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Bioinformatics analysis prioritized PTGS2 and MMP9 as candidate inflammatory genes associated with <t>the</t> <t>IL-17</t> signaling pathway in ischemic stroke. (A) Venn diagram showing the overlap between differentially expressed genes (DEGs) identified from GEO datasets and genes retrieved from the CellAge database. Five overlapping genes were identified: PTGS2, MMP9, SERPINB2, CTNNAL1, and HTRA1. CS, genes from CellAge database; DEG, significantly differentially expressed genes in ischemic stroke. (B) Gene Ontology (GO) enrichment analysis of the overlapping genes, including biological process, cellular component, and molecular function categories. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showing that the overlapping genes were enriched in inflammation-related pathways, including the IL-17 signaling pathway and TNF signaling pathway. (D) GSEA comparing ischemic stroke (IS) and control samples, demonstrating enrichment of immune- and inflammation-related pathways in IS. (E) Predicted transcription factor–target gene regulatory network for PTGS2 and MMP9, showing candidate shared upstream transcription factors. Yellow nodes represent TFs, red nodes represent target genes.
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Bioinformatics analysis prioritized PTGS2 and MMP9 as candidate inflammatory genes associated with <t>the</t> <t>IL-17</t> signaling pathway in ischemic stroke. (A) Venn diagram showing the overlap between differentially expressed genes (DEGs) identified from GEO datasets and genes retrieved from the CellAge database. Five overlapping genes were identified: PTGS2, MMP9, SERPINB2, CTNNAL1, and HTRA1. CS, genes from CellAge database; DEG, significantly differentially expressed genes in ischemic stroke. (B) Gene Ontology (GO) enrichment analysis of the overlapping genes, including biological process, cellular component, and molecular function categories. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showing that the overlapping genes were enriched in inflammation-related pathways, including the IL-17 signaling pathway and TNF signaling pathway. (D) GSEA comparing ischemic stroke (IS) and control samples, demonstrating enrichment of immune- and inflammation-related pathways in IS. (E) Predicted transcription factor–target gene regulatory network for PTGS2 and MMP9, showing candidate shared upstream transcription factors. Yellow nodes represent TFs, red nodes represent target genes.
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Bioinformatics analysis prioritized PTGS2 and MMP9 as candidate inflammatory genes associated with <t>the</t> <t>IL-17</t> signaling pathway in ischemic stroke. (A) Venn diagram showing the overlap between differentially expressed genes (DEGs) identified from GEO datasets and genes retrieved from the CellAge database. Five overlapping genes were identified: PTGS2, MMP9, SERPINB2, CTNNAL1, and HTRA1. CS, genes from CellAge database; DEG, significantly differentially expressed genes in ischemic stroke. (B) Gene Ontology (GO) enrichment analysis of the overlapping genes, including biological process, cellular component, and molecular function categories. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showing that the overlapping genes were enriched in inflammation-related pathways, including the IL-17 signaling pathway and TNF signaling pathway. (D) GSEA comparing ischemic stroke (IS) and control samples, demonstrating enrichment of immune- and inflammation-related pathways in IS. (E) Predicted transcription factor–target gene regulatory network for PTGS2 and MMP9, showing candidate shared upstream transcription factors. Yellow nodes represent TFs, red nodes represent target genes.
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Bioinformatics analysis prioritized PTGS2 and MMP9 as candidate inflammatory genes associated with <t>the</t> <t>IL-17</t> signaling pathway in ischemic stroke. (A) Venn diagram showing the overlap between differentially expressed genes (DEGs) identified from GEO datasets and genes retrieved from the CellAge database. Five overlapping genes were identified: PTGS2, MMP9, SERPINB2, CTNNAL1, and HTRA1. CS, genes from CellAge database; DEG, significantly differentially expressed genes in ischemic stroke. (B) Gene Ontology (GO) enrichment analysis of the overlapping genes, including biological process, cellular component, and molecular function categories. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showing that the overlapping genes were enriched in inflammation-related pathways, including the IL-17 signaling pathway and TNF signaling pathway. (D) GSEA comparing ischemic stroke (IS) and control samples, demonstrating enrichment of immune- and inflammation-related pathways in IS. (E) Predicted transcription factor–target gene regulatory network for PTGS2 and MMP9, showing candidate shared upstream transcription factors. Yellow nodes represent TFs, red nodes represent target genes.
