HSPA2 as a potential lactylation-related key gene in heart failure identified by integrated bioinformatics analysis

Abstract

Background

Lactylation modification has been reported to increase protein function and regulation complexity. However, mechanisms underlying lactylation in heart failure (HF) remain incompletely understood.

Methods

We analyzed the GSE57338 through differential expression analysis and WGCNA to identify HF-associated genes, which were intersected with known lactylation-related genes (LRGs) to identify potential LRGs in HF. Consensus clustering identified lactylation-associated clusters. We profiled molecular and immune features of patient subtypes using GSVA and CIBERSORT. We compared machine learning models (RF, SVM, GLM) and developed a nomogram, validating it with GSE5406 and in vivo experiments.

Results

Intersection analysis identified MYH6 and HSPA2 as the two key LRGs in HF. Consensus clustering revealed two distinct clusters, showing different metabolic features. Immune analysis revealed a positive correlation between HSPA2 and CD8+ T cells, and between MYH6 and M2 macrophages. GLM exhibited better fitting performance than RF and SVM. Finally, the nomogram demonstrated robust predictive performance (AUC > 0.8) in both training and validation set. Notably, HSPA2 was identified as significantly upregulated in HF.

Conclusion

Our study identified two key LRGs in HF (MYH6 and HSPA2) and established an HF prediction nomogram. The identification of HSPA2 as a promising target suggests a novel clinical strategy for HF.