Evidence map›Paper›PMID 40837413›Full record

ArticleGenes & diseases2025

Diagnosis of early-stage non-small cell lung cancer using DNA methylation in tissue and plasma.

Lei Li, Kai Fu, Xuyu Cai, Dan Liu, Yingying Zhu, Weiwen Wang, Panwen Tian, Ye Wang, Hui Xue, Michael P Snyder and 1 more

Abstract read
In one paragraph

Article in Genes & diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. A Noninvasive Circulating Tumor DNA Methylation Classifier to Identify Benign Pulmonary Nodules.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026
    Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Multimodal AI in precision medicine: linking omics, imaging and clinical decisions.American journal of clinical and experimental immunology · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Lei LiDepartment of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610000, China.
Kai FuGenetics Department, Center for Personalized Medicine, Stanford University, CA 94303, USA.
Xuyu CaiPrecision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610000, China.
Dan LiuDepartment of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610000, China.
Yingying ZhuDepartment of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610000, China.
Weiwen WangFrontiers Science Center for Disease-related Molecular Network, Sichuan University, Chengdu, Sichuan 610000, China.
Panwen TianDepartment of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610000, China.
Ye WangDepartment of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610000, China.
Hui XuePrecision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610000, China.
Michael P SnyderGenetics Department, Center for Personalized Medicine, Stanford University, CA 94303, USA.
Weimin LiDepartment of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DNA methylation is a key epigenetic alteration in tumorigenesis, but its diagnostic value in early-stage lung cancer remains unclear. In this study, tissue and plasma samples from patients with lung cancer or benignity were analyzed. Methylation profiles were obtained using bisulfite sequencing and compared with selected lung cancer-specific markers. Diagnostic prediction models were constructed using these markers, with their efficacy assessed by sensitivity, specificity and area under the curve (AUC). In the tissue cohort, 276 markers were found to be significantly differentially methylated in lung cancer (FDR < 0.05). A diagnostic prediction model using six markers showed promising performance in both the training cohort (sensitivity = 90%; specificity = 97%; AUC = 0.988) and the validation cohort (sensitivity = 92%; specificity = 94%; AUC = 0.977). In the plasma cohort, a diagnostic prediction model using nine markers achieved a sensitivity of 98% and specificity of 100% (AUC = 0.998) in the training cohort, a sensitivity of 81% and specificity of 59% (AUC = 0.791) in the validation cohort. Furthermore, we observed a significant correlation between delta methylation changes in tissue and plasma in the paired patient cohort. Additional analysis based on methylation haplotypes identified 1222 differentially methylated regions in tissue samples, mainly enriched in DNA replication-related pathways. Additionally, correlations between DNA methylation and clinical characteristics revealed significant differential methylation patterns between smokers and non-smokers . Thus, DNA methylation in both tissue and plasma holds potential as a biomarker for the early diagnosis of lung cancer.

Indexed as

Circulating tumor DNADiagnosisLiquid biopsyMethylationNon-small cell lung cancer

Identifiers

PMID40837413
PMCPMC12361987

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.