Evidence map›Paper›PMID 41686777›Full record

ArticlePloS one2026

Computational assessment of the relationship between metabolism and histone methylation in cancer cells.

Mohammad Rasouli Koohi, Mahya Mehrmohamadi

Abstract read
In one paragraph

Article in PloS one, 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. Review
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

2 authors.

Mohammad Rasouli KoohiDepartment of Molecular Biotechnology, School of Biotechnology, College of Science, University of Tehran, Tehran, Iran.ORCID https://orcid.org/0009-0007-0083-5517
Mahya MehrmohamadiDepartment of Molecular Biotechnology, School of Biotechnology, College of Science, University of Tehran, Tehran, Iran.ORCID https://orcid.org/0000-0002-6681-0810

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aberrant histone methylation and metabolic alterations are key hallmarks of cancer. Metabolic reprogramming during tumorigenesis could impact the histone methylation pattern by altering the availability of substrates and cofactors required for histone methyltransferases (HMTs) and demethylases (HDMs) activities. Despite advances in understanding this complex interplay, quantitative information about the contributions of specific metabolic shifts and histone methylation dynamics remains poorly understood. Here, we used multi-omics data integrated with machine learning models to discover key metabolites, genes, and pathways predictive of histone methylation levels in cancer cell lines. Our cell line models highlighted the significant role of metabolites associated with one-carbon, nucleotide, redox and lipid metabolism on histone marks. Validation in primary tumors confirmed the cell line models' findings. Overall, this study quantifies the contributions of the metabolic network to histone methylation variation in cancer cells.

Indexed as

HistonesNeoplasmsCell Line, TumorHumansMachine LearningMetabolic Networks and PathwaysMetabolic ReprogrammingMethylationHistones

Identifiers

PMID41686777
PMCPMC12904441

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.