Identification of a Lactylation-Related Gene Signature and Candidate Biomarkers for Dilated Cardiomyopathy.

Dilated cardiomyopathy (DCM) is a leading cause of heart failure. Due to its complex pathogenesis, effective treatment strategies remain limited. Therefore, it is particularly important to identify novel candidate biomarkers from the pathogenesis level. In recent years, lactylation modification plays an important role in various cardiovascular diseases; however, its specific involvement in DCM remains unclear. Therefore, it is of great significance to identify lactylation-related biomarkers associated with DCM to provide insights for future mechanistic investigations. The DCM gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes and co-expression modules were identified using differential expression analysis and weighted gene co-expression network analysis (WGCNA). Lactylation-related gene sets (LRGs) were obtained from the MSigDB database. The intersection of these results was further analysed using machine learning algorithms: Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Support Vector Machine (SVM). CIBERSORT algorithm was used to analyse the immune infiltration characteristics of DCM, and DCM was divided into two subtypes by unsupervised consensus clustering. Mendelian randomization (MR) analysis was employed to evaluate potential causal associations between the candidate genes and DCM or heart failure progression. Finally, the candidate genes were verified by the GSE57338 database and β