- Academic Editor
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Background: This study used
bioinformatics combined with statistical methods to identify plasma biomarkers
that can predict intracranial aneurysm (IA) rupture and provide a strong
theoretical basis for the search for new IA rupture prevention methods.
Methods: We downloaded gene expression profiles in the
GSE36791 and GSE122897 datasets from the Gene
Expression Omnibus (GEO) database. Data were normalized using the “sva” R
package and differentially expressed genes (DEGs) were identified using the
“limma” R package. Gene Ontology (GO) and Kyoto Encyclopedia
of Genes and Genomes (KEGG) pathway enrichment analyses were used for DEG
function analysis. Univariate logistic regression analysis, least absolute
shrinkage and selection operator (LASSO) regression modeling, and the support
vector machine recursive feature elimination
(SVM-RFE) algorithm were used to identify key
biomarker genes. Data from GSE122897 and GSE13353 were
extracted to verify our findings. Results: Eight
co-DEG mRNAs were identified in the GSE36791 and GSE122897
datasets. Genes associated with inflammatory responses were clustered in the
co-DEG mRNAs in IAs. CD6 and C-C chemokine receptor 7 (CCR7) were identified as key genes associated with IA. CD6
and CCR7 were upregulated in patients with IA and their expression
levels were positively correlated. There were significant differences in the
infiltration of immune cells between IAs and normal vascular wall tissues
(p