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. 2025 Apr 29;75(2):60.
doi: 10.1007/s12031-025-02352-5.

Identification and Validation of Glycosylation‑Related Genes in Ischemic Stroke Based on Bioinformatics and Machine Learning

Hui Zhang #  1 , Yanan Ji #  1 , Zhongquan Yi  1 , Jing Zhao  1 , Jianping Liu  2 , Xianxian Zhang  3
Affiliations

Identification and Validation of Glycosylation‑Related Genes in Ischemic Stroke Based on Bioinformatics and Machine Learning

Hui Zhang et al. J Mol Neurosci. .

Abstract

Ischemic stroke (IS) constitutes a severe neurological disorder with restricted treatment alternatives. Recent investigations have disclosed that glycosylation is closely associated with the occurrence and outcome of IS. Nevertheless, data on the transcriptomic dynamics of glycosylation in IS are lacking. The objective of this study was to undertake a comprehensive exploration of glycosylation-related genes (GRGs) in IS via bioinformatics and to assess their immune characteristics. In this study, through the intersection of genes from weighted gene co-expression network analysis, GRGs from five glycosylation pathways, and DEGs from differential expression analysis, 20 candidate GRGs were identified. Subsequently, through LASSO, Random Forest, and SVM-RFE, 3 hub GRGs (F5, PPP6C, and UBE2J1) were identified. Additional, a gene diagnostic model linked to glycosylation was developed and validated. The findings indicated that the diagnostic model could effectively distinguish between IS patients and healthy individuals in the training, validation, and merging datasets, indicating clinical relevance. Subsequently, by employing unsupervised clustering analysis, IS patients were classified into three clusters, and significant disparities were witnessed in immune cell infiltration among distinct clusters. In summary, this study successfully identified hub GRGs in IS and investigated the roles of these hub genes in the immune microenvironment, indicating potential clinical applications for IS.

Keywords: Glycosylation; Hub gene; Immune cell infiltration; Ischemic Stroke; Machine learning.

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Conflict of interest statement

Declarations. Ethics Approval and Consent to Participate: The studies involving humans were approved by The Medical Ethics Committee of the Sixth Affiliated Hospital of Nantong University with number 2023–57. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. The manuscript presents research on animals that do not require ethical approval for their study. Conflict of Interest: The authors declare no competing interests.

References

    1. Bai J, Lyden PD (2015) Revisiting cerebral postischemic reperfusion injury: new insights in understanding reperfusion failure, hemorrhage, and edema. Int J Stroke 10(2):143–152. https://doi.org/10.1111/ijs.12434 - DOI - PubMed
    1. Barr TL, Conley Y, Ding J, Dillman A, Warach S, Singleton A et al (2010) Genomic biomarkers and cellular pathways of ischemic stroke by RNA gene expression profiling. Neurology 75(11):1009–1014. https://doi.org/10.1212/WNL.0b013e3181f2b37f - DOI - PubMed - PMC
    1. Bullimore MA, Ritchey ER, Shah S, Leveziel N, Bourne RRA, Flitcroft DI (2021) The risks and benefits of myopia control. Ophthalmology 128(11):1561–1579. https://doi.org/10.1016/j.ophtha.2021.04.032 - DOI - PubMed
    1. Bynigeri RR, Malireddi RKS, Mall R, Connelly JP, Pruett-Miller SM, Kanneganti TD (2024) The protein phosphatase PP6 promotes RIPK1-dependent PANoptosis. BMC Biol 22(1):122. https://doi.org/10.1186/s12915-024-01901-5 - DOI - PubMed - PMC
    1. Chang CH, Lin CH, Lane HY (2021) Machine learning and novel biomarkers for the diagnosis of Alzheimer’s disease. Int J Mol Sci 22(5):2761. https://doi.org/10.3390/ijms2205276 - DOI - PubMed - PMC

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