Peripheral blood mononuclear cell DNA methylation signatures guide surgical decision-making in indeterminate...

Distinguishing malignant from benign pulmonary nodules remained a significant clinical challenge. Given the involvement of DNA methylation in anti-tumor immunity, we aimed to investigated whether DNA methylation patterns in peripheral blood mononuclear cells (PBMCs) could serve as non-invasive biomarkers for pulmonary nodule classification.

Genome-wide DNA methylation profiling was performed using Methylome-seq on PBMCs from Discovery cohort, including patients with benign pulmonary nodules (BPN), minimally invasive adenocarcinoma (MIA), and early-stage invasive adenocarcinoma (eIAC). Differential analysis identified 56 candidates differentially methylated cytosines (DMCs) and regions (DMRs). Subsequently, 2 methylation features were validated using targeted bisulfite sequencing (TBS) in the Train cohort and Test cohort. We further developed 2 machine learning models integrating the DNA methylation features and routine blood biomarkers to enhance pulmonary nodule classification accuracy. SurgMalig Model was a surgical malignancy classifier used to differentiate between benign nodules and malignant nodules requiring surgical resection. And ResectGuide Model was a resection guide classifier used to subdivide malignant nodules into MIA and eIAC to inform surgical planning.

The validated methylation features demonstrate significant discriminatory ability for pulmonary nodules properties compared to conventional blood biomarkers. SurgMalig Model achieves high performance in distinguishing benign from malignant nodules, with area under the curve (AUC) of 0.926 in the Train cohort and 0.806 in the Test cohort. ResectGuide Model effectively classifies MIA and eIAC subtypes, yielding AUC of 0.862 and 0.843 in the respective cohorts.

PBMC methylation signatures provide clinically actionable tools for surgical triage in indeterminate pulmonary nodules.