Deciphering the role of nicotinamide metabolism and melanin-related genes in acute myocardial infarction: a machine learning approach integrating bioinformatics analysis.

Abstract

Acute myocardial infarction (AMI) represents a significant global mortality factor. Alterations in nicotinamide metabolism within the myocardium post-AMI can influence the progression of the condition. Additionally, melanin plays a crucial role in nicotinamide metabolism and exhibits anti-inflammatory properties. Nevertheless, the diagnostic biomarkers for AMI that are based on nicotinamide metabolism and melanin-associated genes remain poorly defined. In this study, the AMI transcriptomic data from the Gene Expression Omnibus were analyzed to identify differentially expressed genes (DEGs) intersecting with nicotinamide metabolism and melatonin-related genes. Machine learning algorithms, including RandomForest, least absolute shrinkage and selection operator, and support vector machine-recursive feature elimination, were applied to select feature genes. Diagnostic markers were further evaluated based on area under the curve from receiver operating characteristic analysis. We identified 14 candidate genes, refined to 4 key genes, with NAMPT and BST1 ultimately selected as diagnostic biomarkers. These were used to classify AMI into two molecular subtypes. Immune landscape analysis revealed increased infiltration of monocytes, neutrophils, macrophages, and parainflammation in AMI. Enrichment analyses showed DEGs were mainly involved in innate immune response and cytokine production. Additionally, hsa-miR-34a-5p and hsa-miR-181b-5p were identified as potential regulators of NAMPT and BST1. In summary, NAMPT and BST1 are promising diagnostic biomarkers associated with nicotinamide metabolism and melatonin in AMI. The molecular subtyping based on these genes will enhance the management and hierarchical treatment of AMI, offering significant implications for clinical diagnosis and therapeutic strategies.