Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

Abstract

Background: Aberrant DNA methylations serve as rich sources of diagnostic biomarkers, but a further improvement in their accuracy and clinical utility is warranted. Methods: Large panel bisulfite sequencing was performed on paired normal and stage I/IV tumors from 226 lung adenocarcinoma cancer patients to characterize the differentially methylated regions (DMRs). Results: Random forest model achieved high prediction accuracy (sensitivity 96% and specificity 97.56%) to separate normal controls from both early and advanced cancer samples, which is superior to most previous prediction models tested in lung adenocarcinoma. Conclusion: Our results suggest that combining the random forest model with targeted bisulfite sequencing have great clinical potential to accurately predict and diagnose lung adenocarcinoma early during cancer screening.