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Bioinformatics analysis prioritized PTGS2 and MMP9 as candidate inflammatory genes associated with <t>the</t> <t>IL-17</t> signaling pathway in ischemic stroke. (A) Venn diagram showing the overlap between differentially expressed genes (DEGs) identified from GEO datasets and genes retrieved from the CellAge database. Five overlapping genes were identified: PTGS2, MMP9, SERPINB2, CTNNAL1, and HTRA1. CS, genes from CellAge database; DEG, significantly differentially expressed genes in ischemic stroke. (B) Gene Ontology (GO) enrichment analysis of the overlapping genes, including biological process, cellular component, and molecular function categories. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showing that the overlapping genes were enriched in inflammation-related pathways, including the IL-17 signaling pathway and TNF signaling pathway. (D) GSEA comparing ischemic stroke (IS) and control samples, demonstrating enrichment of immune- and inflammation-related pathways in IS. (E) Predicted transcription factor–target gene regulatory network for PTGS2 and MMP9, showing candidate shared upstream transcription factors. Yellow nodes represent TFs, red nodes represent target genes.
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Bioinformatics analysis prioritized PTGS2 and MMP9 as candidate inflammatory genes associated with <t>the</t> <t>IL-17</t> signaling pathway in ischemic stroke. (A) Venn diagram showing the overlap between differentially expressed genes (DEGs) identified from GEO datasets and genes retrieved from the CellAge database. Five overlapping genes were identified: PTGS2, MMP9, SERPINB2, CTNNAL1, and HTRA1. CS, genes from CellAge database; DEG, significantly differentially expressed genes in ischemic stroke. (B) Gene Ontology (GO) enrichment analysis of the overlapping genes, including biological process, cellular component, and molecular function categories. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showing that the overlapping genes were enriched in inflammation-related pathways, including the IL-17 signaling pathway and TNF signaling pathway. (D) GSEA comparing ischemic stroke (IS) and control samples, demonstrating enrichment of immune- and inflammation-related pathways in IS. (E) Predicted transcription factor–target gene regulatory network for PTGS2 and MMP9, showing candidate shared upstream transcription factors. Yellow nodes represent TFs, red nodes represent target genes.
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Bioinformatics analysis prioritized PTGS2 and MMP9 as candidate inflammatory genes associated with <t>the</t> <t>IL-17</t> signaling pathway in ischemic stroke. (A) Venn diagram showing the overlap between differentially expressed genes (DEGs) identified from GEO datasets and genes retrieved from the CellAge database. Five overlapping genes were identified: PTGS2, MMP9, SERPINB2, CTNNAL1, and HTRA1. CS, genes from CellAge database; DEG, significantly differentially expressed genes in ischemic stroke. (B) Gene Ontology (GO) enrichment analysis of the overlapping genes, including biological process, cellular component, and molecular function categories. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showing that the overlapping genes were enriched in inflammation-related pathways, including the IL-17 signaling pathway and TNF signaling pathway. (D) GSEA comparing ischemic stroke (IS) and control samples, demonstrating enrichment of immune- and inflammation-related pathways in IS. (E) Predicted transcription factor–target gene regulatory network for PTGS2 and MMP9, showing candidate shared upstream transcription factors. Yellow nodes represent TFs, red nodes represent target genes.
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Bioinformatics analysis prioritized PTGS2 and MMP9 as candidate inflammatory genes associated with <t>the</t> <t>IL-17</t> signaling pathway in ischemic stroke. (A) Venn diagram showing the overlap between differentially expressed genes (DEGs) identified from GEO datasets and genes retrieved from the CellAge database. Five overlapping genes were identified: PTGS2, MMP9, SERPINB2, CTNNAL1, and HTRA1. CS, genes from CellAge database; DEG, significantly differentially expressed genes in ischemic stroke. (B) Gene Ontology (GO) enrichment analysis of the overlapping genes, including biological process, cellular component, and molecular function categories. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showing that the overlapping genes were enriched in inflammation-related pathways, including the IL-17 signaling pathway and TNF signaling pathway. (D) GSEA comparing ischemic stroke (IS) and control samples, demonstrating enrichment of immune- and inflammation-related pathways in IS. (E) Predicted transcription factor–target gene regulatory network for PTGS2 and MMP9, showing candidate shared upstream transcription factors. Yellow nodes represent TFs, red nodes represent target genes.
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Image Search Results


