ArticleFrontiers in immunology2021
Development and Validation of a Novel Prognostic Model for Acute Myeloid Leukemia Based on Immune-Related Genes.
Article in Frontiers in immunology, 2021. 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, 14 citations in OpenAlex.
- Potential value of immunogenic cell death related-genes in refining European leukemiaNet guidelines classification and predicting the immune infiltration landscape in acute myeloid leukemia.Cancer cell international · 2025Article
- Proteomic Profiling of Extracellular Vesicles Isolated from Plasma and Peritoneal Exudate in Mice Induced byToxins · 2023Article
- Potential clinical use of azacitidine and MEK inhibitor combination therapy in PTPN11-mutated juvenile myelomonocytic leukemia.Molecular therapy : the journal of the American Society of Gene Therapy · 2023Article
- Immune-relatedlncRNAs can predict the prognosis of acute myeloid leukemia.Cancer medicine · 2022Article
- A novel immune prognostic model of non-M3 acute myeloid leukemia.American journal of translational research · 2022Article
- Development and Validation of a Novel Diagnostic Model for Childhood Autism Spectrum Disorder Based on Ferroptosis-Related Genes.Frontiers in psychiatry · 2022Article
- A Prognostic Model for Acute Myeloid Leukemia Based on IL-2/STAT5 Pathway-Related Genes.Frontiers in oncology · 2022Article
- Identification and Validation of a Prognostic Risk-Scoring Model Based on Ferroptosis-Associated Cluster in Acute Myeloid Leukemia.Frontiers in cell and developmental biology · 2021Article
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prognosis of acute myeloid leukemia (AML) is closely related to immune response changes. Further exploration of the pathobiology of AML focusing on immune-related genes would contribute to the development of more advanced evaluation and treatment strategies. In this study, we established a novel immune-17 signature based on transcriptome data from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) databases. We found that immune biology processes and transcriptional dysregulations are critical factors in the development of AML through enrichment analyses. We also formulated a prognostic model to predict the overall survival of AML patients by using LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis. Furthermore, we incorporated the immune-17 signature to improve the prognostic accuracy of the ELN2017 risk stratification system. We concluded that the immune-17 signature represents a novel useful model for evaluating AML survival outcomes and may be implemented to optimize treatment selection in the next future.
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Registered trials
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