ArticleAnnals of translational medicine2022
Analysis of m6A-related signatures associated with the tumor immune microenvironment and predict survival in acute myeloid leukemia.
Article in Annals of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- A m6A/m1A/m5C-related eight-gene signature predicts prognosis and correlates with immune microenvironment in pediatric acute myeloid leukemia.Translational pediatrics · 2026Article
- LncRNA SNHG11 expression in acute myeloid leukemia patients and its relationship with the biology of acute myeloid leukemia cells.Clinical and experimental medicine · 2025Article
- Identification of a m6A-immune-related risk model for predicting prognosis, immune microenvironment, and drug responses in acute myeloid leukemia.Scientific reports · 2025Article
- RNA m6A modification in ferroptosis: implications for advancing tumor immunotherapy.Molecular cancer · 2024Review
- TARGET based mBMC pediatrics · 2024Article
- Small molecule inhibitors targeting mJournal of hematology & oncology · 2024Review
- PIWI-interacting RNA-17458 is oncogenic and a potential therapeutic target in cervical cancer.Journal of Cancer · 2023Article
- Integrated analysis of single-cell RNA-seq and bulk RNA-seq reveals RNA N6-methyladenosine modification associated with prognosis and drug resistance in acute myeloid leukemia.Frontiers in immunology · 2023Article
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Most previous studies have focused on the intrinsic carcinogenic pathways of tumors; however, little is known about the potential role of N6-methyladenosine (m6A) methylation in the tumor immune microenvironment (TIME). To better diagnose and treat acute myeloid leukemia (AML), we sought to examine the correlation between m6A regulatory factors and immune infiltration in cases of AML. At the same time, a prognostic model was constructed to predict the survival of AML. Methods: We extracted data from The Cancer Genome Atlas (TCGA) database, including ribonucleic acid sequencing (RNA-seq) transcriptome data and data on the corresponding clinical characteristics of AML patients. We identified two m6A modification patterns with distinct clinical outcomes and found a significant relationship between them. Simultaneous discovery of distinct m6A clusters associated with the tumor immune microenvironment [immune cell types and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm] are closely related. Next, we implemented Lasso (Least Absolute Shrinkage and Selection Operator) Cox regression to build a predictive model in the 2-m6A regulator TCGA dataset to further explore m6A prognostic features in AML, and perform correlation validation. Results: We identified 2 molecular subtypes (Clusters 1 and 2) by the consistent clustering of significant m6A regulators in AML. Cluster 2 was associated with a higher immune score and obvious immune cell infiltration, and thus patients in Cluster 2 had a poorer prognosis than those in Cluster 1 (P<0.05). Additionally, the 2 m6A-related signatures representing the independent prognostic factors in AML were screened to construct a prognostic risk-score model. We found that patients with low-risk scores had higher immune scores than those with high-risk scores (P<0.05). Conclusions: Our research confirmed that m6A methylation plays an important role in AML. Further provide new directions for the prognosis and treatment of AML.
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