Bioinformatics analysis prioritized PTGS2 and MMP9 as candidate inflammatory genes associated with the IL-17 signaling pathway in ischemic stroke. (A) Venn diagram showing the overlap between differentially expressed genes (DEGs) identified from GEO datasets and genes retrieved from the CellAge database. Five overlapping genes were identified: PTGS2, MMP9, SERPINB2, CTNNAL1, and HTRA1. CS, genes from CellAge database; DEG, significantly differentially expressed genes in ischemic stroke. (B) Gene Ontology (GO) enrichment analysis of the overlapping genes, including biological process, cellular component, and molecular function categories. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showing that the overlapping genes were enriched in inflammation-related pathways, including the IL-17 signaling pathway and TNF signaling pathway. (D) GSEA comparing ischemic stroke (IS) and control samples, demonstrating enrichment of immune- and inflammation-related pathways in IS. (E) Predicted transcription factor–target gene regulatory network for PTGS2 and MMP9, showing candidate shared upstream transcription factors. Yellow nodes represent TFs, red nodes represent target genes.

Journal: Frontiers in Immunology

Article Title: IL-17A-associated PTGS2 and MMP9 inflammatory signaling in ischemic stroke: clinical correlation and experimental evidence

doi: 10.3389/fimmu.2026.1812571

Figure Lengend Snippet: Bioinformatics analysis prioritized PTGS2 and MMP9 as candidate inflammatory genes associated with the IL-17 signaling pathway in ischemic stroke. (A) Venn diagram showing the overlap between differentially expressed genes (DEGs) identified from GEO datasets and genes retrieved from the CellAge database. Five overlapping genes were identified: PTGS2, MMP9, SERPINB2, CTNNAL1, and HTRA1. CS, genes from CellAge database; DEG, significantly differentially expressed genes in ischemic stroke. (B) Gene Ontology (GO) enrichment analysis of the overlapping genes, including biological process, cellular component, and molecular function categories. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showing that the overlapping genes were enriched in inflammation-related pathways, including the IL-17 signaling pathway and TNF signaling pathway. (D) GSEA comparing ischemic stroke (IS) and control samples, demonstrating enrichment of immune- and inflammation-related pathways in IS. (E) Predicted transcription factor–target gene regulatory network for PTGS2 and MMP9, showing candidate shared upstream transcription factors. Yellow nodes represent TFs, red nodes represent target genes.

Article Snippet: Rats in the IL-17A neutralizing antibody-treated group were treated with Anti-Mouse/Rat IL-17A Antibody (17F3; MedChemExpress, Cat. No. HY-P990222), a mouse IgG1κ monoclonal neutralizing antibody against mouse/rat IL-17A, via intravenous injection at 100 μg/rat immediately after reperfusion and again at 12 h post-reperfusion.

Techniques: Control

Serum IL-17A, PTGS2, and MMP9 levels in the clinical cohort and their correlations with each other and with stroke severity. (A–C) Serum IL-17A, PTGS2, and MMP9 levels were significantly higher in patients with ischemic stroke than in healthy controls. IS, ischemic stroke (n = 104); Normal, control group (n = 58). (D) Correlations among serum IL-17A, PTGS2, and MMP9 levels in the stroke group. (E) Positive correlations between serum IL-17A, PTGS2, and MMP9 levels and admission NIHSS scores in patients with ischemic stroke. Correlations were analyzed using Spearman’s rank correlation test. *P < 0.05, **P < 0.01, ***P < 0.001.

Journal: Frontiers in Immunology

Article Title: IL-17A-associated PTGS2 and MMP9 inflammatory signaling in ischemic stroke: clinical correlation and experimental evidence

doi: 10.3389/fimmu.2026.1812571

Figure Lengend Snippet: Serum IL-17A, PTGS2, and MMP9 levels in the clinical cohort and their correlations with each other and with stroke severity. (A–C) Serum IL-17A, PTGS2, and MMP9 levels were significantly higher in patients with ischemic stroke than in healthy controls. IS, ischemic stroke (n = 104); Normal, control group (n = 58). (D) Correlations among serum IL-17A, PTGS2, and MMP9 levels in the stroke group. (E) Positive correlations between serum IL-17A, PTGS2, and MMP9 levels and admission NIHSS scores in patients with ischemic stroke. Correlations were analyzed using Spearman’s rank correlation test. *P < 0.05, **P < 0.01, ***P < 0.001.

Article Snippet: Rats in the IL-17A neutralizing antibody-treated group were treated with Anti-Mouse/Rat IL-17A Antibody (17F3; MedChemExpress, Cat. No. HY-P990222), a mouse IgG1κ monoclonal neutralizing antibody against mouse/rat IL-17A, via intravenous injection at 100 μg/rat immediately after reperfusion and again at 12 h post-reperfusion.

Techniques: Control

MCAO-induced activation of IL-17A/PTGS2/MMP9-related inflammatory markers and changes after anti-IL-17A antibody treatment in rat brain tissue. (A–C) Relative mRNA levels of PTGS2, MMP9, and IL-17A in the sham, MCAO, and anti-IL-17A Ab groups. (D–F) Protein concentrations of PTGS2, MMP9, and IL-17A. (G, H) Concentrations of PGE2 and IL-6. (I) Neurological deficit scores assessed using the Longa scoring system. In panel I, the sham group median was 0; therefore, a minimal bar height was displayed for visualization only. (J) Representative TTC-stained brain sections from the MCAO and anti-IL-17A Ab groups. White areas indicate infarct regions, whereas red areas indicate viable brain tissue. (K) Quantitative analysis of infarct volume percentage based on TTC staining. Sham, sham-operated group; Model, MCAO group; anti-IL-17A Ab, IL-17A neutralizing antibody-treated MCAO group. Data in panels A-H are presented as mean ± SEM (n = 6 per group). Neurological deficit scores in panel I are presented as median (interquartile range) (n = 6 per group). TTC analysis in panels (J, K) was performed in an independent cohort of rats, with final sample sizes of n = 4 for the MCAO group and n = 5 for the anti-IL-17A Ab group; one rat in the MCAO group died before tissue collection. *P < 0.05, **P < 0.01, ***P < 0.001.

