Deciphering lactate/lactylation networks in AML: integrated scRNA-seq and transcriptomics reveal functions and...

Area of Science:

• Oncology
• Epigenetics
• Metabolomics

Background:

• Acute myeloid leukemia (AML) is a heterogeneous cancer requiring precise therapeutic strategies.
• Lactate metabolism and histone lactylation are emerging epigenetic regulators impacting tumor biology and the immune microenvironment.

Purpose of the Study:

• To investigate the prognostic value of lactate/lactylation-associated genes (LL-genes) in AML.
• To develop a predictive model for survival and therapeutic response in AML based on LL-genes.

Main Methods:

• Integrated analysis of single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data.
• Utilized Seurat for scRNA-seq, GSVA for pathway activity, ConsensusClusterPlus for subtyping, and machine learning for prognostic model construction.
• Validated key gene dysregulation using qRT-PCR and Western blot.

Main Results:

• scRNA-seq identified LL-gene overexpression in malignant progenitors, linked to increased lactate metabolism-lactylation activity, metabolic-inflammatory synergy, and immunosuppression.
• Molecular subtyping revealed distinct prognostic clusters, with one cluster showing poorer survival.
• A 7-gene prognostic model accurately predicted survival, chemotherapy response, and sensitivity to targeted inhibitors (BCL-2/FGFR).

Conclusions:

• Lactate/lactylation-associated genes contribute to AML heterogeneity and immunosuppression.
• The developed machine learning model offers a promising tool for predicting AML patient outcomes and guiding precision therapeutics.
• Targeting lactate metabolism and lactylation pathways may represent a novel therapeutic strategy for AML.