Identification of Potential Neddylation-related Key Genes in Ischemic Stroke based on Machine Learning Methods

Abstract

Ischemic stroke (IS) is a complex neurological disease that can lead to severe disability or even death. Understanding the molecular mechanisms involved in the occurrence and progression of IS is of great significance for developing effective treatment strategies. In this context, the role of neddylation refers to the potential impact of neddylation on various cellular processes, which may contribute to the pathogenesis and outcome of IS. First, differential analysis was conducted on the GSE16561 dataset from the GEO database to identify 350 differentially expressed genes (DEGs) between the IS and Control groups. By intersecting the differential genes with neddylation-related genes, 11 neddylation-related DEGs were obtained. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses showed that the DEGs were mainly enriched in hematopoietic cell lineage and neutrophil degranulation, while the neddylation-related DEGs were mainly enriched in apoptosis and post-translational protein modification. Further Lasso-Cox and random forest analyses were performed on the 11 neddylation-related DEGs, identifying key genes SRPK1, BIRC2, and KLHL3. Additionally, validation of the key genes was carried out using the GSE58294 dataset and clinical patients. Finally, the correlation between the key genes and ferroptosis and cuproptosis was analyzed, and a ceRNA network was constructed. Our study helps to elucidate the complex role of neddylation in the mechanism of ischemic stroke, providing potential opportunities for the development of therapeutic interventions.

Keywords: Bioinformatics; Biomarker; Ischemic stroke; Machine learning; Neddylation.