Integrating single-cell analysis and machine learning algorithms to explore lactylation-related molecular mechanisms...

Lactylation is a novel histone modification driven by lactate accumulation, which has been implicated in clear cell renal cell carcinoma (ccRCC) progression. However, its comprehensive molecular mechanism and clinical relevance remain poorly understood. This study aimed to investigate lactylation-related molecular mechanisms and therapeutic responses in ccRCC using integrated single-cell analysis and machine learning algorithms.

We integrated single-cell RNA sequencing and bulk transcriptomic data from patients with ccRCC. Transcriptional signatures of lactylation-related genes were evaluated using four gene set scoring algorithms. Key lactylation-related genes were identified through weighted gene co-expression network analysis and differential expression analysis. A prognostic model was constructed using 10 machine learning algorithms and subsequently validated in independent cohorts. The functional role of the core gene, CDT1, was validated through in vitro and in vivo experiments.

We established a prognostic model comprising 13 lactylation-related genes. The model robustly stratified patients into high- and low-risk groups with distinct survival outcomes, immune microenvironment features, and immunotherapy responses. Functional assays revealed that CDT1, the core gene of the signature, promoted ccRCC cell proliferation, migration, and invasion. Moreover, modulation of CDT1 altered intracellular L-lactate levels. However, whether this effect reflects a direct metabolic-epigenetic regulatory mechanism or is secondary to proliferative changes remains to be elucidated.

This study delineates the molecular heterogeneity associated with lactylation-related gene expression in ccRCC and presents a validated prognostic model. It identifies CDT1 as a novel oncogene and potential biomarker, providing insights into metabolic-epigenetic crosstalk and a foundation for future therapeutic development.