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<p>Ischemic stroke is one of the leading causes of deaths and disability, which is linked to inflammation. In this study, we aimed to identify inflammation-related lncRNAs as diagnostic biomarkers of acute ischemic stroke (AIS). A competing endogenous RNAs (ceRNA) network was established through whole transcriptome analysis. Gene expression datasets from the GEO database were analyzed to identify differentially expressed genes (DEGs), miRNAs and lncRNAs. Inflammation-related DEGs were determined through the intersection of the DEGs of the inflammation-related gene set from Genecards. Multiple databases like lncBase and Targetscan were analyzed to establish a ceRNA network. Several hub genes and sub-networks were obtained from a protein to protein (PPI) network. In addition, the candidate lncRNAs derived from the subnetwork were validated using mice MCAO model and clinical samples. Finally, a network comprising 20 lncRNAs, 26 miRNAs, and 43 inflammatory genes was analyzed, leading to the identification of MALAT1, SNHG8, and GAS5 as potential diagnostic biomarkers. Knockdown of MALAT1 and GAS5 resulted in decreased neurological severity score and inflammation response in mice MCAO model, indicating that these genes were significant diagnostic biomarkers for distinguishing AIS from healthy controls. These findings show that circulating MALAT1 and GAS5 have the potential to serve as clinical diagnostic biomarkers of AIS associated with inflammation.</p>

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Identification and validation of the inflammatory response-related LncRNAs as diagnostic biomarkers for acute ischemic stroke

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月22日

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  • Peidong He,
  • Hongxiang Jiang,
  • Jiangrui Zhu,
  • Min Hu,
  • Ping Song

摘要

Ischemic stroke is one of the leading causes of deaths and disability, which is linked to inflammation. In this study, we aimed to identify inflammation-related lncRNAs as diagnostic biomarkers of acute ischemic stroke (AIS). A competing endogenous RNAs (ceRNA) network was established through whole transcriptome analysis. Gene expression datasets from the GEO database were analyzed to identify differentially expressed genes (DEGs), miRNAs and lncRNAs. Inflammation-related DEGs were determined through the intersection of the DEGs of the inflammation-related gene set from Genecards. Multiple databases like lncBase and Targetscan were analyzed to establish a ceRNA network. Several hub genes and sub-networks were obtained from a protein to protein (PPI) network. In addition, the candidate lncRNAs derived from the subnetwork were validated using mice MCAO model and clinical samples. Finally, a network comprising 20 lncRNAs, 26 miRNAs, and 43 inflammatory genes was analyzed, leading to the identification of MALAT1, SNHG8, and GAS5 as potential diagnostic biomarkers. Knockdown of MALAT1 and GAS5 resulted in decreased neurological severity score and inflammation response in mice MCAO model, indicating that these genes were significant diagnostic biomarkers for distinguishing AIS from healthy controls. These findings show that circulating MALAT1 and GAS5 have the potential to serve as clinical diagnostic biomarkers of AIS associated with inflammation.