Abstract
Background
Acute 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.
Methods
We 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.
Results
We 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.
Conclusion
This 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.
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Availability of data and materials
The datasets supporting the conclusions of this article are available in the TARGET (https://www.cancer.gov/ccg/access-data), Targetscan (https://www.targetscan.org/vert_80/), miRcode (http://mircode.org/), GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM5936941) and TCGA (https://portal.gdc.cancer.gov/).
Abbreviations
- AML:
-
Acute myeloid leukemia
- ssGSEA:
-
Single sample gene set enrichment analysis
- GSEA:
-
Gene set enrichment analysis
- WGCNA:
-
Weighted correlation network analysis
- TCGA:
-
The cancer genome atlas
- miRNAs:
-
MicroRNAs
- DEGs:
-
Discovering differentially expressed genes
- scRNA-seq:
-
Single-cell RNA-seq
- LASSO:
-
Least absolute shrinkage and selection operator
- ROC:
-
Receiver operating characteristic
- AUC:
-
Areas under the curve
- K–M:
-
Kaplan–Meier
- TME:
-
Tumor microenvironment
- KEGG:
-
Kyoto encyclopedia of genes and genomes
- GO:
-
Gene ontology
- OS:
-
Overall survival
- CR:
-
Complete response
- CAR-T:
-
Chimeric antigen receptor T-cells
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This study was supported by the Fundamental Research Funds for the Central Universities (DUT22YG131).
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HZ, YX, JX and XG have contributed equally to this work: they designed the study, collected research data, analyzed data and drafted the manuscript; YF, JF, FL and JW were responsible for the interpretation of the results; and GZ and YL provided the overall guidance. All authors read and approved the final manuscript.
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Zhu, H., Xu, Y., **a, J. et al. Identification and analysis of methylation signature genes and association with immune infiltration in pediatric acute myeloid leukemia. J Cancer Res Clin Oncol 149, 14965–14982 (2023). https://doi.org/10.1007/s00432-023-05284-y
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DOI: https://doi.org/10.1007/s00432-023-05284-y