Evidence map›Paper›PMID 42429351›Full record

ReviewMolecular oncology2026

Single-cell DNA methylation profiling: Technologies, computation, and applications in precision oncology.

Ik Soo Kim

Abstract readReview
In one paragraph

Review in Molecular oncology, 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

1 author.

Ik Soo KimDepartment of Microbiology, Gachon University College of Medicine, Incheon, South Korea.ORCID https://orcid.org/0000-0002-0767-0766

Funding

National Research Foundation of Korea 2021R1A5A2030333Samsung Science and Technology Foundation SSTF-BA2102-08
6 · The paper itself

Abstract

Cancer is an intrinsically heterogeneous disease characterized by distinct malignant subclones defined by specific genetic and epigenetic alterations, such as aberrant DNA methylation. Traditional bulk sequencing methods analyze large populations of cells in aggregate, yielding an averaged methylome signal that masks the rare but clinically significant epigenetic patterns driving tumor initiation, metastasis, and therapeutic resistance. The emergence of single-cell DNA methylation (scDNAme) sequencing has driven a paradigm shift in oncology by providing the resolution required to dissect this intratumoral heterogeneity. By profiling the epigenome of individual cells, scDNAme analysis enables the discovery of novel aberrant patterns, the precise reconstruction of cellular lineages, and the characterization of specific populations-such as cancer stem cells (CSCs) or drug-resistant clones-that possess distinct methylome signatures. This technological advance is not merely an incremental improvement; it is a prerequisite for understanding core cancer hallmarks, such as the evasion of growth control and resistance to apoptosis, which are frequently governed by these specific subclones. This review specifically provides a comprehensive comparative overview evaluating the technical and chemical capabilities of emerging single-cell modalities to distinguish DNA methylation profiles. By highlighting the strategic workflows of these emergent technologies alongside advanced computational tools, we emphasize how resolving these discrete cytosine variants empowers precise cell lineage tracing, the identification of refractory clones, and the implementation of locus-specific epigenetic editing therapies against causal tumor subclones.

Indexed as

clinical epigenomicsepigenetic therapyintratumoral heterogeneitymulti‐omicssingle‐cell methylomics

Identifiers

PMID42429351
PMCPMC13398810

What OpenQuestion holds

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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.