Evidence map›Paper›PMID 41184358›Full record

ArticleScientific reports2025

Identification of a m6A-immune-related risk model for predicting prognosis, immune microenvironment, and drug responses in acute myeloid leukemia.

Yanliang Bai, Huijie Nan, Lijie Wang, Peiyao Yang, Yabin Cui, Jinhui Xu, Mingyue Shi, Yuqing Chen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. 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

8 authors.

Yanliang Bai *Department of Hematology, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, Henan, China.
Huijie Nan *Department of Hematology, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, Henan, China.
Lijie WangDepartment of Hematology, Henan University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, Henan, China.
Peiyao YangDepartment of Hematology, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, Henan, China.
Yabin CuiDepartment of Hematology, Henan University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, Henan, China.
Jinhui XuDepartment of Hematology, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, Henan, China.
Mingyue Shi *Department of Hematology, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, Henan, China. shimingyue16@gmail.com.
Yuqing Chen *Department of Hematology, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, Henan, China. henanblood@sina.com.

Funding

Health Commission Project of Henan Province, P.R. China No. 2221023101010; No.242102311116; No.212102310205Health Technology Innovation Out-standing Youth Talent Training Project of Henan Province No. JQRC2023014Medical Science and Technology Research Program Jointly Established Project of Henan Province, P.R. China No. LHGJ20230016; No. LHGJ20230023Overseas Training Program for Medical and Health Science and Technology Talents, which was jointly launched by Henan Provincial Health Commission and Henan Academy of Medical Sciences HNMOT2024038The 'Three 100s' Project of Henan Provincial Department of Science and Technology and Henan Provincial Medical College, P.R. China No. H20240205
6 · The paper itself

Abstract

This study utilized TCGA database to explore the role of m6A modification and immune infiltration in AML. Through unsupervised clustering and WGCNA analysis, 8 hub genes were identified, and a risk model with EHBP1L1 and ZNF385A was established using LASSO regression. A nomogram incorporating hub gene risk score and age showed satisfactory prognostic prediction. External validation of GEO confirmed the model's effectiveness. TME analysis revealed correlations with monocytes and Treg cells, while immune checkpoints and HLA genes were associated with risk scores. Drug sensitivity analysis suggested potential responses to specific chemotherapy drugs. TIDE analysis indicated reduced ICI treatment benefit in high-risk patients. RT-qPCR validations revealed the significance of prognosis and risk stratification of ZNF385A. The noticeable trend of EHBP1L1 was observed. In addition, the accurate predictive capability of the risk model has been validated by clinical samples. Therefore, the risk model enables a quantitative evaluation of disease severity and progression risk in AML patients, based on their clinical and biological characteristics. This precise prediction not only informs treatment decisions but also guides the selection of chemotherapy regimens, overall improving patient outcomes.

Indexed as

AdenosineDNA-Binding ProteinsGene Expression Regulation, LeukemicLeukemia, Myeloid, AcuteRNA Processing, Post-TranscriptionalAntineoplastic AgentsBiomarkers, TumorCohort StudiesDrug Resistance, NeoplasmHumansImmune Checkpoint ProteinsKaplan-Meier EstimateNomogramsRisk AssessmentRNA-SeqTreatment OutcomeAdenosineAntineoplastic AgentsBiomarkers, TumorDNA-Binding ProteinsImmune Checkpoint ProteinsN-methyladenosineZNF385A protein, humanAcute myeloid leukemiaImmunem6A (N6-methyladenosine)PrognosisTMETreatment

Identifiers

PMID41184358
PMCPMC12583824

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