Identification of arachidonic acid metabolism-related diagnostic markers in heart failure based on bioinformatics analysis and machine learning.

Background: Heart failure (HF) represents the terminal phase of multiple cardiovascular conditions and is associated with significant morbidity and mortality rates. Arachidonic acid (AA), an essential fatty acid, plays a crucial role in modulating cardiovascular function under both normal and disease states. The purpose of this research was to examine how AA is related to HF, providing new perspective for individualized treatment. Methods: Transcriptomic datasets were retrieved from the Gene Expression Omnibus (GEO) database. The raw data were consolidated to identify differentially expressed genes (DEGs) and subsequently subjected to bioinformatics analysis. Gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. Signature genes were identified through Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) algorithms. Receiver Operating Characteristic (ROC) curves were generated for gene evaluation, and a nomogram was developed. An analysis of immune cell infiltration was conducted using Single Sample Gene Set Enrichment Analysis (ssGSEA), and Gene Set Enrichment Analysis (GSEA) was conducted to determine important pathways. Subsequently, we also performed drug sensitivity evaluation. Finally, the expression levels of the identified signature genes in HF samples were confirmed using qRT-PCR analysis. Results: Four characteristic genes demonstrating favorable performance in the ROC analysis. The comprehensive nomogram developed in this study exhibited enhanced clinical utility. In addition, notable variations in immune cell infiltration levels were detected, and GSEA highlighted key biological pathways. Conclusion: This investigation demonstrated a strong association between arachidonic acid-associated gene expression and heightened risk of HF, offering novel perspectives on the disease's underlying pathological processes and providing potential insights for personalized management of HF.