ArticleBMC neuroscience2025
Identification of key genes associated with oxidative stress in ischemic stroke via bioinformatics integrated analysis.
Article in BMC neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Identified BIRC5 and HDAC1 as novel diagnostic biomarkers linked to centrosome-immune crosstalk for cutaneous squamous cell carcinoma via machine learning-based multi-omics analysis.Discover oncology · 2026Article
- HSPA8 lactylation attenuates neuronal pyroptosis via E3 ligase-mediated NLRP3 degradation after ischemic stroke.Apoptosis : an international journal on programmed cell death · 2026Article
- Article
- Identification of Senescence- and Inflammation-Related Genes and Immune Microenvironment Characterization in Intracranial AneurysmsEndocrine, metabolic & immune disorders drug targets · 2026Article
- Single-cell transcriptome integrated with genome-wide association study reveals heterogeneity of carotid and femoral plaques and its association with plaque stability.Scientific reports · 2025Article
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7 authors.
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Abstract
backgroundIschemic stroke (IS) is a common cerebrovascular disease. Although the formation of atherosclerosis, which is closely related to oxidative stress (OS), is associated with stroke-related deaths. However, the role of OS in IS is unknown.
methodsOS-related key genes were obtianed by overlapping the differentially expressed genes (DEGs) between IS and normal control (NC) specimens, IS-related genes, and OS-related genes. Then, we investigated the mechanism of action of key genes. Subsequently, protein-protein interaction (PPI) network and machine learning algorithms were utilized to excavate feature genes. In addition, the network between feature genes and microRNAs (miRNAs) was established to investigate the regulatory mechanism of feature genes. Finally, quantitative PCR (qPCR) was utilized to validate the expression of feature genes with blood specimens.
resultsA total of 42 key genes related to OS were acquired. Enrichment analysis indicated that the key genes were associated with oxidative stress, reactive oxygen species, lipid and atherosclerosis, and cell migration-related pathways. Then, 6 feature genes (HSPA8, NCF2, FOS, KLF4, THBS1, and HSPA1A) related to OS were identified for IS. Besides, 6 feature genes and 255 miRNAs were utilized to establish a feature genes-miRNA network which contained 261 nodes and 277 edges. At last, qPCR results revealed that there was a trend for higher expression of FOS, KLF4, and HSPA1A in IS specimens than in NC specimens. Additionally, HSPA8 expression was significantly decreased in the IS specimens, which was consistent with the findings of the GEO database analysis.
conclusionIn conclusion, 6 feature genes (HSPA8, NCF2, FOS, KLF4, THBS1, and HSPA1A) related to OS were mined by bioinformatics analysis, which might provide a new insights into the evaluation and treatment of IS. CLINICAL TRIAL NUMBER: Not applicable.
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