Journal: Frontiers in Immunology

Article Title: IL-17A-associated PTGS2 and MMP9 inflammatory signaling in ischemic stroke: clinical correlation and experimental evidence

doi: 10.3389/fimmu.2026.1812571

Figure Lengend Snippet: MCAO-induced activation of IL-17A/PTGS2/MMP9-related inflammatory markers and changes after anti-IL-17A antibody treatment in rat brain tissue. (A–C) Relative mRNA levels of PTGS2, MMP9, and IL-17A in the sham, MCAO, and anti-IL-17A Ab groups. (D–F) Protein concentrations of PTGS2, MMP9, and IL-17A. (G, H) Concentrations of PGE2 and IL-6. (I) Neurological deficit scores assessed using the Longa scoring system. In panel I, the sham group median was 0; therefore, a minimal bar height was displayed for visualization only. (J) Representative TTC-stained brain sections from the MCAO and anti-IL-17A Ab groups. White areas indicate infarct regions, whereas red areas indicate viable brain tissue. (K) Quantitative analysis of infarct volume percentage based on TTC staining. Sham, sham-operated group; Model, MCAO group; anti-IL-17A Ab, IL-17A neutralizing antibody-treated MCAO group. Data in panels A-H are presented as mean ± SEM (n = 6 per group). Neurological deficit scores in panel I are presented as median (interquartile range) (n = 6 per group). TTC analysis in panels (J, K) was performed in an independent cohort of rats, with final sample sizes of n = 4 for the MCAO group and n = 5 for the anti-IL-17A Ab group; one rat in the MCAO group died before tissue collection. *P < 0.05, **P < 0.01, ***P < 0.001.

Article Snippet: Rats in the IL-17A neutralizing antibody-treated group were treated with Anti-Mouse/Rat IL-17A Antibody (17F3; MedChemExpress, Cat. No. HY-P990222), a mouse IgG1κ monoclonal neutralizing antibody against mouse/rat IL-17A, via intravenous injection at 100 μg/rat immediately after reperfusion and again at 12 h post-reperfusion.

Techniques: Activation Assay, Staining

Spearman correlation analyses among IL-17A, PTGS2, MMP9, PGE2, and IL-6 in rat brain tissue. Correlation analyses were performed using pooled data from all animals (n = 18). (A) Correlation between IL-17A and PTGS2 mRNA expression. (B) Correlation between IL-17A and MMP9 mRNA expression. (C) Correlation between IL-17A and PTGS2 protein expression. (D) Correlation between IL-17A and MMP9 protein expression. (E) Correlation between IL-17A and PGE2 levels. (F) Correlation between IL-17A and IL-6 levels. Sham, sham-operated group; Model, MCAO group; anti-IL-17A Ab, IL-17A neutralizing antibody-treated MCAO group. Given the limited sample size, these correlations should be interpreted with caution.

Journal: Frontiers in Immunology

Article Title: IL-17A-associated PTGS2 and MMP9 inflammatory signaling in ischemic stroke: clinical correlation and experimental evidence

doi: 10.3389/fimmu.2026.1812571

Figure Lengend Snippet: Spearman correlation analyses among IL-17A, PTGS2, MMP9, PGE2, and IL-6 in rat brain tissue. Correlation analyses were performed using pooled data from all animals (n = 18). (A) Correlation between IL-17A and PTGS2 mRNA expression. (B) Correlation between IL-17A and MMP9 mRNA expression. (C) Correlation between IL-17A and PTGS2 protein expression. (D) Correlation between IL-17A and MMP9 protein expression. (E) Correlation between IL-17A and PGE2 levels. (F) Correlation between IL-17A and IL-6 levels. Sham, sham-operated group; Model, MCAO group; anti-IL-17A Ab, IL-17A neutralizing antibody-treated MCAO group. Given the limited sample size, these correlations should be interpreted with caution.

Article Snippet: Rats in the IL-17A neutralizing antibody-treated group were treated with Anti-Mouse/Rat IL-17A Antibody (17F3; MedChemExpress, Cat. No. HY-P990222), a mouse IgG1κ monoclonal neutralizing antibody against mouse/rat IL-17A, via intravenous injection at 100 μg/rat immediately after reperfusion and again at 12 h post-reperfusion.

Techniques: Expressing