Bioinformatics method combined with logistic regression analysis reveal potentially important miRNAs in ischemic stroke. Issue 8 (17th August 2020)

• Record Type: Journal Article
• Authors: Wei, Zhiqiang; Qi, Xingdi; Chen, Yan; Xia, Xiaoshuang; Zheng, Boyu; Sun, Xugang; Zhang, Guangming; Wang, Ling; Zhang, Qi; Xu, Chen; Jiang, Shihe; Li, Xiulian; Xie, Bingxin; Liao, Xiaohui; Zhu, Ai
• Is Part Of: Bioscience reports. Volume 40:Issue 8(2020)
• Publication Date: 2020-08-17
• Subjects: ischemic stroke -- machine learning algorithms -- miRNAs; Molecular biology -- Periodicals; Cytology -- Periodicals; 572.8
• DOI: 10.1042/BSR20201154
• Languages: English
• ISSNs: 0144-8463

Abstract

Abstract: Purpose: The present study aimed to investigate the comprehensive differential expression profile of microRNAs (miRNAs) by screening for miRNA expression in ischemic stroke and normal samples. Methods: Differentially expressed miRNA (DEM) analysis was conducted using limma R Bioconductor package. Target genes of DEMs were identified from TargetScanHuman and miRTarBase databases. Functional enrichment analysis of the target genes was performed using clusterProfiler R Bioconductor package. The miRNA-based ischemic stroke diagnostic signature was constructed via logistic regression analysis. Results: Compared with the normal cohort, a total of 14 DEMs, including 5 up-regulated miRNAs and 9 down-regulated miRNAs, were identified in ischemic stroke patients. These DEMs have 1600 regulatory targets. Using a logistic regression model, the top five miRNAs were screened for constructing an miRNA-based ischemic stroke diagnostic signature. Using the miRNA–mRNA interaction pairs, two target genes (specificity protein 1 ( SP1 ) and Argonaute 1 ( AGO1 )) were speculated to be the primary genes of ischemic stroke. Discussion and conclusion: Here, several potential miRNAs biomarkers were identified and an miRNA-based diagnostic signature for ischemic stroke was established, which can be a valuable reference for future clinical researches.