Application of decision tree combined with biomarkers in auxiliary diagnosis of lung cancer

Objective To diagnose lung cancer by detections of p16,RASSF1A,fragile histidine traid(FHIT)genes promoter methylation status,and relative telomere length in peripheral blood DNA and to identify the significances of discrimination analysis and decision tree for auxiliury diagnosis of lung cancer.Methods The levels of p16,RASSF1A,FHIT genes promoter methylation,and relative telomere length in peripheral blood DNA of 200 healthy individuals and 200 patients with lung cancer were measured by SYBR green-based quantitative methylation-specific PCR and quantitative PCR,respectively,and then discrimination analysis and decision tree models were developed.Results The levels(95% confidence interval)of p16,RASSF1A,and FHIT genes promoter methylation of the lung cancer patients and healthy individuals were 0.59(0.16-4.50)and 0.36(0.06-4.00)(P=0.008),27.62(9.09-52.86)and 17.17(3.86-50.87)(P=0.038),and 3.33(1.86-6.40)and 2.85(1.39-5.44)(P=0.002),respectively,and the relative telomere lengths were 0.93±0.32 and 1.16±0.57(P<0.001).There were statistically significant differences in the four biomarkers between the two groups.The accuracies of discrimination analysis and decision tree models were 64% and 83%,respectively and the areas under receiver operating curve were 0.640 and 0.830,with statistically significant differences between the two groups(P<0.05 for all).ConclusionThe efficacy of decision tree model established with data mining tool is better than that of discrimination analysis in auxiliary diagnose of lung cancer auxiliary.