Peripheral Blood Aging-Related Gene Signatures Enable Temporal Stage Discrimination in Intracerebral Hemorrhage: An Integrative Analysis with Exploratory m6A Profiling of IRS1

Abstract

Intracerebral hemorrhage (ICH) is a life-threatening neurological emergency with disproportionately high mortality and disability rates, particularly among elderly individuals. Aging is the most critical non-modifiable risk factor for ICH, yet the transcriptomic changes associated with aging during ICH temporal progression remain poorly characterized. Peripheral blood transcriptomics offers a non-invasive window into the systemic molecular response following hemorrhagic brain injury; however, no study has systematically interrogated the aging-related transcriptomic landscape across different post-ICH stages at the peripheral blood level. We retrieved the peripheral blood gene expression dataset GSE125512 from the Gene Expression Omnibus (GEO) database, comprising samples from 22 ICH patients collected at 24 hours and 72 hours post-onset. Differential expression analysis was performed using DESeq2, and the resulting differentially expressed genes (DEGs) were intersected with aging-related genes (ARGs) curated from the Human Ageing Genomic Resources (HAGR) GenAge database. The aging-related DEGs were subjected to Gene Ontology (GO) and KEGG pathway enrichment analyses, protein-protein interaction (PPI) network construction, and logistic regressionbased modeling. Cross-species validation was performed using a murine ICH single-cell RNA sequencing dataset (GSE167593). A total of 218 DEGs were identified between the 24-hour and 72-hour post-ICH groups, comprising 178 upregulated and 40 downregulated genes. Intersection with 109 ARGs yielded 6 aging-related hub genes: CD24, EGF, ICAM1, IRS1, MYB, and VEGFA. Functional enrichment analysis revealed significant enrichment in pathways related to neuroinflammation, PI3K-AKT signaling, and cellular senescence. A machine learning-based temporal unprogression model incorporating the 6 hub genes achieved an area under the receiver operating characteristic curve (AUC) of 0.70, with IRS1 and ICAM1 identified as the highest-weighted stage-discriminative features. Five of the 6 hub genes (EGF, ICAM1, IRS1, MYB, and VEGFA) demonstrated conserved expression in the murine ICH single-cell dataset, with CD24 showing species-specific absence potentially attributable to ortholog nomenclature differences or low cell-type-specific expression. This study presents a systematic characterization of aging-related transcriptomic dysregulation in ICH and identifies a 6-gene peripheral blood signature associated with temporal stage discrimination. The identified hub genes—particularly CD24, ICAM1, and VEGFA—show a potential regulatory association with neuroinflammation, blood-brain barrier disruption, and vascular remodeling pathways altered during ICH progression in the context of aging. This integrative bioinformatics and machine learning framework may provide a useful reference for aging-related biomarker discovery in hemorrhagic stroke and related neurological disorders.