Identification of key genes associated with oxidative stress in ischemic stroke via bioinformatics integrated analysis
Abstract Background Ischemic 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. Methods OS-related key genes were obtianed b...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
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| Series: | BMC Neuroscience |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12868-024-00921-9 |
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| Summary: | Abstract Background Ischemic 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. Methods OS-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. Results A 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. Conclusion In 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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| ISSN: | 1471-2202 |