FHIT GENE; HYPERMETHYLATION; CLASSIFICATION; EXPRESSION; RISK;
D O I
10.1007/s11517-016-1585-7
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 +/- 0.32) were significantly lower than those of the normal controls (1.16 +/- 0.57), t = 4.072, P < 0.001. The areas under the ROC curve (AUC) and 95 % CI of prediction set from Fisher discrimination analysis and BP neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.
引用
收藏
页码:1239 / 1248
页数:10
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