Evidence map›Paper›PMID 37606761›Full record

ArticleJournal of cancer research and clinical oncology2023

Identification and analysis of methylation signature genes and association with immune infiltration in pediatric acute myeloid leukemia.

Huawei Zhu, Yanbo Xu, Jun Xia, Xu Guo, Yujie Fang, Jingzhi Fan, Fangjun Li, Jinhong Wu, Guoliang Zheng, Yubo Liu

Open access · greenAbstract read
In one paragraph

Article in Journal of cancer research and clinical oncology, 2023. 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
0.3field-weighted citation impact, top 36% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  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

10 authors at 3 institutions in 1 country.

Huawei Zhu *School of Life and Pharmaceutical Sciences, Dalian University of Technology, Panjin, 124221, China.
Yanbo Xu *School of Life and Pharmaceutical Sciences, Dalian University of Technology, Panjin, 124221, China.
Jun Xia *School of Life and Pharmaceutical Sciences, Dalian University of Technology, Panjin, 124221, China.
Xu Guo *School of Life and Pharmaceutical Sciences, Dalian University of Technology, Panjin, 124221, China.
Yujie FangSchool of Life and Pharmaceutical Sciences, Dalian University of Technology, Panjin, 124221, China.
Jingzhi FanSchool of Life and Pharmaceutical Sciences, Dalian University of Technology, Panjin, 124221, China.
Fangjun LiSchool of Life and Pharmaceutical Sciences, Dalian University of Technology, Panjin, 124221, China.
Jinhong WuSchool of Life and Pharmaceutical Sciences, Dalian University of Technology, Panjin, 124221, China.
Guoliang ZhengLiaoning Cancer Hospital, China Medical University, Shenyang, 110042, China. Zhengboren1@126.com.
Yubo LiuSchool of Life and Pharmaceutical Sciences, Dalian University of Technology, Panjin, 124221, China. liuyubo@dlut.edu.cn.
Dalian University · CNDalian University of Technology · CNLiaoning Cancer Hospital & Institute · CN

Funding

Fundamental Research Funds for the Central Universities DUT22YG131
6 · The paper itself

Abstract

backgroundAcute myeloid leukemia (AML) is a common leukemia with low cure rate and poor prognosis among pediatric patients. The regulation of AML immune microenvironment and methylation remains to be explored. Pediatric and adult AML patients differ significantly in epigenetic factors, and the efficiency of treatment modalities varies between the two groups of patients.

methodsWe collected mRNA, miRNA and DNA methylation data from pediatric AML patients across multiple databases. Differentially expression genes were identified, and a gene-miRNA regulatory network was constructed. Prognostic risk models were established by integrating LASSO and Cox regression, and a nomogram was generated. Based on this model, we investigated tumor-infiltrating immune cells and cell communication, analyzing the biological functions and pathways associated with prognostic factors. Furthermore, the relationships between all prognostic factors and gene modules were explored, and the impact of these factors on treatment modalities was determined.

resultsWe developed an efficient prognostic risk model and identified HOXA9, SORT1, SH3BP5, mir-224 and mir-335 as biomarkers. We validated these findings in an external dataset and observed a correlation between age and risk in pediatric patients. AML samples with lower risk scores have a better prognosis and higher expression of immune-upregulated biomarkers, and have lower immune scores. Furthermore, we detected discrepancies in immune cell infiltration and interactions between high- and low-risk group samples, which affected the efficacy of immunotherapy. We evaluated all prognostic factors and predicted the effect of immunotherapy and medicine.

conclusionThis study comprehensively investigated the role of methylation signature genes in pediatric AML at the level of genomes and transcriptomes. The research aims to enhance the risk stratification, prognosis evaluation and assessment of treatment effectiveness of AML patients. This study also highlight the uniqueness of pediatric AML and foster the development of new immunotherapy and targeted therapy strategies.

Indexed as

Leukemia, Myeloid, AcuteMicroRNAsAdultBiomarkersChildDNA MethylationHumansPrognosisProtein Processing, Post-TranslationalTumor MicroenvironmentBiomarkersMicroRNAsMIRN224 microRNA, humanAMLBiomarkerImmune infiltrationMethylationPediatric

Identifiers

PMID37606761
PMCPMC11797363
OpenAlexW4386046608

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.