Evidence map›Paper›PMID 40474333›Full record

ArticleChinese medical journal2026

Identification and validation of blood leukocyte DNA methylation biomarkers for early detection of colorectal neoplasm.

Na Li, Chenyu Luo, Yuqing Chen, Xinran Cheng, Jiahui Luo, Yike Yan, Yuelun Zhang, Bin Lu, Zhiliang He, Kai Song and 6 more

Abstract read
In one paragraph

Article in Chinese medical journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

16 authors.

Na LiDepartment of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Chenyu LuoDepartment of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Yuqing ChenCenter for Prevention and Early Intervention, National Infrastructures for Translational Medicine, Institute of Clinical Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
Xinran ChengCenter for Prevention and Early Intervention, National Infrastructures for Translational Medicine, Institute of Clinical Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
Jiahui LuoDepartment of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Yike YanDepartment of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Yuelun ZhangCenter for Prevention and Early Intervention, National Infrastructures for Translational Medicine, Institute of Clinical Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
Bin LuDepartment of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Zhiliang HeDepartment of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Kai SongDepartment of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
Dong WuDepartment of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
Jianbo TianDepartment of Epidemiology and Biostatistics, School of Public Health, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei 430071, China.
Xiaoping MiaoDepartment of Epidemiology and Biostatistics, School of Public Health, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei 430071, China.
Hongda ChenCenter for Prevention and Early Intervention, National Infrastructures for Translational Medicine, Institute of Clinical Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
Fulan HuDepartment of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, Guangdong 518060, China.
Min DaiDepartment of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentifying high-risk populations for colorectal cancer (CRC) is critical for precise screening. This study aimed to develop a novel risk prediction model using blood DNA methylation biomarkers to identify individuals at high risk for colorectal neoplasms.

methodsThe biomarker discovery phase involved 106 samples (56 advanced adenomas and 50 healthy controls) collected from the TARGET-C screening cohort between May 2018 and May 2021, which were analyzed using the Illumina Infinium MethylationEPIC v2.0 BeadChip, and 72 samples (22 CRC, 20 advanced adenomas, and 30 healthy controls) collected from clinical cohorts between July 2023 and July 2024, which were analyzed using reduced representation bisulfite sequencing (RRBS). Differentially methylated positions (DMPs) and regions (DMRs) were identified and independently validated in 147 samples (48 CRC, 50 advanced adenomas, and 49 healthy controls) collected from an independent clinical cohort between June 2022 and May 2024 using targeted bisulfite sequencing (TBS). A multi-marker prediction model was constructed using logistic regression, and its diagnostic performance was evaluated through receiver operating characteristic (ROC) curve analysis.

resultsIn the discovery set, 48 DMPs and 74 DMRs were identified, exhibiting significant differences between CRC/advanced adenomas and healthy controls. Of these, three DMPs and 11 DMRs were successfully validated in the independent set using TBS. Through machine learning approaches, five stable methylation markers were identified and incorporated into a multi-marker prediction model. This model demonstrated excellent diagnostic performance for detecting colorectal neoplasms, with an area under the curve (AUC) of 0.85 (95% confidence interval [CI]: 0.74-0.94), outperforming the traditional lifestyle score (AUC = 0.55, 95% CI: 0.46-0.68). Combining methylation markers with lifestyle scores further improved diagnostic accuracy, achieving an AUC of 0.89. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the significant markers indicated their involvement in tumorigenesis through pathways regulating developmental processes, transcriptional activation, and cancer-related signaling.

conclusionsBlood leukocyte DNA methylation markers show significant potential for identifying high-risk populations for CRC. The identified markers could contribute to the development of novel, effective tools for CRC screening, facilitating precision screening strategies.

Indexed as

Biomarkers, TumorColorectal NeoplasmsDNA MethylationLeukocytesAdenomaAgedEarly Detection of CancerFemaleHumansMaleMiddle AgedROC CurveBiomarkers, TumorAdvanced adenomasColorectal cancerDNA methylation biomarkersRisk prediction model

Identifiers

PMID40474333
PMCPMC13441104

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Registered trials

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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.