A panel of two miRNAs correlated to systolic blood pressure is a good diagnostic indicator for stroke

Qi, Yujun; Yuan, Mingfeng; Yi, Qiong; Wang, Yan; Xu, Lei; Xu, Changshui; Lü, Min

doi:10.1042/bsr20203458

Cited by 3 publications

(3 citation statements)

References 45 publications

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“…It is a well-known fact that, after an ischemic stroke, inflammation causes neuronal cell death and brain damage. The future potential of ischemic stroke treatment may be associated with therapeutic approaches that target the inflammasome activity in neurons [ 30 , 31 ]. The PPI networks were identified among the overlapping DEGs, and an effective network module was identified.…”

Section: Discussionmentioning

confidence: 99%

Integrated Microarray Analysis to Identify Genes and Small-Molecule Drugs Associated with Stroke Progression

Cui¹,

Zhao

Huang

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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Several blood biomarkers are now considered increasingly important for stratifying risk, monitoring disease progression, and evaluating the response to therapy in ischemic stroke. The purpose of the present study was to identify the key genes associated with ischemic stroke progression and elucidate the potential therapeutic small molecules. Microarray datasets related to stroke for GSE58294, GSE22255, and GSE16561 were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were filtered using the Limma package. DAVID was then searched to perform gene ontology (GO) and pathway enrichment analyses. Based on the DEGs, a protein-protein interaction (PPI) network was developed using Cytoscape, and MCODE was applied to conduct module analysis. Finally, to identify the potential drugs for ischemic stroke, the connectivity map (CMap) database was used. Sixty DEGs were identified after analyzing the three datasets. The GO data analysis revealed that the DEGs were significantly associated with biological processes, including positive regulation of programmed cell death, protein localization in organelles, and positive regulation of apoptosis. KEGG analysis showed that the DEGs were particularly enriched in the Fc epsilon RI signaling pathway, MAPK signaling pathway, and Huntington’s disease. We selected five DEGs with high connectivity (CYBB, SYK, DUSP1, TNF, and SP1) that significantly predicted stroke progression. In addition, CMap prediction showed ten small molecules that could be used as adjuvants when treating ischemic stroke. The outcomes of the present study indicated that the five genes mentioned above can be considered potential targets for developing new medications that can modify the ischemic stroke process, and mycophenolic acid was the most promising small molecule to treat ischemic stroke.

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Section: Discussionmentioning

confidence: 99%

Integrated Microarray Analysis to Identify Genes and Small-Molecule Drugs Associated with Stroke Progression

Cui¹,

Zhao

Huang

et al. 2022

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

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“…The module eigengene values were determined using the “moduleEigengenes” algorithm, and the gene expression profiles of specific modules were summarized. Hierarchical clustering and Spearman correlation analyses were performed between the module eigengene values of modules and the clinical characteristics of samples, P values < 0.05 and correlation coefficient ( r ) > 0.3 are statistically significant [ 14 ], and these modules were identified as COVID-19- or stroke-related crucial modules.…”

Section: Methodsmentioning

confidence: 99%

Weighted Gene Co-Expression Network Analysis to Identify Potential Biological Processes and Key Genes in COVID-19-Related Stroke

Cen

Liu

Wang

et al. 2022

Oxidative Medicine and Cellular Longevity

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The purpose of this research was to explore the underlying biological processes causing coronavirus disease 2019- (COVID-19-) related stroke. The Gene Expression Omnibus (GEO) database was utilized to obtain four COVID-19 datasets and two stroke datasets. Thereafter, we identified key modules via weighted gene co-expression network analysis, following which COVID-19- and stroke-related crucial modules were crossed to identify the common genes of COVID-19-related stroke. The common genes were intersected with the stroke-related hub genes screened via Cytoscape software to discover the critical genes associated with COVID-19-related stroke. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for common genes associated with COVID-19-related stroke, and the Reactome database was used to annotate and visualize the pathways involved in the key genes. Two COVID-19-related crucial modules and one stroke-related crucial module were identified. Subsequently, the top five genes were screened as hub genes after visualizing the genes of stroke-related critical module using Cytoscape. By intersecting the COVID-19- and stroke-related crucial modules, 28 common genes for COVID-19-related stroke were identified. ITGA2B and ITGB3 have been further identified as crucial genes of COVID-19-related stroke. Functional enrichment analysis indicated that both ITGA2B and ITGB3 were involved in integrin signaling and the response to elevated platelet cytosolic Ca2+, thus regulating platelet activation, extracellular matrix- (ECM-) receptor interaction, the PI3K-Akt signaling pathway, and hematopoietic cell lineage. Therefore, platelet activation, ECM-receptor interaction, PI3K-Akt signaling pathway, and hematopoietic cell lineage may represent the potential biological processes associated with COVID-19-related stroke, and ITGA2B and ITGB3 may be potential intervention targets for COVID-19-related stroke.

