ArticleAging2024
Machine learning identifies novel coagulation genes as diagnostic and immunological biomarkers in ischemic stroke.
Article in Aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 4 citations in OpenAlex.
- Short report: Targeted analysis of whole exome sequencing data in Indian cryptogenic stroke patients.PloS one · 2026Article
- Identification of coagulation-related hub genes in ischemic stroke based on bioinformatics integration analysis and investigation of their immune regulatory mechanisms.European journal of medical research · 2025Article
- Influence of Stress-Induced Senescence on the Secretome of Primary Mesenchymal Stromal Cells.Biomolecules · 2025Article
- Bioinformatics analysis of genes associated with disulfidptosis in spinal cord injury.PloS one · 2025Article
- Zinc alleviates stroke development through autophagy-mediated modulation of immune microenvironment.Frontiers in immunology · 2025Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCoagulation system is currently known associated with the development of ischemic stroke (IS). Thus, the current study is designed to identify diagnostic value of coagulation genes (CGs) in IS and to explore their role in the immune microenvironment of IS.
methodsAberrant expressed CGs in IS were input into unsupervised consensus clustering to classify IS subtypes. Meanwhile, key CGs involved in IS were further selected by weighted gene co-expression network analysis (WGCNA) and machine learning methods, including random forest (RF), support vector machine (SVM), generalized linear model (GLM) and extreme-gradient boosting (XGB). The diagnostic performance of key CGs were evaluated by receiver operating characteristic (ROC) curves. At last, quantitative PCR (qPCR) was performed to validate the expressions of key CGs in IS.
resultsIS patients were classified into two subtypes with different immune microenvironments by aberrant expressed CGs. Further WGCNA, machine learning methods and ROC curves identified ACTN1, F5, TLN1, JMJD1C and WAS as potential diagnostic biomarkers of IS. In addition, their expressions were significantly correlated with macrophages, neutrophils and/or T cells. GSEA also revealed that those biomarkers may regulate IS via immune and inflammation. Moreover, qPCR verified the expressions of ACTN1, F5 and JMJD1C in IS.
conclusionsThe current study identified ACTN1, F5 and JMJD1C as novel coagulation-related biomarkers associated with IS immune microenvironment, which enriches our knowledge of coagulation-mediated pathogenesis of IS and sheds light on next-step
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