Myocardial infarction unveiled: Key miRNA players screened by a novel lncRNA-miRNA-mRNA network model

文献类型： 外文期刊

第一作者： Zhan, Chaoying

作者： Zhan, Chaoying;Zhang, Yuxin;Zhang, Yingbo;He, Mengqiao;Wu, Rongrong;Bi, Cheng;Shen, Bairong;Zhan, Chaoying;Zhang, Yuxin;Zhang, Yingbo;He, Mengqiao;Wu, Rongrong;Bi, Cheng;Shen, Bairong;Liu, Kai;Zhang, Yingbo

作者机构：

关键词： Myocardial infarction; Disease -related miRNA discovery; Network characteristics; lncRNA-miRNA-mRNA network; Multi-omics analysis

期刊名称：COMPUTERS IN BIOLOGY AND MEDICINE （ 影响因子：7.7； 五年影响因子：6.9 ）

ISSN： 0010-4825

年卷期： 2023 年 160 卷

页码：

收录情况： SCI

摘要： Background: Myocardial infarction (MI) is a major contributor to global mortality, and microRNAs (miRNAs) are important in its pathogenesis. Identifying blood miRNAs with clinical application potential for the early detec-tion and treatment of MI is crucial. Methods: We obtained MI-related miRNA and miRNA microarray datasets from MI Knowledge Base (MIKB) and Gene Expression Omnibus (GEO), respectively. A new feature called target regulatory score (TRS) was proposed to characterize the RNA interaction network. MI-related miRNAs were characterized using TRS, transcription factor (TF) gene proportion (TFP), and ageing-related gene (AG) proportion (AGP) via the lncRNA-miRNA-mRNA network. A bioinformatics model was then developed to predict MI-related miRNAs, which were verified by literature and pathway enrichment analysis.Results: The TRS-characterized model outperformed previous methods in identifying MI-related miRNAs. MI -related miRNAs had high TRS, TFP, and AGP values, and combining the three features improved prediction accuracy to 0.743. With this method, 31 candidate MI-related miRNAs were screened from the specific-MI lncRNA-miRNA-mRNA network, associated with key MI pathways like circulatory system processes, inflamma-tory response, and oxygen level adaptation. Most candidate miRNAs were directly associated with MI according to literature evidence, except hsa-miR-520c-3p and hsa-miR-190b-5p. Furthermore, CAV1, PPARA and VEGFA were identified as MI key genes, and were targeted by most of the candidate miRNAs.Conclusions: This study proposed a novel bioinformatics model based on multivariate biomolecular network analysis to identify putative key miRNAs of MI, which deserve further experimental and clinical validation for translational applications.

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