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pmid:40307317

The impact of inflammation and iron metabolism on gene expression alterations in ischemic stroke: a bioinformatics approach.

Shengwu Wang · Xuemei Li · Youcai Bi · Chao Yan · Yunbo Chen

Scientific reports · 2025 doi:10.1038/s41598-025-00369-9

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:40307317
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title
The impact of inflammation and iron metabolism on gene expression alterations in ischemic stroke: a bioinformatics approach.
doi
10.1038/s41598-025-00369-9
pmid
40307317
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[0]
Shengwu Wang
[1]
Xuemei Li
[2]
Youcai Bi
[3]
Chao Yan
[4]
Yunbo Chen
journal
Scientific reports
year
2025
abstract
This study explores the differential expression of inflammation and iron metabolism-related genes (IIMRDEGs) in Ischemic Stroke (IS), a major contributor to global morbidity and mortality. Using the Gene Expression Omnibus (GEO) query tool, we integrated gene expression datasets GSE22255 and GSE16561. We identified 56 differentially expressed genes (DEGs), including 42 that were upregulated and 14 downregulated, according to criteria of |logFC| > 0.5 and p < 0.05. An intersection with known IIMRDEGs revealed 16 genes with significant relevance to IS, such as SLC22A4 and DUSP1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that these genes are mainly involved in leukocyte chemotaxis and responses to bacterial molecules, in addition to IL-17 and TNF signaling pathways. A protein-protein interaction (PPI) network of 12 IIMRDEGs identified 8 hub genes, including IL7R and ADM, which exhibited significant expression differences (p < 0.001) and potential diagnostic utility with AUC values between 0.7 and 0.9 in ROC curve analysis. Furthermore, immune infiltration analysis showed notable differences in 7 immune cell types between IS and control samples. Our findings advance the understanding of ischemic stroke mechanisms and present potential biomarkers for improving diagnosis and therapeutic strategies.
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To analyze the differences in gene expression values between the IS samples and control samples in the combined GEO datasets, the R package limma was used for differential analysis.
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sections[5] "Cerebral infarction-related inflammation and iron metabolism related differentially expressed genes" paragraph 2
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To analyze the differences in gene expression values between the IS samples and control samples in the combined GEO datasets, the R package limma was used for differential analysis.
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An intersection with known IIMRDEGs revealed 16 genes with significant relevance to IS, such as SLC22A4 and DUSP1.
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An intersection with known IIMRDEGs revealed 16 genes with significant relevance to IS, such as SLC22A4 and DUSP1.
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An intersection with known IIMRDEGs revealed 16 genes with significant relevance to IS, such as SLC22A4 and DUSP1.
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Gene expression analysis of peripheral whole blood RNA following ischemic stroke
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To analyze the differences in gene expression values between the IS samples and control samples in the combined GEO datasets, the R package limma was used for differential analysis.
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Using the Gene Expression Omnibus (GEO) query tool, we integrated gene expression datasets GSE22255 and GSE16561. We identified 56 differentially expressed genes (DEGs), including 42 that were upregulated and 14 downregulated, according to criteria of |logFC| > 0.5 and p < 0.05. An intersection with known IIMRDEGs revealed 16 genes with significant relevance to IS, such as SLC22A4 and DUSP1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that these genes are mainly involved in leukocyte chemotaxis and responses to bacterial molecules, in addition to IL-17 and TNF signaling pathways. A protein-protein interaction (PPI) network of 12 IIMRDEGs identified 8 hub genes, including IL7R and ADM, which exhibited significant expression differences (p < 0.001) and potential diagnostic utility with AUC values between 0.7 and 0.9 in ROC curve analysis. Furthermore, immune infiltration analysis showed notable differences in 7 immune cell types between IS and control samples. 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