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

Sep 22, 2026

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Peripheral blood mononuclear cell (PBMC) DNA methylation patterns can support non-invasive classification of indeterminate pulmonary nodules, where distinguishing malignant from benign remains a clinical challenge. Genome-wide methylome profiling used Methylome-seq to identify 56 differential methylated cytosines and regions, then validated two methylation features via targeted bisulfite sequencing in discovery and independent cohorts. Two machine-learning models combine methylation features with routine blood biomarkers to guide surgery. Results show strong discrimination between benign and malignant nodules and between MIA and early-stage IAC subtypes.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages14

Reading Time35 min

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Abstract

Background

Methods

Results

Conclusions

Plain language summary

A Nature Portfolio journal [https://doi.org/10.1038/s43856-026-01616-4](https://doi.org/10.1038/s43856-026-01616-4) | Peripheral blood mononuclear cell DNA methylation signatures guide surgical decision-making in indeterminate pulmonary nodules
Check for updates | | | | --- | --- | --- | | Haofan Yin 1,2,3,9, Ningfang Zhang1,2,3,9, Lumei Qiu1,2,3,9, Jiaju Zhang4,5, Jiahui Ding6, Jiannan Xu 7 , Zhijian Huang8 & Xiaopeng Yuan1,2,3 | | | | Abstract
Background Distinguishing malignant from benign pulmonary nodules remained asigniﬁcant 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.
Methods 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 usedto subdivide malignant nodules into MIA andeIACto inform surgical planning. Results 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 andeIAC subtypes, yieldingAUCof0.862 and 0.843 in the respective cohorts.
Conclusions PBMC methylation signatures provide clinically actionable tools for surgical triage in indeterminate pulmonary nodules. | | Plain language summary
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 DNAfrom 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. | | Lung cancer remains the most commonly diagnosed malignancy worldwide1. Despite signiﬁcant advancements in treatment modalities in recent years, the overall survival rate of lung cancer patients remains low, primarily due to the fact that most patients are diagnosed at an advanced stage2. Early diagnosis is critical for improving survival outcomes, as studies have shown that the 5-year survival rate | for early-stage lung cancer patients can reach 70%–90%, compared to less than 20% for those diagnosed at advanced stages3. However, pulmonary nodules are frequently detected in the general population, with approximately 20%–50% of adults showing nodules on computed tomography (CT) scans, the majority of which are benign4. Therefore, the development of effective biomarkers for early lung | | A full list of afﬁliations appears at the end of the paper. [e-mail:](e-mail: xjan2018@163.com)[ xjan2018@163.com](e-mail: xjan2018@163.com); [huangzhijian@sysush.com](huang

FAQs

What clinical problem does the study address?

It addresses the difficulty of distinguishing malignant from benign pulmonary nodules and guiding whether surgery is needed.

How were the DNA methylation signals identified and validated?

Genome-wide DNA methylation profiling (Methylome-seq) identified candidate differential methylation sites, and validated methylation features used targeted bisulﬁte sequencing in separate cohorts.

What models were developed to guide decisions?

SurgMalig differentiates benign from malignant nodules requiring resection, while ResectGuide further subdivides malignant nodules into MIA and early-stage invasive adenocarcinoma to inform surgical planning.

Sep 22, 2026

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