Bioinformatics analysis identifies potential biomarkers for the prediction and treatment of myocardial infarction
Objectives: The aim of this study was to identify differentially expressed genes (DEGs) related to myocardial infarction (MI), which may serve as research and therapeutic targets. Methods: MI expression profiles were obtained from the Gene Expression Omnibus (GEO) database. DEGs were screened using...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer Health/LWW
2021-01-01
|
| Series: | Cardiology Plus |
| Subjects: | |
| Online Access: | http://www.cardiologyplus.org/article.asp?issn=2470-7511;year=2021;volume=6;issue=1;spage=48;epage=55;aulast=Ji |
| _version_ | 1797991332502831104 |
|---|---|
| author | Yu-Yao Ji Siang Wei Ran Xu Run-Da Wu Kang Yao Yun-Zeng Zou |
| author_facet | Yu-Yao Ji Siang Wei Ran Xu Run-Da Wu Kang Yao Yun-Zeng Zou |
| author_sort | Yu-Yao Ji |
| collection | DOAJ |
| description | Objectives: The aim of this study was to identify differentially expressed genes (DEGs) related to myocardial infarction (MI), which may serve as research and therapeutic targets. Methods: MI expression profiles were obtained from the Gene Expression Omnibus (GEO) database. DEGs were screened using GEO2R, and DEGs in multiple datasets were identified using Venn diagrams. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery v6.8. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape 3.7.2. Coexpedia was used for gene coexpression network analysis and functional annotation. Results: We identified 50 DEGs in the four datasets, including 29 with important roles in the PPI network. GO functional enrichment analysis revealed the involvement of DEGs in biological processes such as cytokine activation, peptidase inhibition, and chemokine activation. KEGG analysis revealed enrichment in chemokine signaling and cytokine-cytokine receptor interactions. Gene coexpression network analysis identified nine hub genes involved in the occurrence and development of MI including tissue inhibitor of metalloproteinase 1; CD44 antigen; lysyl oxidase; formyl peptide receptor 2; matrix metallopeptidase 3; formyl peptide receptor 1; serine (or cysteine) peptidase inhibitor, clade E, member 1; prostaglandin-endoperoxide synthase 2; and elastin. Conclusions: The hub genes identified may play important roles in MI-related biological processes and represent potential diagnostic and therapeutic targets. Therefore, this study lays a foundation for further exploration of the molecular mechanisms of MI. |
| first_indexed | 2024-04-11T08:50:27Z |
| format | Article |
| id | doaj.art-2e32958ae4a34407852ddbe163708b67 |
| institution | Directory Open Access Journal |
| issn | 2470-7511 2470-752X |
| language | English |
| last_indexed | 2024-04-11T08:50:27Z |
| publishDate | 2021-01-01 |
| publisher | Wolters Kluwer Health/LWW |
| record_format | Article |
| series | Cardiology Plus |
| spelling | doaj.art-2e32958ae4a34407852ddbe163708b672022-12-22T04:33:35ZengWolters Kluwer Health/LWWCardiology Plus2470-75112470-752X2021-01-0161485510.4103/2470-7511.312597Bioinformatics analysis identifies potential biomarkers for the prediction and treatment of myocardial infarctionYu-Yao JiSiang WeiRan XuRun-Da WuKang YaoYun-Zeng ZouObjectives: The aim of this study was to identify differentially expressed genes (DEGs) related to myocardial infarction (MI), which may serve as research and therapeutic targets. Methods: MI expression profiles were obtained from the Gene Expression Omnibus (GEO) database. DEGs were screened using GEO2R, and DEGs in multiple datasets were identified using Venn diagrams. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery v6.8. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape 3.7.2. Coexpedia was used for gene coexpression network analysis and functional annotation. Results: We identified 50 DEGs in the four datasets, including 29 with important roles in the PPI network. GO functional enrichment analysis revealed the involvement of DEGs in biological processes such as cytokine activation, peptidase inhibition, and chemokine activation. KEGG analysis revealed enrichment in chemokine signaling and cytokine-cytokine receptor interactions. Gene coexpression network analysis identified nine hub genes involved in the occurrence and development of MI including tissue inhibitor of metalloproteinase 1; CD44 antigen; lysyl oxidase; formyl peptide receptor 2; matrix metallopeptidase 3; formyl peptide receptor 1; serine (or cysteine) peptidase inhibitor, clade E, member 1; prostaglandin-endoperoxide synthase 2; and elastin. Conclusions: The hub genes identified may play important roles in MI-related biological processes and represent potential diagnostic and therapeutic targets. Therefore, this study lays a foundation for further exploration of the molecular mechanisms of MI.http://www.cardiologyplus.org/article.asp?issn=2470-7511;year=2021;volume=6;issue=1;spage=48;epage=55;aulast=Jigene expression; genes; heart disease; microarray analysis; signal transduction |
| spellingShingle | Yu-Yao Ji Siang Wei Ran Xu Run-Da Wu Kang Yao Yun-Zeng Zou Bioinformatics analysis identifies potential biomarkers for the prediction and treatment of myocardial infarction Cardiology Plus gene expression; genes; heart disease; microarray analysis; signal transduction |
| title | Bioinformatics analysis identifies potential biomarkers for the prediction and treatment of myocardial infarction |
| title_full | Bioinformatics analysis identifies potential biomarkers for the prediction and treatment of myocardial infarction |
| title_fullStr | Bioinformatics analysis identifies potential biomarkers for the prediction and treatment of myocardial infarction |
| title_full_unstemmed | Bioinformatics analysis identifies potential biomarkers for the prediction and treatment of myocardial infarction |
| title_short | Bioinformatics analysis identifies potential biomarkers for the prediction and treatment of myocardial infarction |
| title_sort | bioinformatics analysis identifies potential biomarkers for the prediction and treatment of myocardial infarction |
| topic | gene expression; genes; heart disease; microarray analysis; signal transduction |
| url | http://www.cardiologyplus.org/article.asp?issn=2470-7511;year=2021;volume=6;issue=1;spage=48;epage=55;aulast=Ji |
| work_keys_str_mv | AT yuyaoji bioinformaticsanalysisidentifiespotentialbiomarkersforthepredictionandtreatmentofmyocardialinfarction AT siangwei bioinformaticsanalysisidentifiespotentialbiomarkersforthepredictionandtreatmentofmyocardialinfarction AT ranxu bioinformaticsanalysisidentifiespotentialbiomarkersforthepredictionandtreatmentofmyocardialinfarction AT rundawu bioinformaticsanalysisidentifiespotentialbiomarkersforthepredictionandtreatmentofmyocardialinfarction AT kangyao bioinformaticsanalysisidentifiespotentialbiomarkersforthepredictionandtreatmentofmyocardialinfarction AT yunzengzou bioinformaticsanalysisidentifiespotentialbiomarkersforthepredictionandtreatmentofmyocardialinfarction |