Evidence map›Paper›PMID 42251373›Full record

ArticleBMC medicine2026

Blood-based DNA methylation marker model for short-term and long-term lung cancer risk prediction.

Megha Bhardwaj, Yi-Qian Sun, Clara Frick, Ben Schöttker, Oluf Dimitri Røe, Bernd Holleczek, Xiao-Mei Mai, Hermann Brenner

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Article in BMC medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Megha BhardwajClinical Epidemiology of Early Cancer Detection, German Cancer Research Center (DKFZ), Heidelberg, Germany. megha.bhardwaj@dkfz-heidelberg.de.
Yi-Qian SunDepartment of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.
Clara FrickClinical Epidemiology of Early Cancer Detection, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Ben SchöttkerClinical Epidemiology of Early Cancer Detection, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Oluf Dimitri RøeDepartment of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.
Bernd HolleczekSaarland Cancer Registry, Saarbrücken, Germany.
Xiao-Mei MaiDepartment of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway.
Hermann BrennerCancer Prevention Graduate School, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundScreening heavy smokers with low-dose computed tomography (LDCT) has been shown to reduce lung cancer (LC) mortality, however, identifying the specific high-risk population that benefits most, a critical requirement for implementing effective and cost-efficient screening, remains challenging.

methodsWe developed and validated a blood-based DNA methylation marker model (BBDMM) for all participants, including both ever and never smokers, using the LC risk-informative CpG sites from epigenome-wide association studies (EWAS). The model was developed and internally validated in 2,459 participants from ESTHER, a population-based cohort from Germany. Subsequently, BBDMM was externally validated in exactly same 233 participants drawn from the Norwegian HUNT2 and HUNT3 cohorts with long- and short-term follow-ups and cases identified up to 18 and 6.7 years before LC diagnosis, respectively.

resultsThe BBDMM predicted LC incidence with an area under the curve (AUC) of 0.84 [95% confidence interval (95% CI), 0.80-0.87] in the derivation set. In the independent external validation sets, AUCs of 0.85 (95% CI, 0.80-0.90) and 0.85 (95% CI, 0.80-0.90) were observed in HUNT2 and HUNT3, respectively.

conclusionsThe BBDMM identified future lung cancer cases with promising potential and the model discrimination was highly stable at different time points. These markers may contribute to the evolution of a blood-based test for predicting LC risk.

Indexed as

Biomarkers, TumorDNA MethylationLung NeoplasmsAgedCpG IslandsFemaleGermanyHumansMaleMiddle AgedNorwayRisk FactorsBiomarkers, TumorDNA methylationepidemiologylung neoplasmsscreening

Identifiers

PMID42251373
PMCPMC13242670

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