ArticleScientific reports2025
Identification of post-translational modification-related biomarkers in ischemic stroke using bioinformatics and machine learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Advances in genetics and multi-omics for ischemic stroke: from pathogenesis to clinical translation.Frontiers in genetics · 2026Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ischemic stroke (IS) remains a major clinical challenge due to the difficulty of early diagnosis and incomplete understanding of its pathological mechanisms. Post-translational modifications (PTMs) regulate key cellular processes in IS, but their roles as diagnostic biomarkers and therapeutic targets have not been fully elucidated. This study aimed to identify PTM-related genes (PTMRGs) associated with IS and evaluate their diagnostic and therapeutic potential using comprehensive bioinformatics and machine learning methods. Gene expression data from two GEO cohorts (GSE16561, training group n = 63; GSE58294, testing group n = 92) were analyzed. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and intersection with known PTMRGs were performed to screen candidate genes. Protein-protein interaction (PPI) networks and machine learning algorithms (Boruta, SVM-RFE, LASSO) were used to prioritize biomarkers. The expression of key genes was validated in clinical samples by RT-qPCR. An artificial neural network (ANN) was constructed to evaluate diagnostic performance. In addition, immune infiltration, gene set enrichment analysis (GSEA), gene-gene interaction (GGI), and molecular docking analyses were conducted to explore biological functions and therapeutic candidates. A total of 1465 upregulated genes and 1782 downregulated genes were identified. WGCNA revealed modules significantly associated with IS, yielding 75 key PTMRGs identified after intersection with DEGs and PTMRGs. Six genes (ATG7, KAT2A, RNF20, UBA1, UBE2I, and USP15) were identified as diagnostic markers with AUC > 0.7. RT-qPCR in 10 IS patients and 10 controls confirmed differential expression, consistent with bioinformatics results. The ANN model showed high diagnostic accuracy (AUC = 0.983 in training, 0.95 in testing). Functional enrichment linked these genes to ubiquitin-mediated proteolysis, DNA repair, and Myc signaling. Immune analysis showed associations with CD8
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