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Isolation and Flow Cytometric Assessment of Neuroimmune Interactions in a Mini-Stroke Murine Model
Published on: June 20, 2025
Identification of key genes and metabolites in acute ischemic stroke via integrated transcriptomic and metabolomic
Jiayan Zhang1, Xin Zhou1, Dian He1
1Department of Neurology, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.
Background:
Acute ischaemic stroke (AIS) remains a leading cause of death and disability. This exploratory study integrated transcriptomic and metabolomic analyses to identify candidate hub genes and metabolites associated with AIS and to provide preliminary insights into its molecular mechanisms and potential therapeutic targets.
Methods:
Metabolomic sequencing data were analyzed using orthogonal partial least squares discriminant analysis (OPLS-DA), together with multivariate and univariate analyses, to identify differentially expressed metabolites (DEMs). Differential gene expression analysis was performed using the GSE16561 dataset, and key genes were screened through protein-protein interaction network analysis, machine learning, expression validation, and diagnostic evaluation. Co-enrichment analysis of key genes and DEMs was then conducted, followed by immune infiltration analysis, molecular network construction, drug prediction, and RT-qPCR validation.
Results:
A total of 103 and 51 DEMs were identified in the positive and negative ion modes, respectively, and were mainly enriched in steroid hormone biosynthesis. Five key genes-ITGAM, TLR4, MMP9, STAT3, and TLR2-were identified as significantly dysregulated in AIS and showed good diagnostic performance. RT-qPCR confirmed increased expression of TLR4, MMP9, and TLR2 in AIS, whereas ITGAM and STAT3 showed inverse trends. Joint enrichment analysis indicated that key genes and DEMs were mainly involved in 30 pathways, including the HIF-1 and FoxO signaling pathways. Immune infiltration analysis showed that ITGAM had the strongest negative correlation with memory B cells (p < 0.01, r = -0.39). In the clinical metabolomics cohort, 10 AIS patients and 10 non-stroke controls were included. AIS patients were 70.0% male (7/10) with a mean age of 65.70 ± 6.48 years, while controls were 60.0% male (6/10) with a mean age of 66.64 ± 7.18 years. Blood samples were obtained within 24 h of stroke onset. Stroke subtypes included large artery atherosclerosis (n = 3), cardioembolism (n = 5), and small artery occlusion (n = 2). None of the patients underwent intravenous thrombolysis or mechanical thrombectomy, all received standard medical treatment for AIS. In addition, 28 candidate drugs, including paregoric and celecoxib, were predicted.
Conclusion:
This exploratory study identified candidate hub genes and metabolites associated with AIS and highlighted their related pathways, immune features, molecular networks, and candidate drugs. These findings provide preliminary insights into the molecular basis and potential therapeutic targets of AIS.
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