Identification of a potential miRNA-mRNA regulatory network for ischemic stroke by using bioinformatics methods: a retrospective study based on the Gene Expression Omnibus database.

Background: Ischemic stroke (IS), a leading cause of disability and death worldwide, lacks effective biomarkers for early diagnosis and therapeutic intervention. This study aims to explore the potential miRNA-mRNA regulatory network in IS using clinical samples and bioinformatics methods, providing insights into its pathophysiology and identifying novel biomarkers.

Methods: We analyzed plasma samples from IS patients and controls collected at Ningbo No. 2 Hospital between May 2022 and February 2023, alongside data from the Gene Expression Omnibus (GEO) database. Bioinformatics analyses, including differential expression analysis and machine learning algorithms, were employed to identify key miRNAs and their target mRNAs. The findings were validated using four-dimensional data-independent acquisition (4D-DIA) quantitative proteomics.

Results: Our analysis revealed differentially expressed miRNAs and mRNAs in IS patients compared to controls. We constructed a potential miRNA-mRNA regulatory network and confirmed the differential expression of proteins associated with this network by proteomic validation, suggesting that they play a role in IS pathophysiology. The results of data analysis and clinical sample validation emphasized Integrin alpha M (ITGAM) as a key gene associated with IS. In addition, ROC curve analysis reflected the good performance of ITGAM as a potential biomarker for the diagnosis of IS and for differentiating between early- and late-onset stroke. The area under curve (AUC) of ITGAM in diagnosing IS was 0.750, and the AUC of ITGAM in distinguishing early-onset stroke from late-onset stroke was 0.759, with a sensitivity of 93.8%.

Conclusion: This study identifies a novel miRNA-mRNA regulatory network in IS, offering potential biomarkers for diagnosis and targets for therapeutic intervention. Our findings bridge the gap between clinical observations and molecular mechanisms, paving the way for improved IS management.