The use of multiple datasets to identify autophagy-related molecular mechanisms in intracerebral hemorrhage

Background: Intracerebral hemorrhage (ICH) is a stroke syndrome with high mortality and disability rates, but autophagy's mechanism in ICH is still unclear. We identified key autophagy genes in ICH by bioinformatics methods and explored their mechanisms. Methods: We downloaded ICH patient chip data from the Gene Expression Omnibus (GEO) database. Based on the GENE database, differentially expressed genes (DEGs) for autophagy were identified. We identified key genes through protein-protein interaction (PPI) network analysis and analyzed their associated pathways in Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Gene-motif rankings, miRWalk and ENCORI databases were used to analyze the key gene transcription factor (TF) regulatory network and ceRNA network. Finally, relevant target pathways were obtained by gene set enrichment analysis (GSEA). Results: Eleven autophagy-related DEGs in ICH were obtained, and IL-1B, STAT3, NLRP3 and NOD2 were identified as key genes with clinical predictive value by PPI and receiver operating characteristic (ROC) curve analysis. The candidate gene expression level was significantly correlated with the immune infiltration level, and most of the key genes were positively correlated with the immune cell infiltration level. The key genes are mainly related to cytokine and receptor interactions, immune responses and other pathways. The ceRNA network predicted 8,654 interaction pairs (24 miRNAs and 2,952 lncRNAs). Conclusion: We used multiple bioinformatics datasets to identify IL-1B, STAT3, NLRP3 and NOD2 as key genes that contribute to the development of ICH.