Objective:

To systematically analyze lipid metabolic characteristics in AML cells, identify molecular subtypes related to lipid metabolism, and construct a prognostic model based on lipid metabolism-related genes.

Approach:

• Data Integration: Multi-omics data were integrated, including single-cell RNA-seq and bulk transcriptomes from nine AML cohorts.
• Lipid Metabolism Assessment: Lipid metabolism activity was assessed using Gene Set Variation Analysis (GSVA) and consensus clustering identified molecular subtypes.
• Prognostic Signature Development: A lipid metabolism-related prognostic signature (LMRS) was constructed via machine learning algorithms and validated across nine independent cohorts.
• Functional Validation: Functional validation was performed in AML cell lines using GSTO1 inhibition.

Key Findings:

• Single-cell analysis revealed significant upregulation of lipid metabolism pathways in AML malignant cells, particularly in progenitor-like subpopulations.
• Three lipid metabolism-based subtypes (C1–C3) were identified, with the C3 subtype exhibiting the highest metabolic activity and the worst prognosis.
• A robust nine-gene LMRS model was developed, which effectively stratified patients into high- and low-risk groups with distinct survival outcomes.
• LMRS demonstrated superior predictive accuracy over existing models and correlated with chemotherapy and immunotherapy resistance.
• Inhibition of GSTO1 significantly induced apoptosis and ROS production in AML cells.

Interpretation:

The study defines lipid metabolic heterogeneity in AML, establishes a prognostic signature, and highlights lipid metabolism's role in AML progression and immunosuppression.

Limitations:

• The study primarily relies on data from public databases, which may have inherent biases.
• Functional validation was limited to AML cell lines and may not fully represent in vivo conditions.

Conclusion:

Targeting lipid metabolism, particularly through GSTO1 inhibition, represents a promising therapeutic strategy in AML.