Abstract
INTRODUCTION: Patients with acute myocardial infarction (AMI) are at high risk of progressing to heart failure (HF). Recent research has shown that lipid droplet-related genes (LDRGs) play a crucial role in myocardial metabolism following MI, thereby influencing the progression to HF. METHODS: Weighted gene co-expression network analysis (WGCNA) and differential expression gene analysis were used to screen a transcriptome dataset of whole blood cells from AMI patients with (AMI HF, n = 16) and without progression (AMI no-HF, n = 16). Functional enrichment analysis were performed to observe the involved function. Machine learning methods were used to screen the genes related to prognosis. Transcriptional factors (TF) were predicted by using relevant databases. ROC curves were drawn to evaluate the TF-LDRG pair in predicting HF in the validation dataset (n = 16) and the clinical trial (n = 13). RESULTS: The 235 identified genes were primarily involved in pathways related to fatty acid and energy metabolism. 22 genes were screened out that they were strongly associated with prognosis. 35 corresponding transcription factors were predicted. The TF-LDRG pair, ABHD5-ARID3a, was demonstrated good predictive accuracy. DISCUSSION: Our findings suggest that ABHD5-ARID3a have significant potential as predictive biomarkers for heart failure post-AMI which also provides a foundation for further exploration into the molecular mechanisms underlying the progression from AMI to HF.