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Updated: Sep 6, 2025

A Mouse Model of Hemorrhagic Transformation Induced by Acute Hyperglycemia Combined with Transient Focal Ischemia
Published on: November 15, 2024
Differential Expression and Correlation Analysis of Global Transcriptome for Hemorrhagic Transformation After Acute
Rongrong Han1, Peng Zhang1, Hongfang Li2
1Department of Clinical Medicine, Jining Medical University, Jining, China.
Abstract:
In order to explore the epigenetic characteristics of hemorrhagic transformation (HT) after acute ischemic stroke, we used transcriptome sequencing technology to analyze the global transcriptome expression profile of patients with and without HT after acute ischemic stroke and to study the differential expression of messenger RNA (mRNA), long noncoding RNA (lncRNA), circular RNA (circRNA) and mircoRNA (miRNA) between the two groups. To further explore the role of differentially expressed genes in HT, we annotated the function of differentially expressed genes by using gene ontology (GO) and pathway analysis on the results and showed that there were 1,051 differential expressions of lncRNAs, 2,575 differential expressions of mRNAs, 447 differential expressions of circRNAs and 47 miRNAs in patients with HT compared with non-HT patients. Pathway analysis showed that ubiquitin-mediated proteolysis, MAPK signal pathway, axon guidance, HIF-1 signal pathway, NOD-like receptor signal pathway, beta-alanine metabolism, Wnt signal pathway, sphingolipid signal pathway, neuroactive ligand-receptor interaction, and intestinal immune network used in IgA production play an important role in HT. Terms such as iron homeostasis, defense response, immune system process, DNA conformational change, production of transforming growth factor beta-2, and oxidoreductase activity were enriched in the gene list, suggesting a potential correlation with HT. A total of 261 lncRNA-miRNA relationship pairs and 21 circRNA-miRNA relationship pairs were obtained; additionally, 5 circRNAs and 13 lncRNAs were screened, which can be used as competing endogenous RNA (ceRNA) to compete with miRNA in the co-expression network. Co-expression network analysis shows that these differentially expressed circRNA and lncRNA may play a vital role in HT and provide valuable information for new biomarkers or therapeutic targets.

