Evidence map›Paper›PMID 39688413›Full record

ArticleMolecular carcinogenesis2025

mRNA m5C Alteration in Azacitidine Demethylation Treatment of Acute Myeloid Leukemia.

Ziwei Chen, Yingyu Guo, Zaifeng Zhang, Chang Li, Lili Zhang, Ye Liu, Gaoyuan Sun, Fei Xiao, Ru Feng, Chunli Zhang

Abstract read
In one paragraph

Article in Molecular carcinogenesis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. 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

10 authors.

Ziwei ChenThe Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, China.ORCID 0000-0001-7618-342X
Yingyu GuoThe Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, China.
Zaifeng ZhangGraduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Chang LiThe Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, China.ORCID 0000-0001-9588-8118
Lili ZhangClinical Biobank, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Ye LiuThe Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, China.
Gaoyuan SunClinical Biobank, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Fei XiaoThe Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, China.
Ru FengDepartment of Hematology, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Chunli ZhangDepartment of Hematology, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.

Funding

This work was supported by National High Level Hospital Clinical Research Funding (BJ-2023-078), Beijing natural science foundation (No. 7222158), CAMS Innovation Fund for Medical Sciences (2021-I2M-1-050).
6 · The paper itself

Abstract

The DNA demethylating therapy with azacitidine (AZA) is a promising therapeutic strategy for elderly patients with acute myeloid leukemia (AML). AZA primarily inhibits DNA methylation, promotes cell differentiation and apoptosis in AML. However, as a cytosine nucleoside analog, AZA also has the potential to be incorporated into RNA molecules. To assess the impact of AZA on RNA m5C methylation during demethylating therapy, we conducted Nanopore direct-RNA sequencing on samples from three AML patients pre and after demethylating therapy, as well as on HL-60 cells pretreated with AZA. We performed an integrated analysis of the transcriptome and the m5C methylome, contrasting the states of complete remission with those of active disease (AML). Our results revealed an extensive demethylation effect at the RNA level attributable to AZA and found that mRNA m5C modification may play a pivotal role in the progression of AML. Additionally, S100P was identified as a biomarker with significant prognostic implications. We also conducted a conjoint analysis of the transcriptome and the m5C methylome of the full-length transcripts, uncovering several dysregulated mRNA isoforms. Collectively, our findings indicate that mRNA m5C methylation is implicated during AML progression, and AZA exhibits an overall suppressive effect on this process.

Indexed as

5-MethylcytosineAntimetabolites, AntineoplasticAzacitidineDNA MethylationLeukemia, Myeloid, AcuteRNA, MessengerAgedBiomarkers, TumorDemethylationFemaleHL-60 CellsHumansMalePrognosisTranscriptome5-MethylcytosineAntimetabolites, AntineoplasticAzacitidineBiomarkers, TumorRNA, Messengeracute myeloid leukemiananopore sequencingRNA 5‐methylcytidine methylation

Identifiers

PMID39688413
PMCPMC11814907

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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.