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

Year: 2026

Type: article

Abstract: 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. It is hard to tell if small pulmonary nodules are harmless or cancerous and need surgery. Our study looked at whether a routine blood test could help. We examined special markers on DNA from blood immune cells of patients with pulmonary nodules. Then, we trained computer models to recognize patterns in these markers. The models were very good at telling which nodules were cancerous and needed surgery. They could also separate slow-growing cancers from faster-growing ones, helping doctors plan the right region for surgery. This means that in the future, a simple blood test might help avoid unnecessary operations. Yin, Zhang, Qiu et al. profile genome‑wide DNA methylation in PBMCs and combine validated methylation features with routine blood biomarkers to build machine learning models for pulmonary nodule classification. The models accurately distinguish benign from malignant nodules and minimally invasive from early invasive adenocarcinoma, supporting PBMC methylation signatures as practical, non‑invasive tools to inform surgical decision‑making.

Source: Communications Medicine

DOI: 10.1038/s43856-026-01616-4