Evidence map›Paper›PMID 41630950›Full record

ReviewGenes & diseases2026

DNA methylation heterogeneity in complex tumor microenvironment: Quantitative methods, influencing factors, and clinical implications.

Yongle Xu, Shuangyue Ma, Manyi Xu, Hongbo Zhu, Yuncong Wang, Wenbo Dong, Jing Gan, Yusen Zhao, Xinrong Li, Shuangshuang Wang and 4 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Genes & diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Yongle XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Shuangyue MaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Manyi XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Hongbo ZhuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Yuncong WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Wenbo DongCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Jing GanCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Yusen ZhaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Xinrong LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Shuangshuang WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Haoyu HuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Jiaheng HeCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Shangwei NingCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Hui ZhiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

5-Methylcytosine (5-mC) is the most prevalent DNA methylation modification in the human genome, and its abnormal patterns are strongly associated with tumor progression. Intratumoral and intertumoral DNA methylation heterogeneity (DNAmeH) primarily arises from cancer epigenome heterogeneity and the diverse cell compositions within the tumor microenvironment (TME). Furthermore, recent advancements in high-throughput sequencing and microarray technologies have facilitated the development of quantitative methods for measuring DNAmeH, enabling a more thorough exploration of the factors influencing it. Moreover, investigating various DNA methylation patterns at the single-cell level within the intricate TME sheds light on DNAmeH being driven by cellular heterogeneity. In addition, accumulating studies on the selection of methylation biomarkers in tissue or circulating DNA elucidate the cell specificity of DNA methylation, which is valuable for early cancer detection and personalized therapy. In this review, we elucidate the characteristics of intratumoral and intertumoral DNAmeH, considering DNAmeH differences across cancer types, among individual cells, and at allele-specific hemimethylation sites. Several metrics are summarized to quantitatively assess DNAmeH. We evaluate the factors that influence DNAmeH via these metrics, including the cell cycle phase, tumor mutational burden (TMB), cellular stemness, copy number variation (CNV), tumor subtype, tumor characteristics, tumor stage, state of tumor cells, hypoxia, and tumor purity. Finally, we highlight the deconvolution of TME cellular components and the application of predictive methylation biomarkers in cancer clinical research.

Indexed as

Circulating DNADNAmeHHemimethylationMethylation biomarkersTumor microenvironment

Identifiers

PMID41630950
PMCPMC12861004

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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.