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: Gaiyan Li, Yu Cheng, Shanshan Ding, Qianyun Zheng, Lanqiong Kuang, Ying Zhang, Ying Zhou
Format: Article
Language:English
Published: BMC 2025-01-01
Series:BMC Neuroscience
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Online Access:https://doi.org/10.1186/s12868-024-00921-9
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author Gaiyan Li
Yu Cheng
Shanshan Ding
Qianyun Zheng
Lanqiong Kuang
Ying Zhang
Ying Zhou
author_facet Gaiyan Li
Yu Cheng
Shanshan Ding
Qianyun Zheng
Lanqiong Kuang
Ying Zhang
Ying Zhou
author_sort Gaiyan Li
collection DOAJ
description 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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spelling doaj-art-345e3ecdb4c74600a94d470d9dab23312025-01-19T12:11:37ZengBMCBMC Neuroscience1471-22022025-01-0126111210.1186/s12868-024-00921-9Identification of key genes associated with oxidative stress in ischemic stroke via bioinformatics integrated analysisGaiyan Li0Yu Cheng1Shanshan Ding2Qianyun Zheng3Lanqiong Kuang4Ying Zhang5Ying Zhou6Department of Rehabilitation, Shanghai Xuhui Central HospitalDepartment of Image, Shanghai Xuhui Central HospitalDepartment of Rehabilitation, Shanghai Xuhui Central HospitalDepartment of Rehabilitation, Shanghai Xuhui Central HospitalDepartment of Image, Shanghai Xuhui Central HospitalDepartment of Rehabilitation, Shanghai Xuhui Central HospitalDepartment of General Practice, Shanghai Xuhui Central HospitalAbstract 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.https://doi.org/10.1186/s12868-024-00921-9Ischemic strokeOxidative stressMachine learning algorithmWGCNA
spellingShingle Gaiyan Li
Yu Cheng
Shanshan Ding
Qianyun Zheng
Lanqiong Kuang
Ying Zhang
Ying Zhou
Identification of key genes associated with oxidative stress in ischemic stroke via bioinformatics integrated analysis
BMC Neuroscience
Ischemic stroke
Oxidative stress
Machine learning algorithm
WGCNA
title Identification of key genes associated with oxidative stress in ischemic stroke via bioinformatics integrated analysis
title_full Identification of key genes associated with oxidative stress in ischemic stroke via bioinformatics integrated analysis
title_fullStr Identification of key genes associated with oxidative stress in ischemic stroke via bioinformatics integrated analysis
title_full_unstemmed Identification of key genes associated with oxidative stress in ischemic stroke via bioinformatics integrated analysis
title_short Identification of key genes associated with oxidative stress in ischemic stroke via bioinformatics integrated analysis
title_sort identification of key genes associated with oxidative stress in ischemic stroke via bioinformatics integrated analysis
topic Ischemic stroke
Oxidative stress
Machine learning algorithm
WGCNA
url https://doi.org/10.1186/s12868-024-00921-9
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