Critical gene network and signaling pathway analysis of the extracellular signal-regulated kinase (ERK) pathway in ischemic stroke
Background and objectiveIschemic stroke remains a leading cause of morbidity worldwide, demanding reliable biomarkers and mechanistic insights to inform personalized diagnostic and therapeutic strategies. We sought to integrate multiple ischemic stroke transcriptomic datasets, identify key extracell...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Molecular Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnmol.2025.1604670/full |
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| Summary: | Background and objectiveIschemic stroke remains a leading cause of morbidity worldwide, demanding reliable biomarkers and mechanistic insights to inform personalized diagnostic and therapeutic strategies. We sought to integrate multiple ischemic stroke transcriptomic datasets, identify key extracellular signal-regulated kinase (ERK) pathway–related biomarkers, delineate immune–stromal heterogeneity, and develop a nomogram for clinical risk assessment.MethodsWe retrieved three public microarray datasets (GSE22255, GSE16561, GSE58294) and merged two of them (GSE22255, GSE16561) into a discovery cohort after stringent batch correction. Differential expression analyses were performed using the limma package in R, followed by weighted gene co-expression network analysis (WGCNA) to identify ERK-associated gene modules. Gene Ontology (GO) enrichment and protein–protein interaction (PPI) network analyses further elucidated the functional and interaction landscapes of the key ERK pathway genes, collectively termed GSERK. Subsequently, hub genes were prioritized using cytoHubba, and their diagnostic utility was validated by receiver operating characteristic (ROC) analyses in both discovery and validation cohorts. Four machine learning algorithms (Boruta, SVM, LASSO, random forest) corroborated hub gene robustness. Finally, we stratified ischemic stroke samples by immune–stromal profiling and constructed a GSERK-based nomogram to predict stroke risk.ResultsA total of 140 differentially expressed genes (DEGs) were identified, with the ERK-related subset (GSERK) highlighted for its pivotal roles in ischemic stroke pathogenesis. Five hub GSERK genes (GADD45A, DUSP1, IL1B, JUN, and GADD45B) emerged from cytoHubba. DUSP1, GADD45A, and GADD45B showed robust diagnostic accuracy (AUC: 0.75–0.91), confirmed across discovery and validation sets. Immune–stromal clustering revealed two distinct stroke subgroups with hyperinflammatory or quiescent stromal phenotypes. A GSERK-based nomogram demonstrated a strong bootstrap-validated C-index, underscoring its potential for clinical risk stratification.ConclusionThese findings affirm the significance of ERK signaling in ischemic stroke, unveil critical GSERK biomarkers with promising diagnostic and therapeutic implications, and present a novel GSERK-based nomogram for precision risk assessment. Further studies, including experimental validation and multi-center clinical trials, are warranted to refine this integrative approach toward personalized stroke care. |
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| ISSN: | 1662-5099 |