Identification of the cuproptosis-related ceRNA network and risk model in acute ischemic stroke by integrated bioinformatics analysis

Abstract

Abstract Background Acute ischemic stroke (AIS) is one of the leading contributors to death and disability in adults. And cuproptosis is a novel type of cell death. Yet, its role in AIS is still unknown. Methods The mRNA, miRNA, and circRNA expression data were downloaded from the Gene Expression Omnibus database. We explored differentially expressed circRNAs (DEcircRNAs), microRNAs (DEmiRNAs), and cuproptosis-related genes (DECuRGs) after AIS. With the target prediction tools, we constructed a cuproptosis-related competitive endogenous RNA (ceRNA) network mediated by circRNAs in AIS. Afterward, functional enrichment analysis, cytoHubba plugin, protein–protein interaction, weighted gene co-expression network analysis, and unsupervised clustering analysis were performed to determine the critical genes and relevant pathways. Machine learning techniques were used to identify the optimal risk model. The CIBERSORT was applied to explore the immune-infiltrating characteristics in AIS samples. Finally, two independent datasets were employed to verify the predictive value of the risk model. Results Altogether, 26 DECuRGs were identified in this study. Enrichment analysis revealed that they participated in the reactive oxygen metabolism, inflammatory responses, and corresponding cuproptosis-related biological processes. Of the DECuRGs, MTF1 and UBE2D2 were included in the ceRNA network, comprising three circRNA-miRNA and two miRNA-mRNA interaction pairs. Hub gene analysis determined the hub regulatory axis in the process of cuproptosis, namely, MTF1-miR-765-circ_0040760/0068531. We finally constructed a 5-gene risk model (C10orf32, NUCB1, AX748267, MRPL28, and PPP1R15A) by multiple analyses, which was validated by two independent datasets (AUC = 0.958 and 0.668). Besides, significant differences in immune cell infiltration were observed between AIS patients and normal controls. The levels of neutrophils were correlated with most of the DECuRGs. The ceRNA axis identified in this study was also associated with the immune microenvironment of AIS patients. Conclusion The findings revealed that cuproptosis might be associated with AIS and that the key nodes, including the regulatory axes, might exert critical roles in the process of AIS. The risk model provided new insights into the early diagnosis and treatment of AIS.

Keywords

• Acute ischemic stroke
• Cuproptosis
• ceRNA network
• Machine learning
• Immune infiltration
• Risk model