Machine learning using scRNA-seq Combined with bulk-seq to identify lactylation-related hub genes in carotid arteriosclerosis.

Atherosclerosis is a chronic inflammatory disease, this study aims to investigate the immune landscape in carotid atherosclerotic plaque formation and explore diagnostic biomarkers of lactylation-associated genes, so as to gain new insights into underlying molecular mechanisms and provide new perspectives for disease detection and treatment. Single cell transcriptome data and Bulk transcriptome data of carotid atherosclerosis samples were obtained from the Gene Expression Omnibus (GEO). Eleven cell types were identified by scRNA-seq data. Lactylation scores were significantly higher in γδT cells than in cells of other subtypes, but lower in plasma cells than in cells of other subtypes. The scores of malignant related pathways were significantly increased in cells with high lactylation scores. scRNA-seq combined with bulk-seq identified differentially expressed lactylation genes in carotid atherosclerosis. A diagnostic model was constructed by combining 10 machine learning algorithms and 101 algorithms, SOD1, DDX42 and PDLIM1 as core genes. Further analysis revealed that the expression levels of core genes were significantly correlated with immune cell infiltration, and their regulatory networks were constructed. Clinical samples verified that the expression of core gene in unstable plaque was significantly lower than that in stable plaque, suggesting that it has protective effect on atherosclerosis. By combining scRNA-seq and Bulk transcriptome data in this study, three lactylation-associated genes SOD1, DDX42 and PDLIM1 were identified in carotid atherosclerosis samples, providing targets for the diagnosis and treatment of carotid atherosclerosis samples.