Supplementary Table 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.

研究背景：异常DNA甲基化（Aberrant DNA methylation）是诊断生物标志物的丰富来源，但其诊断准确性与临床应用价值仍有待进一步提升。
研究方法：针对226例肺腺癌患者的配对正常组织与I/IV期肿瘤组织样本，开展大panel亚硫酸氢盐测序（bisulfite sequencing），以鉴定差异甲基化区域（differentially methylated regions, DMRs）。
研究结果：随机森林（Random Forest）模型可实现较高的预测准确率，其灵敏度达96%、特异度达97.56%，能够有效区分正常对照与早期及晚期癌症样本，性能优于绝大多数已在肺腺癌中验证的既往预测模型。
研究结论：本研究结果表明，将随机森林模型与靶向亚硫酸氢盐测序相结合，在癌症筛查阶段精准预测并早期诊断肺腺癌方面具备极大的临床应用潜力。