TITLE:
Identification of Immune-Related Diagnostic Biomarkers and Construction of a Combined Predictive Model for Intracranial Aneurysm Based on Bioinformatics Analysis
AUTHORS:
Xiaochi Jiao, Yingying Huang, Xiang Qin, Shengliang Wei, Huadong Huang, Yixia Yin
KEYWORDS:
Intracranial Aneurysm, Bioinformatics Analysis, Immune-Related Genes, Diagnostic Model
JOURNAL NAME:
Journal of Biosciences and Medicines,
Vol.14 No.5,
May
26,
2026
ABSTRACT: Objective: This study aimed to systematically identify core immune-related genes involved in the development and progression of intracranial aneurysm (IA) through bioinformatics analysis, validate their differential expression in an independent dataset, and construct a precise diagnostic prediction model. The findings are intended to provide a theoretical basis for the early non-invasive diagnosis of IA, enrich the understanding of its immune-inflammatory pathological mechanisms, and offer potential intervention targets and novel research directions for clinical diagnosis and treatment. Methods: The GSE75436 dataset was downloaded from the Gene Expression Omnibus (GEO) database as the training set. Differentially expressed genes (DEGs) were screened using the “limma” package in R software. Immune-related differentially expressed genes (IM-DEGs) were identified by cross-referencing with the ImmPort database. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to elucidate the biological functions and signaling pathways associated with the IM-DEGs. To visualize protein-protein interactions (PPI), STRING, Cytoscape, and the CytoHubba plugin were utilized to construct a PPI network and identify hub genes. The GSE54083 dataset was then downloaded as an independent validation set to verify the expression of the core genes. Genes exhibiting statistically significant differential expression in both the training and validation sets were selected to construct an IA diagnostic model based on a linear prediction algorithm. The diagnostic performance of the model was assessed using receiver operating characteristic (ROC) curves in both datasets, and correlation analysis of the core genes was conducted to reveal their synergistic regulatory relationships. Results: A total of 173 IM-DEGs were identified from the intersection of 757 DEGs in the training set GSE75436 and the immune gene list. These genes were predominantly enriched in biological processes such as leukocyte migration, leukocyte-mediated immunity, cell chemotaxis, and positive regulation of cytokine production. Analysis using Cytoscape software identified the top 10 hub genes with the highest scores: IL1B, TNF, CXCL8, CD8A, TYROBP, CCR1, CXCL10, CCL4, CCR5, and CCL20. Validation using the independent dataset GSE54083 confirmed that three hub genes (CXCL8, TNF, and CD8A) exhibited strong diagnostic potential. Expression validation revealed significant differences in the relative expression of these three genes between the control and aneurysm groups (P Conclusion: By integrating GEO data mining with independent dataset cross-validation, this study identified CXCL8, TNF, and CD8A as key genes associated with the immune-inflammatory pathogenesis of intracranial aneurysm. A combined prediction model constructed from these genes demonstrated robust diagnostic efficacy, thereby providing a novel combination of biomarkers and a theoretical foundation for the early non-invasive diagnosis and immune-targeted therapy of IA.