Evidence map›Paper›PMID 35008106›Full record

ArticleBlood advances2022

Genome-wide DNA methylation analysis in pediatric acute myeloid leukemia.

Genki Yamato, Tomoko Kawai, Norio Shiba, Junji Ikeda, Yusuke Hara, Kentaro Ohki, Shin-Ichi Tsujimoto, Taeko Kaburagi, Kenichi Yoshida, Yuichi Shiraishi and 12 more

Open access · goldAbstract read
In one paragraph

Article in Blood advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
4.2field-weighted citation impact, top 5% of its field
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

16 citing papers in PubMed, 29 citations in OpenAlex.

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  13. Methods in DNA methylation array dataset analysis: A review.Computational and structural biotechnology journal · 2024
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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

22 authors at 10 institutions in 2 countries.

Genki YamatoDepartment of Hematology/Oncology, Gunma Children's Medical Center, Shibukawa, Japan.ORCID 0000-0003-4000-0753
Tomoko KawaiDepartment of Maternal-Fetal Biology, National Research Institute for Child Health and Development, Tokyo, Japan.ORCID 0000-0001-8137-0334
Norio ShibaDepartment of Pediatrics, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Junji IkedaDepartment of Pediatrics, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Yusuke HaraDepartment of Pediatrics, Gunma University Graduate School of Medicine, Maebashi, Japan.
Kentaro OhkiDepartment of Pediatric Hematology and Oncology Research, National Research Institute for Child Health and Development, Tokyo, Japan.
Shin-Ichi TsujimotoDepartment of Pediatrics, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Taeko KaburagiDepartment of Hematology/Oncology, Gunma Children's Medical Center, Shibukawa, Japan.
Kenichi YoshidaDepartment of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Yuichi ShiraishiDivision of Cellular Signaling, National Cancer Center Research Institute, Tokyo, Japan.
Satoru MiyanoLaboratory of DNA Information Analysis, and.
Nobutaka KiyokawaDepartment of Pediatric Hematology and Oncology Research, National Research Institute for Child Health and Development, Tokyo, Japan.
Daisuke TomizawaChildren's Cancer Center, National Center for Child Health and Development, Tokyo, Japan.ORCID 0000-0003-1520-7007
Akira ShimadaDepartment of Pediatrics, Jichi Medical University, Shimotsuke, Japan.
Manabu SotomatsuDepartment of Hematology/Oncology, Gunma Children's Medical Center, Shibukawa, Japan.
Hirokazu ArakawaDepartment of Pediatrics, Gunma University Graduate School of Medicine, Maebashi, Japan.
Souichi AdachiDepartment of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Takashi TagaDepartment of Pediatrics, Shiga University of Medical Science, Ōtsu, Japan.
Keizo HoribeClinical Research Center, National Hospital Organization Nagoya Medical Center, Nagoya, Japan.ORCID 0000-0002-6251-6059
Seishi OgawaDepartment of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.ORCID 0000-0002-7778-5374
Kenichiro HataDepartment of Maternal-Fetal Biology, National Research Institute for Child Health and Development, Tokyo, Japan.ORCID 0000-0002-7809-7637
Yasuhide HayashiDepartment of Hematology/Oncology, Gunma Children's Medical Center, Shibukawa, Japan.
National Center For Child Health and Development · JPGunma University · JPKyoto University · JPYokohama City University · JPGunma Children's Medical Center · JPJichi Medical University · JPJobu University · JPNational Hospital Organization · JPShiga University of Medical Science · JPTokyo University of Science · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We investigated genome-wide DNA methylation patterns in 64 pediatric patients with acute myeloid leukemia (AML). Based on unsupervised clustering with the 567 most variably methylated cytosine guanine dinucleotide (CpG) sites, patients were categorized into 4 clusters associated with genetic alterations. Clusters 1 and 3 were characterized by the presence of known favorable prognostic factors, such as RUNX1-RUNX1T1 fusion and KMT2A rearrangement with low MECOM expression, and biallelic CEBPA mutations (all 8 patients), respectively. Clusters 2 and 4 comprised patients exhibiting molecular features associated with adverse outcomes, namely internal tandem duplication of FLT3 (FLT3-ITD), partial tandem duplication of KMT2A, and high PRDM16 expression. Depending on the methylation values of the 1243 CpG sites that were significantly different between FLT3-ITD+ and FLT3-ITD- AML, patients were categorized into 3 clusters: A, B, and C. The STAT5-binding motif was most frequently found close to the 1243 CpG sites. All 8 patients with FLT3-ITD in cluster A harbored high PRDM16 expression and experienced adverse events, whereas only 1 of 7 patients with FLT3-ITD in the other clusters experienced adverse events. PRDM16 expression levels were also related to DNA methylation patterns, which were drastically changed at the cutoff value of PRDM16/ABL1 = 0.10. The assay for transposase-accessible chromatin sequencing of AMLs supported enhanced chromatin accessibility around genomic regions, such as HOXB cluster genes, SCHIP1, and PRDM16, which were associated with DNA methylation changes in AMLs with FLT3-ITD and high PRDM16 expression. Our results suggest that DNA methylation levels at specific CpG sites are useful to support genetic alterations and gene expression patterns of patients with pediatric AML.

Indexed as

DNA MethylationLeukemia, Myeloid, AcuteChildChromatinHumansMutationChromatin

Identifiers

PMID35008106
PMCPMC9198913
OpenAlexW4206090394

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.