Construction of an acute myeloid leukemia prognostic model based on m6A-related efferocytosis-related genes

Ying Wang; Ting Bin; Jing Tang; Xiao-Jun Xu; Chao Lin; Bo Lu; Tian-Tian Sun

doi:10.3389/fimmu.2023.1268090

Construction of an acute myeloid leukemia prognostic model based on m6A-related efferocytosis-related genes

Front Immunol. 2023 Nov 22:14:1268090. doi: 10.3389/fimmu.2023.1268090. eCollection 2023.

Authors

Ying Wang^#¹, Ting Bin^#¹, Jing Tang^#¹, Xiao-Jun Xu¹, Chao Lin², Bo Lu¹, Tian-Tian Sun¹

Affiliations

¹ Department of Haematology. The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.
² Pediatric Hematology Laboratory, Division of Hematology/Oncology, Department of Pediatrics. The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.

^# Contributed equally.

Abstract

Background: One of the most prevalent hematological system cancers is acute myeloid leukemia (AML). Efferocytosis-related genes (ERGs) and N6-methyladenosine (m6A) have an important significance in the progression of cancer, and the metastasis of tumors.

Methods: The AML-related data were collected from The Cancer Genome Atlas (TCGA; TCGA-AML) database and Gene Expression Omnibus (GEO; GSE9476, GSE71014, and GSE13159) database. The "limma" R package and Venn diagram were adopted to identify differentially expressed ERGs (DE-ERGs). The m6A related-DE-ERGs were obtained by Spearman analysis. Subsequently, univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) were used to construct an m6A related-ERGs risk signature for AML patients. The possibility of immunotherapy for AML was explored. The pRRophetic package was adopted to calculate the IC50 of drugs for the treatment of AML. Finally, the expression of characterized genes was validated by quantitative reverse transcription-PCR (qRT-PCR).

Results: Based on m6A related-DE-ERGs, a prognostic model with four characteristic genes (UCP2, DOCK1, SLC14A1, and SLC25A1) was constructed. The risk score of model was significantly associated with the immune microenvironment of AML, with four immune cell types, 14 immune checkpoints, 20 HLA family genes and, immunophenoscore (IPS) all showing differences between the high- and low-risk groups. A total of 56 drugs were predicted to differ between the two groups, of which Erlotinib, Dasatinib, BI.2536, and bortezomib have been reported to be associated with AML treatment. The qRT-PCR results showed that the expression trends of DOCK1, SLC14A1 and SLC25A1 were consistent with the bioinformatics analysis.

Conclusion: In summary, 4 m6A related- ERGs were identified and the corresponding prognostic model was constructed for AML patients. This prognostic model effectively stratified the risk of AML patients.

Keywords: N6-methyladenosine; acute myeloid leukemia; bioinformatics; drug prediction; efferocytosis; prognostic risk model.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Genes, Regulator
Hematologic Neoplasms*
Humans
Leukemia, Myeloid, Acute* / genetics
Prognosis
Transcription Factors
Tumor Microenvironment

Substances

6-methyladenine
Transcription Factors

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. We thank Sanming Project of Medicine in Shenzhen (No.SZSM201911004) for supporting the manuscript preparation and publication.