ABSTRACT
Objective: Cerebral ischemia can cause mild damage to local brain nerves due to hypoxia and even lead to irreversible damage due to neuronal cell death. However, the underlying pathogenesis of this phenomenon remains unclear. This study utilized bioinformatics to explore the role of cuproptosis in cerebral ischemic disease and its associated biomarkers. Method: R software identified the overlap of cerebral ischemia and cuproptosis genes, analyzed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and explored hub genes. Expressions and localizations of hub genes in brain tissue, cells, and immune cells were analyzed, along with predictions of protein structures, miRNAs, and transcription factors. A network was constructed depicting hub gene co-expression with miRNAs and interactions with transcription factors. Ferredoxin 1 (FDX1) expression was determined using qRT-PCR. Results: Ten cuproptosis-related genes in cerebral ischemia were identified, with GO analysis revealing involvement in acetyl-CoA synthesis, metabolism, mitochondrial function, and iron-sulfur cluster binding. KEGG highlighted processes like the tricarboxylic acid cycle, pyruvate metabolism, and glycolysis/gluconeogenesis. Using the Human Protein Atlas, eight hub genes associated with cuproptosis were verified in brain tissues, hippocampus, and AF22 cells. Lipoyl(octanoyl) transferase 1 (LIPT1), was undetected, while others were found in mitochondria or both nucleus and mitochondria. These genes were differentially expressed in immune cells. FDX1, lipoic acid synthetase (LIAS), dihydrolipoamide S-acetyltransferase (DLAT), pyruvate dehydrogenase E1 component subunit alpha 1 (PDHA1), PDHB, and glutaminase (GLS) were predicted to target 111 miRNAs. PDHA1, FDX1, LIPT1, PDHB, LIAS, DLAT, GLS, and dihydrolipoamide dehydrogenase (DLD) were predicted to interact with 11, 10, 10, 9, 8, 7, 5, and 4 transcription factors, respectively. Finally, FDX1 expression was significantly upregulated in the hippocampus of ovariectomized rats with ischemia. Conclusion: This study revealed an association between cerebral ischemic disease and cuproptosis, identifying eight potential target genes. These findings offer new insights into potential biomarkers for the diagnosis, treatment, and prognosis of cerebral ischemia, and provide avenues for the exploration of new medical intervention targets.
ABSTRACT
Ischemic stroke (IS) is of increasing concern given the aging population and prevalence of unhealthy lifestyles, with older females exhibiting higher susceptibility. This study aimed to identify practical diagnostic markers, develop a diagnostic model for immunogenic cell death (ICD)-associated IS, and investigate alterations in the immune environment caused by hub genes. Differentially expressed genes associated with ICD in IS were identified based on weighted gene co-expression network analysis and the identification of significant modules. Subsequently, machine learning algorithms were employed to screened hub genes, which were further assessed using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis. A nomogram mode lwas then constructed for IS diagnosis, and its diagnostic value was assessed using a receiver operating characteristic curve. Finally, alterations in immune cell infiltration were assessed within patients with IS, and the pan-cancer expression patterns of hub genes were evaluated. Three hub genes associated with ICD (PDK4, CCL20, and FBL) were identified. The corresponding nomogram model for IS diagnosis could effectively identify older female patients with IS (area under the curve (AUC) = 0.9555). Overall, the three hub genes exhibit good diagnostic value (AUC > 0.8). CCL20 and FBL are significantly associated with the extent of immune cells infiltration. Moreover, a strong link exists between hub gene expression and pan-cancer prognosis. Cumulatively, these results indicate that ICD-related hub genes critically influence IS progression in older females, presenting novel diagnostic and therapeutic targets for personalized treatment.