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Identification of key proteins as potential biomarkers associated with post-infarction complications in diabetics

Background: The ability of transcriptome analysis to identify dysregulated pathways and outcome-related genes following myocardial infarction in diabetic patients remains unknown. The present study was designed to detect possible biomarkers associated with the incidence of post-infarction complicati...

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Autores principales: Wu, Wei, Yan, Li, Yuan, Xiao-Fei, Wang, Lu, Zhang, Yu, Tu, Rong-xiang, Pan, Jiang-Qi, Yin, Lu, Ge, Zhi-Ru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10676060/
https://www.ncbi.nlm.nih.gov/pubmed/37999626
http://dx.doi.org/10.1177/03946320231216313
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author Wu, Wei
Yan, Li
Yuan, Xiao-Fei
Wang, Lu
Zhang, Yu
Tu, Rong-xiang
Pan, Jiang-Qi
Yin, Lu
Ge, Zhi-Ru
author_facet Wu, Wei
Yan, Li
Yuan, Xiao-Fei
Wang, Lu
Zhang, Yu
Tu, Rong-xiang
Pan, Jiang-Qi
Yin, Lu
Ge, Zhi-Ru
author_sort Wu, Wei
collection PubMed
description Background: The ability of transcriptome analysis to identify dysregulated pathways and outcome-related genes following myocardial infarction in diabetic patients remains unknown. The present study was designed to detect possible biomarkers associated with the incidence of post-infarction complications in diabetes to assist thedevelopment of novel treatments for this condition. Methods: Two gene expression datasets, GSE12639 and GSE6880, were downloaded from the Gene Expression Omnibus (GEO) database, and then differentially expressed genes (DEGs) were identified between post-infarction diabetics and healthy samples from the left ventricular wall of rats. These DEGs were then arranged into a protein–protein interaction (PPI) network, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses were performed to explore the functional roles of these genes. Results: In total, 30 DEGs (14 upregulated and 16 downregulated) were shared between these two datasets, as identified through Venn diagram analyses. GO analyses revealed these DEGs to be significantly enriched in ovarian steroidogenesis, fatty acid elongation, biosynthesis of unsaturated fatty acids, synthesis and degradation of ketone bodies, and butanoate metabolism. The PPI network of the DEGs had 14 genes and 70 edges. We identified two key proteins, 3-hydroxymethylglutaryl-CoA synthase 2 (Hmgcs2) and Δ3, Δ2-Enoyl-CoA Delta Isomerase 1 (ECI1), and the upregulated gene Hmgcs2 with the highest score in the MCC method. We generated a co-expression network for the hub genes and obtained the top ten medications suggested for infarction with diabetes. Conclusion: Taken together, the findings of these bioinformatics analyses identified key hub genes associated with the development of myocardial infarction in diabetics. These hub genes and potential drugs may become novel biomarkers for prognosis and precision treatment in the future.
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spelling pubmed-106760602023-11-24 Identification of key proteins as potential biomarkers associated with post-infarction complications in diabetics Wu, Wei Yan, Li Yuan, Xiao-Fei Wang, Lu Zhang, Yu Tu, Rong-xiang Pan, Jiang-Qi Yin, Lu Ge, Zhi-Ru Int J Immunopathol Pharmacol Original Research Article Background: The ability of transcriptome analysis to identify dysregulated pathways and outcome-related genes following myocardial infarction in diabetic patients remains unknown. The present study was designed to detect possible biomarkers associated with the incidence of post-infarction complications in diabetes to assist thedevelopment of novel treatments for this condition. Methods: Two gene expression datasets, GSE12639 and GSE6880, were downloaded from the Gene Expression Omnibus (GEO) database, and then differentially expressed genes (DEGs) were identified between post-infarction diabetics and healthy samples from the left ventricular wall of rats. These DEGs were then arranged into a protein–protein interaction (PPI) network, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses were performed to explore the functional roles of these genes. Results: In total, 30 DEGs (14 upregulated and 16 downregulated) were shared between these two datasets, as identified through Venn diagram analyses. GO analyses revealed these DEGs to be significantly enriched in ovarian steroidogenesis, fatty acid elongation, biosynthesis of unsaturated fatty acids, synthesis and degradation of ketone bodies, and butanoate metabolism. The PPI network of the DEGs had 14 genes and 70 edges. We identified two key proteins, 3-hydroxymethylglutaryl-CoA synthase 2 (Hmgcs2) and Δ3, Δ2-Enoyl-CoA Delta Isomerase 1 (ECI1), and the upregulated gene Hmgcs2 with the highest score in the MCC method. We generated a co-expression network for the hub genes and obtained the top ten medications suggested for infarction with diabetes. Conclusion: Taken together, the findings of these bioinformatics analyses identified key hub genes associated with the development of myocardial infarction in diabetics. These hub genes and potential drugs may become novel biomarkers for prognosis and precision treatment in the future. SAGE Publications 2023-11-24 /pmc/articles/PMC10676060/ /pubmed/37999626 http://dx.doi.org/10.1177/03946320231216313 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research Article
Wu, Wei
Yan, Li
Yuan, Xiao-Fei
Wang, Lu
Zhang, Yu
Tu, Rong-xiang
Pan, Jiang-Qi
Yin, Lu
Ge, Zhi-Ru
Identification of key proteins as potential biomarkers associated with post-infarction complications in diabetics
title Identification of key proteins as potential biomarkers associated with post-infarction complications in diabetics
title_full Identification of key proteins as potential biomarkers associated with post-infarction complications in diabetics
title_fullStr Identification of key proteins as potential biomarkers associated with post-infarction complications in diabetics
title_full_unstemmed Identification of key proteins as potential biomarkers associated with post-infarction complications in diabetics
title_short Identification of key proteins as potential biomarkers associated with post-infarction complications in diabetics
title_sort identification of key proteins as potential biomarkers associated with post-infarction complications in diabetics
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10676060/
https://www.ncbi.nlm.nih.gov/pubmed/37999626
http://dx.doi.org/10.1177/03946320231216313
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