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“…Hierarchical clustering and Spearman correlation analysis were performed between the Eigengene value of the module and the clinical characteristics of the samples. P < 0.05 and correlation coefficient (r) > 0.25 were statistically significant 18 , and these modules were identified as aortic dissection- or metabolic syndrome-related key modules and selected for further analysis. To analyze the biological functions and pathways involved in these positively or negatively related key modules.…”

Section: Methodsmentioning

confidence: 99%

Identification of co-diagnostic effect genes for aortic dissection and metabolic syndrome by multiple machine learning algorithms

Zhang,

Li,

Chen

et al. 2023

Sci Rep

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Aortic dissection (AD) is a life-threatening condition in which the inner layer of the aorta tears. It has been reported that metabolic syndrome (MS) has a close linkage with aortic dissection. However, the inter-relational mechanisms between them were still unclear. This article explored the hub gene signatures and potential molecular mechanisms in AD and MS. We obtained five bulk RNA-seq datasets of AD, one single cell RNA-seq (scRNA-seq) dataset of ascending thoracic aortic aneurysm (ATAA), and one bulk RNA-seq dataset of MS from the gene expression omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and key modules via weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, and machine learning algorithms (Random Forest and LASSO regression) were used to identify hub genes for diagnosing AD with MS. XGBoost further improved the diagnostic performance of the model. The receiver operating characteristic (ROC) and precision-recall (PR) curves were developed to assess the diagnostic value. Then, immune cell infiltration and metabolism-associated pathways analyses were created to investigate immune cell and metabolism-associated pathway dysregulation in AD and MS. Finally, the scRNA-seq dataset was performed to confirm the expression levels of identified hub genes. 406 common DEGs were identified between the merged AD and MS datasets. Functional enrichment analysis revealed these DEGs were enriched for applicable terms of metabolism, cellular processes, organismal systems, and human diseases. Besides, the positively related key modules of AD and MS were mainly enriched in transcription factor binding and inflammatory response. In contrast, the negatively related modules were significantly associated with adaptive immune response and regulation of nuclease activity. Through machine learning, nine genes with common diagnostic effects were found in AD and MS, including MAD2L2, IMP4, PRPF4, CHSY1, SLC20A1, SLC9A1, TIPRL, DPYD, and MAPKAPK2. In the training set, the AUC of the hub gene on RP and RR curves was 1. In the AD verification set, the AUC of the Hub gene on RP and RR curves were 0.946 and 0.955, respectively. In the MS set, the AUC of the Hub gene on RP and RR curves were 0.978 and 0.98, respectively. scRNA-seq analysis revealed that the SLC20A1 was found to be relevant in fatty acid metabolic pathways and expressed in endothelial cells. Our study revealed the common pathogenesis of AD and MS. These common pathways and hub genes might provide new ideas for further mechanism research.

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A panel of two miRNAs correlated to systolic blood pressure is a good diagnostic indicator for stroke

Cited by 3 publications

References 45 publications

Integrated Microarray Analysis to Identify Genes and Small-Molecule Drugs Associated with Stroke Progression

Integrated Microarray Analysis to Identify Genes and Small-Molecule Drugs Associated with Stroke Progression

Weighted Gene Co-Expression Network Analysis to Identify Potential Biological Processes and Key Genes in COVID-19-Related Stroke

Identification of co-diagnostic effect genes for aortic dissection and metabolic syndrome by multiple machine learning algorithms

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