In Frontiers in pharmacology
Background: Aneurysmal subarachnoid hemorrhage (aSAH) is a devastating hemorrhagic stroke with high disability and mortality. Neuroinflammation and the immunological response after aSAH are complex pathophysiological processes that have not yet been fully elucidated. Therefore, attention should be paid to exploring the inflammation-related genes involved in the systemic response to the rupture of intracranial aneurysms. Methods: The datasets of gene transcriptomes were downloaded from the Gene Expression Omnibus database. We constructed a gene co-expression network to identify cluster genes associated with aSAH and screened out differentially expressed genes (DEGs). The common gene was subsequently applied to identify hub genes by protein-protein interaction analysis and screen signature genes by machine learning algorithms. CMap analysis was implemented to identify potential small-molecule compounds. Meanwhile, Cibersort and ssGSEA were used to evaluate the immune cell composition, and GSEA reveals signal biological pathways. Results: We identified 602 DEGs from the GSE36791. The neutrophil-related module associated with aSAH was screened by weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis. Several small molecular compounds were predicted based on neutrophil-related genes. MAPK14, ITGAM, TLR4, and FCGR1A have been identified as crucial genes involved in the peripheral immune activation related to neutrophils. Six significant genes (CST7, HSP90AB1, PADI4, PLBD1, RAB32, and SLAMF6) were identified as signature biomarkers by performing the LASSO analysis and SVM algorithms. The constructed machine learning model appears to be robust by receiver-operating characteristic curve analysis. The immune feature analysis demonstrated that neutrophils were upregulated post-aSAH and PADI4 was positively correlated with neutrophils. The NETs pathway was significantly upregulated in aSAH. Conclusion: We identified core regulatory genes influencing the transcription profiles of circulating neutrophils after the rupture of intracranial aneurysms using bioinformatics analysis and machine learning algorithms. This study provides new insight into the mechanism of peripheral immune response and inflammation after aSAH.
Weng Weipin, Cheng Fan, Zhang Jie
2022
aneurymal subarachnoid hemorrhage, machine learning, neutrophil, peripheral inflammatory response, transcription profiles
