A novel prognostic classification integrating lipid metabolism and immune co-related genes in acute myeloid leukemia

Front Immunol. 2023 Nov 10:14:1290968. doi: 10.3389/fimmu.2023.1290968. eCollection 2023.

Abstract

Background: As a severe hematological malignancy in adults, acute myeloid leukemia (AML) is characterized by high heterogeneity and complexity. Emerging evidence highlights the importance of the tumor immune microenvironment and lipid metabolism in cancer progression. In this study, we comprehensively evaluated the expression profiles of genes related to lipid metabolism and immune modifications to develop a prognostic risk signature for AML.

Methods: First, we extracted the mRNA expression profiles of bone marrow samples from an AML cohort from The Cancer Genome Atlas database and employed Cox regression analysis to select prognostic hub genes associated with lipid metabolism and immunity. We then constructed a prognostic signature with hub genes significantly related to survival and validated the stability and robustness of the prognostic signature using three external datasets. Gene Set Enrichment Analysis was implemented to explore the underlying biological pathways related to the risk signature. Finally, the correlation between signature, immunity, and drug sensitivity was explored.

Results: Eight genes were identified from the analysis and verified in the clinical samples, including APOBEC3C, MSMO1, ATP13A2, SMPDL3B, PLA2G4A, TNFSF15, IL2RA, and HGF, to develop a risk-scoring model that effectively stratified patients with AML into low- and high-risk groups, demonstrating significant differences in survival time. The risk signature was negatively related to immune cell infiltration. Samples with AML in the low-risk group, as defined by the risk signature, were more likely to be responsive to immunotherapy, whereas those at high risk responded better to specific targeted drugs.

Conclusions: This study reveals the significant role of lipid metabolism- and immune-related genes in prognosis and demonstrated the utility of these signature genes as reliable bioinformatic indicators for predicting survival in patients with AML. The risk-scoring model based on these prognostic signature genes holds promise as a valuable tool for individualized treatment decision-making, providing valuable insights for improving patient prognosis and treatment outcomes in AML.

Keywords: acute myeloid leukemia; drug sensitivity; immunotherapy; lipid metabolism; prognostic signature.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Computational Biology
  • Drug Delivery Systems
  • Humans
  • Leukemia, Myeloid, Acute* / genetics
  • Lipid Metabolism* / genetics
  • Prognosis
  • Sphingomyelin Phosphodiesterase
  • Tumor Microenvironment / genetics
  • Tumor Necrosis Factor Ligand Superfamily Member 15

Substances

  • TNFSF15 protein, human
  • Tumor Necrosis Factor Ligand Superfamily Member 15
  • SMPDL3B protein, human
  • Sphingomyelin Phosphodiesterase

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by Henan Provincial Science and Technology Research Project (232102311016) and Henan Provincial Young and Middle-aged Health Science and Technology Innovation Excellent Youth Talent Project (YQRC2023003).