Cargando…
Bioinformatic Analysis for Potential Biomarkers and Therapeutic Targets of T2DM-related MI
BACKGROUND: Type 2 diabetes mellitus (T2DM), a major risk factor of coronary heart disease, is associated with an approximately twofold increase in the risk of myocardial infarction (MI). We studied co-expressed genes to demonstrate relationships between DM and MI and revealed the potential biomarke...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8370113/ https://www.ncbi.nlm.nih.gov/pubmed/34413670 http://dx.doi.org/10.2147/IJGM.S325980 |
_version_ | 1783739413109407744 |
---|---|
author | Li, Chan Liu, Zhaoya |
author_facet | Li, Chan Liu, Zhaoya |
author_sort | Li, Chan |
collection | PubMed |
description | BACKGROUND: Type 2 diabetes mellitus (T2DM), a major risk factor of coronary heart disease, is associated with an approximately twofold increase in the risk of myocardial infarction (MI). We studied co-expressed genes to demonstrate relationships between DM and MI and revealed the potential biomarkers and therapeutic targets of T2DM-related MI. METHODS: DM and MI-related differentially expressed genes (DEGs) were identified by bioinformatic analysis, Gene Expression Omnibus (GEO) datasets GSE42148 and GSE61144 of MI patients, and the normal control and GSE26168 and GSE15932 of DM patients and normal controls, respectively. Further target prediction and network analysis method were used to detect protein-protein interaction (PPI) networks, gene ontology (GO) terms, and pathway enrichment of DEGs. Co-expressed DEGs of T2DM-related MI were analyzed as well. RESULTS: We identified 210 upregulated and 127 downregulated DEGs in T2DM, as well as 264 upregulated and 242 downregulated DEGs in MI. Eighteen upregulated and four downregulated DEGs were identified as co-DEGs of T2DM and MI. Functional analysis revealed that T2DM-related DEGs were mostly enriched in the viral process and ubiquitin-mediated proteolysis, while MI-related DEGs were mostly enriched in protein phosphorylation and TNF signaling pathway. MPO, MMP9, CAMP, LTF, AZU1, DEFA4, STAT3, and PECAM1 were recognized as the hub genes of the co-DEGs with acceptable diagnostic values in T2DM and MI datasets. Adenosine receptor agonist IB-MECA was predicted to be a potential drug for T2DM-related MI with the highest CMap connectivity score. CONCLUSION: Our study identified that the co-DEGs of MPO, MMP9, CAMP, LTF, AZU1, DEFA4, STAT3, and PECAM1 are significantly associated with novel biomarkers involved in T2DM-related MI. However, more experimental research and clinical trials are demanded to verify our results. |
format | Online Article Text |
id | pubmed-8370113 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-83701132021-08-18 Bioinformatic Analysis for Potential Biomarkers and Therapeutic Targets of T2DM-related MI Li, Chan Liu, Zhaoya Int J Gen Med Original Research BACKGROUND: Type 2 diabetes mellitus (T2DM), a major risk factor of coronary heart disease, is associated with an approximately twofold increase in the risk of myocardial infarction (MI). We studied co-expressed genes to demonstrate relationships between DM and MI and revealed the potential biomarkers and therapeutic targets of T2DM-related MI. METHODS: DM and MI-related differentially expressed genes (DEGs) were identified by bioinformatic analysis, Gene Expression Omnibus (GEO) datasets GSE42148 and GSE61144 of MI patients, and the normal control and GSE26168 and GSE15932 of DM patients and normal controls, respectively. Further target prediction and network analysis method were used to detect protein-protein interaction (PPI) networks, gene ontology (GO) terms, and pathway enrichment of DEGs. Co-expressed DEGs of T2DM-related MI were analyzed as well. RESULTS: We identified 210 upregulated and 127 downregulated DEGs in T2DM, as well as 264 upregulated and 242 downregulated DEGs in MI. Eighteen upregulated and four downregulated DEGs were identified as co-DEGs of T2DM and MI. Functional analysis revealed that T2DM-related DEGs were mostly enriched in the viral process and ubiquitin-mediated proteolysis, while MI-related DEGs were mostly enriched in protein phosphorylation and TNF signaling pathway. MPO, MMP9, CAMP, LTF, AZU1, DEFA4, STAT3, and PECAM1 were recognized as the hub genes of the co-DEGs with acceptable diagnostic values in T2DM and MI datasets. Adenosine receptor agonist IB-MECA was predicted to be a potential drug for T2DM-related MI with the highest CMap connectivity score. CONCLUSION: Our study identified that the co-DEGs of MPO, MMP9, CAMP, LTF, AZU1, DEFA4, STAT3, and PECAM1 are significantly associated with novel biomarkers involved in T2DM-related MI. However, more experimental research and clinical trials are demanded to verify our results. Dove 2021-08-10 /pmc/articles/PMC8370113/ /pubmed/34413670 http://dx.doi.org/10.2147/IJGM.S325980 Text en © 2021 Li and Liu. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Li, Chan Liu, Zhaoya Bioinformatic Analysis for Potential Biomarkers and Therapeutic Targets of T2DM-related MI |
title | Bioinformatic Analysis for Potential Biomarkers and Therapeutic Targets of T2DM-related MI |
title_full | Bioinformatic Analysis for Potential Biomarkers and Therapeutic Targets of T2DM-related MI |
title_fullStr | Bioinformatic Analysis for Potential Biomarkers and Therapeutic Targets of T2DM-related MI |
title_full_unstemmed | Bioinformatic Analysis for Potential Biomarkers and Therapeutic Targets of T2DM-related MI |
title_short | Bioinformatic Analysis for Potential Biomarkers and Therapeutic Targets of T2DM-related MI |
title_sort | bioinformatic analysis for potential biomarkers and therapeutic targets of t2dm-related mi |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8370113/ https://www.ncbi.nlm.nih.gov/pubmed/34413670 http://dx.doi.org/10.2147/IJGM.S325980 |
work_keys_str_mv | AT lichan bioinformaticanalysisforpotentialbiomarkersandtherapeutictargetsoft2dmrelatedmi AT liuzhaoya bioinformaticanalysisforpotentialbiomarkersandtherapeutictargetsoft2dmrelatedmi |