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

Comprehensive Summary

This study investigates the regulatory networks of lactate metabolism and histone lactylation in acute myeloid leukemia (AML) to determine their impact on tumor heterogeneity, prognosis, and the immune microenvironment. To perform the research, the authors integrated single-cell and bulk RNA sequencing data with machine learning algorithms to develop a prognostic model based on lactate/lactylation-associated genes (LL-genes). These bioinformatic findings were further validated through experimental techniques, including qRT-PCR and Western blot analysis of patient bone marrow samples. The findings reveal that elevated lactylation activity in malignant cells correlates with an immunosuppressive environment enriched with regulatory T cells and M2 macrophages. The study identified an optimized 7-gene prognostic model that accurately predicts survival and chemotherapy response, identifying high-risk patients who show sensitivity to BCL-2 and FGFR inhibitors. Additionally, the researchers identified two molecular subtypes, where Cluster A represents a high-risk group with significantly poorer outcomes and enrichment in MYC target pathways. In the discussion, the authors argue that histone lactylation acts as a pivotal interface between metabolic reprogramming and epigenetic regulation in AML. They conclude that their prognostic framework provides a novel strategy for personalized medicine by identifying molecular vulnerabilities that can be targeted in high-risk patients.

Outcomes and Implications

This research is critical because existing prognostic models for acute myeloid leukemia (AML) frequently overlook the metabolic and epigenetic roles of histone lactylation, which contribute significantly to the disease's high rate of chemoresistance and relapse. By mapping these lactate-related networks, the study fills a major gap in understanding how metabolic signals are translated into epigenetic instructions that allow leukemic cells to evade the immune system. Clinically, the developed 7-gene prognostic model offers a direct application for precision medicine by identifying high-risk patients likely to respond to targeted therapies such as BCL-2 or FGFR inhibitors. This approach is highly relevant for improving the current sub-30% five-year survival rate through tailored therapeutic strategies based on a patient's specific molecular subtype. Although the authors do not provide a specific timeline for clinical adoption, the successful validation of these biomarkers in patient bone marrow samples demonstrates the model's potential as a robust foundation for future clinical trial integration.