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

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.

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Publication Details

Journal
Scientific Reports
Published
2025-04-22
DOI
https://doi.org/10.1038/s41598-025-98101-0
Citations
2
Primary Topic
Cancer-related molecular mechanisms research
Type
article
Field-Weighted Citation Impact
1.87

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article

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

Hongxiang Jiang, Ping Song, Min Hu, Peidong He et al.
2 citations
Scientific Reports
Cancer-related molecular mechanisms research
1.87
article

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

Hongxiang Jiang, Ping Song, Min Hu, Peidong He, Jiangrui Zhu
article en
2 citations

Abstract

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.

Scientific ReportsVol. 15(1)
Wuhan University (CN), Renmin Hospital of Wuhan University (CN)
Natural Science Foundation of Hubei Province, Wuhan University, Peking University, Peking University Third Hospital
Good health and well-being
Openalex Percentile: Top 15%
Cancer-related molecular mechanisms research
1.87
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