Supplementary Figure 3: Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

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 were 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 potentials to accurately predict and early diagnose lung adenocarcinoma during cancer screening.

背景：异常的DNA甲基化作用作为丰富的诊断生物标志物来源，但其准确性和临床应用价值的进一步提升尚属必要。方法：对226例肺腺癌患者的配对正常组织和I/IV期肿瘤样本进行了大规模的亚硫酸氢盐测序，以描述差异甲基化区域（DMRs）。结果：随机森林模型在区分正常对照组与早期及晚期癌症样本方面实现了高预测准确性（灵敏度96%，特异性97.56%），优于大多数先前在肺腺癌中测试的预测模型。结论：我们的研究结果提示，将随机森林模型与靶向亚硫酸氢盐测序相结合，在癌症筛查过程中，具有准确预测和早期诊断肺腺癌的巨大临床潜力。