Transcriptomics in ischemic stroke： A new perspective from differentially expressed genes to therapeutic targets
10.19845/j.cnki.zfysjjbzz.2025.0186
- VernacularTitle:缺血性脑卒中转录组学揭示：从差异表达基因到治疗靶点的新视角
- Author:
Junchi YANG
1
;
Xinzhe TAN
1
Author Information
1. Heilongjiang University of Chinese Medicine， Harbin 150040， China
- Publication Type:Journal Article
- Keywords:
Ischemic stroke;
Machine learning;
Mendelian randomization;
RPL22L1
- From:
Journal of Apoplexy and Nervous Diseases
2025;42(11):1017-1023
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the potential pathogenic genes of ischemic stroke（IS） based on transcriptomics/bioinformatics analyses and validation， and to perfect the molecular pathogenesis of IS. Methods The peripheral whole blood gene expression microarray dataset GSE58294 for human IS was downloaded from GEO database， and R software was used to analyze the differentially expressed genes（DEGs）. The STRING program in R software was used to construct a protein-protein interaction（PPI） network and identify Hub genes. Three high-confidence machine learning algorithms（LASSO， SVM-RFE， and RF） and eQTL Mendelian randomization were used to obtain the core feature genes of IS， and box plots of differentially expressed genes， the receiver operating characteristic curve analysis， and single-gene Mendelian randomization further validated their association with IS. Finally， gene ontology （GO）/Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analyses， Gene Set Enrichment Analysis （GSEA）， and immune infiltration analysis （CIBERSORT） were performed for the core features genes of IS to explore the molecular mechanism of their association with IS. Results A total of 183 DEGs were identified by the differential expression analysis of the IS dataset， and Mendelian randomization and machine learning algorithms finally obtained and verified one key IS pathogenic gene RPL22L1. The GO/KEGG analysis showed that this gene was mainly involved in ribosomal and cytoplasmic translation， the GSEA analysis showed that it was most closely associated with AMP metabolism， cell motility， and fat metabolism， and the CIBERSORT analysis showed that RPL22L1 expression significantly inhibited infantile CD4+ T cell infiltration. Conclusion RPL22L1 IS a key pathogenic gene for IS， and it may promote the onset of IS by influencing cytoplasmic translation， AMP metabolism， and cellular immunity， which provides a new direction for the clinical diagnosis and treatment of IS.
