Bioinformatics strategies to identify differences in molecular biomarkers for ischemic stroke and myocardial infarction.

Ischemic strokes (ISs) are commonly treated by intravenous thrombolysis using a recombinant tissue plasminogen activator; however, successful treatment can only occur within 3 hours after the stroke. Therefore, it is crucial to determine the causes and underlying molecular mechanisms, identify molecular biomarkers for early diagnosis, and develop precise preventive treatments for strokes. We aimed to clarify the differences in gene expression, molecular mechanisms, and drug prediction approaches between IS and myocardial infarction (MI) using comprehensive bioinformatics analysis. The pathogenesis of these diseases was explored to provide directions for future clinical research. The IS (GSE58294 and GSE16561) and MI (GSE60993 and GSE141512) datasets were downloaded from the Gene Expression Omnibus database. IS and MI transcriptome data were analyzed using bioinformatics methods, and the differentially expressed genes (DEGs) were screened. A protein-protein interaction network was constructed using the STRING database and visualized using Cytoscape, and the candidate genes with high confidence scores were identified using Degree, MCC, EPC, and DMNC in the cytoHubba plug-in. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed using the database annotation, visualization, and integrated discovery database. Network Analyst 3.0 was used to construct transcription factor (TF) - gene and microRNA (miRNA) - gene regulatory networks of the identified candidate genes. The DrugBank 5.0 database was used to identify gene-drug interactions. After bioinformatics analysis of IS and MI microarray data, 115 and 44 DEGS were obtained in IS and MI, respectively. Moreover, 8 hub genes, 2 miRNAs, and 3 TFs for IS and 8 hub genes, 13 miRNAs, and 2 TFs for MI were screened. The molecular pathology between IS and MI presented differences in terms of GO and KEGG enrichment pathways, TFs, miRNAs, and drugs. These findings provide possible directions for the diagnosis of IS and MI in the future.

Wang M ，Gao Y ，Chen H ，Shen Y ，Cheng J ，Wang G ... -  《-》

Identification of core genes shared by ischemic stroke and myocardial infarction using an integrated approach.

Both ischemic stroke (IS) and myocardial infarction (MI) are caused by vascular occlusion that results in ischemia. While there may be similarities in their mechanisms, the potential relationship between these 2 diseases has not been comprehensively analyzed. Therefore, this study explored the commonalities in the pathogenesis of IS and MI. Datasets for IS (GSE58294, GSE16561) and MI (GSE60993, GSE61144) were downloaded from the Gene Expression Omnibus database. Transcriptome data from each of the 4 datasets were analyzed using bioinformatics, and the differentially expressed genes (DEGs) shared between IS and MI were identified and subsequently visualized using a Venn diagram. A protein-protein interaction (PPI) network was constructed using the Interacting Gene Retrieval Tool database, and identification of key core genes was performed using CytoHubba. Gene Ontology (GO) term annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the shared DEGs were conducted using prediction and network analysis methods, and the functions of the hub genes were determined using Metascape. The analysis revealed 116 and 1321 DEGs in the IS and MI datasets, respectively. Of the 75 DEGs shared between IS and MI, 56 were upregulated and 19 were downregulated. Furthermore, 15 core genes - S100a12, Hp, Clec4d, Cd163, Mmp9, Ormdl3, Il2rb, Orm1, Irak3, Tlr5, Lrg1, Clec4e, Clec5a, Mcemp1, and Ly96 - were identified. GO enrichment analysis of the DEGs showed that they were mainly involved in the biological functions of neutrophil degranulation, neutrophil activation during immune response, and cytokine secretion. KEGG analysis showed enrichment in pathways pertaining to Salmonella infection, Legionellosis, and inflammatory bowel disease. Finally, the core gene-transcription factor, gene-microRNA, and small-molecule relationships were predicted. These core genes may provide a novel theoretical basis for the diagnosis and treatment of IS and MI.

Wang M ，Gao Y ，Chen H ，Cheng J ，Wang G ... -  《-》

Study on potential differentially expressed genes in stroke by bioinformatics analysis.

Stroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine - cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.

Yang X ，Wang P ，Yan S ，Wang G ... -  《-》

Identification of microRNA-mRNA-TF regulatory networks in periodontitis by bioinformatics analysis.

Periodontitis is a complex infectious disease with various causes and contributing factors. The aim of this study was to identify key genes, microRNAs (miRNAs) and transcription factors (TFs) and construct a miRNA-mRNA-TF regulatory networks to investigate the underlying molecular mechanism in periodontitis. The GSE54710 miRNA microarray dataset and the gene expression microarray dataset GSE16134 were downloaded from the Gene Expression Omnibus database. The differentially expressed miRNAs (DEMis) and mRNAs (DEMs) were screened using the "limma" package in R. The intersection of the target genes of candidate DEMis and DEMs were considered significant DEMs in the regulatory network. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted. Subsequently, DEMs were uploaded to the STRING database, a protein-protein interaction (PPI) network was established, and the cytoHubba and MCODE plugins were used to screen out key hub mRNAs and significant modules. Ultimately, to investigate the regulatory network underlying periodontitis, a global triple network including miRNAs, mRNAs, and TFs was constructed using Cytoscape software. 8 DEMis and 121 DEMs were found between the periodontal and control groups. GO analysis showed that mRNAs were most significantly enriched in positive regulation of the cell cycle, and KEGG pathway analysis showed that mRNAs in the regulatory network were mainly involved in the IL-17 signalling pathway. A PPI network was constructed including 81 nodes and 414 edges. Furthermore, 12 hub genes ranked by the top 10% genes with high degree connectivity and five TFs, including SRF, CNOT4, SIX6, SRRM3, NELFA, and ONECUT3, were identified and might play crucial roles in the molecular pathogenesis of periodontitis. Additionally, a miRNA-mRNA-TF coregulatory network was established. In this study, we performed an integrated analysis based on public databases to identify specific TFs, miRNAs, and mRNAs that may play a pivotal role in periodontitis. On this basis, a TF-miRNA-mRNA network was established to provide a comprehensive perspective of the regulatory mechanism networks of periodontitis.

Gao X ，Zhao D ，Han J ，Zhang Z ，Wang Z ... -  《BMC Oral Health》

Identification of Hub Genes in the Pathogenesis of Ischemic Stroke Based on Bioinformatics Analysis.

Yang X ，Yan S ，Wang P ，Wang G ... -  《-》