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Mining the potential therapeutic targets for coronary artery disease by bioinformatics analysis.

ABSTRACT: The present study aimed to mine therapeutic molecular targets that play an important part in the progression of coronary artery disease (CAD). The gene expression profile GSE28829 dataset and the microRNA (miRNA) expression profile GSE59421 dataset were downloaded from the Gene Expression Omnibus (GEO) database. The GEO2R online analytical tool was used to identify differentially expressed genes (DEGs) and miRNAs (DEMs). The target genes of DEMs were identified using the miRWalk2.0 web?based tool and 2 miRNA?gene regulatory networks were constructed using Cytoscape software. Subsequently, enriched Gene Ontology (GO) terms of miRNA?target DEGs were obtained using the Database for Visualization, Annotation and Integrated Analysis, and locations of these genes in the chromosomes were determined by Map Viewer. In the present study, 350 DEGs and 66 DEMs were screened. A total of 3,588 target genes were identified from the DEMs, and 57 of these target genes and established DEGs were identified to overlap. GO terms associated with 5 processes, and 4 types of composition were identified to be enriched in the miRNA?target DEGs. Furthermore, 26 miRNA?gene regulatory pairs were obtained between the 57 target genes and DEMs. The 26 miRNA?target DEGs were unevenly distributed, and no genes were located on the sex chromosomes. As a result of the present study, potential therapeutic targets for CAD were identified through bioinformatics analysis.

SUBMITTER: Wang W

PROVIDER: S-EPMC6236289 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Mining the potential therapeutic targets for coronary artery disease by bioinformatics analysis.

Wang Wendong W Xu Zhiwei Z Zhu Xiaobo X Chang Xiaotong X

Molecular medicine reports 20181010 6

The present study aimed to mine therapeutic molecular targets that play an important part in the progression of coronary artery disease (CAD). The gene expression profile GSE28829 dataset and the microRNA (miRNA) expression profile GSE59421 dataset were downloaded from the Gene Expression Omnibus (GEO) database. The GEO2R online analytical tool was used to identify differentially expressed genes (DEGs) and miRNAs (DEMs). The target genes of DEMs were identified using the miRWalk2.0 web‑based too ...[more]

PMID: 30320387

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Project description:BackgroundCoronary artery disease (CAD) is a complex illness with unknown pathophysiology. Peripheral biomarkers are a non-invasive method required to track the onset and progression of CAD and have unbeatable benefits in terms of early identification, prognostic assessment, and categorization of the diagnosis. This study aimed to identify and validate the diagnostic and therapeutic potential of differentially expressed immune-related genes (DE-IRGs) in CAD, which will aid in improving our knowledge on the etiology of CAD and in forming genetic predictions.MethodsFirst, we searched coronary heart disease in the Gene Expression Omnibus (GEO) database and identified GSE20680 (CAD = 87, Normal = 52) as the trial set and GSE20681 (CAD = 99, Normal = 99) as the validation set. Functional enrichment analysis using protein-protein interactions (PPIs), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) was carried out on the identified differentially expressed genes. Optimal feature genes (OFGs) were generated using the support vector machine recursive feature elimination algorithm and the least absolute shrinkage and selection operator (LASSO) algorithm. Furthermore, immune infiltration in CAD patients and healthy controls was compared using CIBERSORT, and the relationship between immune cells and OFGs was examined. In addition, we constructed potential targeted drugs for this model through the Drug-Gene Interaction database (DGIdb) database. Finally, we verify the expression of S100A8-dominated OFGs in the GSE20681 dataset to confirm the universality of our study.ResultsWe identified the ten best OFGs for CAD from the DE-IRGs. Functional enrichment analysis showed that these marker genes are crucial for receptor-ligand activity, signaling receptor activator activity, and positive control of the response to stimuli from the outside world. Additionally, CIBERSORT revealed that S100A8 could be connected to alterations in the immune microenvironment in CAD patients. Furthermore, with the help of DGIdb and Cytoscape, a total of 64 medicines that target five marker genes were subsequently discovered. Finally, we verified the expression of the OFGs genes in the GSE20681 dataset between CAD patients and normal patients and found that there was also a significant difference in the expression of S100A8.ConclusionWe created a 10-gene immune-related prognostic model for CAD and confirmed its validity. The model can identify potential biomarkers for CAD prediction and more accurately gauge the progression of the disease.

Project description:Gliomas are primary tumors that originate in the brain or spinal cord and develop from supportive glial cells. The present study aimed to identify potential candidate molecular markers for the treatment of gliomas, and to explore the underlying mechanisms of this disease. The gene expression profile data GSE50021, which consisted of 10 specimens of normal brain tissues and 35 specimens of glioma tissues, was downloaded from Gene Expression Omnibus (GEO). The methylation microarray data GSE50022, consisting of 28 glioma specimens, was also downloaded from GEO. Differentially expressed genes (DEGs) between patients with glioma and normal individuals were identified, and key methylation sites were screened. Transcriptional regulatory networks were constructed, and target genes were selected. Survival analysis of key methylation sites and risk analysis of sub-pathways were performed, from which key genes and pathways were selected. A total of 79 DEGs and 179 key methylation sites were identified, of which 20 target genes and 36 transcription factors were included in the transcriptional regulatory network. Glutamate metabotropic receptor 2 (GRM2) was regulated by 8 transcription factors. Inositol-trisphosphate 3-kinase A (ITPKA) was a significantly enriched DEG, associated with the inositol phosphate metabolism pathway, Survival analysis revealed that the survival time of patients with lower methylation levels in cg00157228 was longer than patients with higher methylation levels. ITPKA was the closest located gene to cg00157228. In conclusion, GRM2 and enriched ITPKA, associated with the inositol phosphate metabolism pathway, may be key mechanisms in the development and progression of gliomas. Furthermore, the present study provided evidence for an additional mechanism of methylation-induced gliomas, in which methylation results in the dysregulation of specific transcripts. The results of the present study may provide a research direction for studying the mechanisms underlying the development and progression of gliomas.

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Mining the potential therapeutic targets for coronary artery disease by bioinformatics analysis.

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Mining the potential therapeutic targets for coronary artery disease by bioinformatics analysis.

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