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Exploring an immune cells-related molecule in STEMI by bioinformatics analysis

BACKGROUND: ST-elevated myocardial infarction (STEMI) is the leading cause of mortality worldwide. The mortality rate of heart attacks has decreased due to various preventive factors and the development of early diagnostic resuscitation measures, but the long-term prognosis remains poor. The present...

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Autores principales: Zhang, Min, Li, Jiaxing, Hua, Cuncun, Niu, Jiayin, Liu, Pengfei, Zhong, Guangzhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311814/
https://www.ncbi.nlm.nih.gov/pubmed/37391746
http://dx.doi.org/10.1186/s12920-023-01579-8
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author Zhang, Min
Li, Jiaxing
Hua, Cuncun
Niu, Jiayin
Liu, Pengfei
Zhong, Guangzhen
author_facet Zhang, Min
Li, Jiaxing
Hua, Cuncun
Niu, Jiayin
Liu, Pengfei
Zhong, Guangzhen
author_sort Zhang, Min
collection PubMed
description BACKGROUND: ST-elevated myocardial infarction (STEMI) is the leading cause of mortality worldwide. The mortality rate of heart attacks has decreased due to various preventive factors and the development of early diagnostic resuscitation measures, but the long-term prognosis remains poor. The present study aimed to identify novel serum biomarkers in STEMI patients and explored a possible new mechanism of STEMI from an immune molecular angle with bioinformatics analysis. METHODS: Gene expression profiles were obtained from Gene Expression Omnibus (GEO) database. Differential gene analysis, machine learning algorithms, gene set enrichment analysis, and immune cell infiltration analysis were conducted using R software. RESULTS: We identified 146 DEGs (differentially expressed genes) in the integrated dataset between the STEMI and CAD (coronary artery disease) groups. Immune infiltration analysis indicated that eleven cell types were differentially infiltrated. Through correlation analysis, we further screened 25 DEGs that showed a high correlation with monocytes and neutrophils. Afterwards, five genes consistently selected by all three machine learning algorithms were considered candidate genes. Finally, we identified a hub gene (ADM) as a biomarker of STEMI. AUC curves showed that ADM had more than 80% high accuracy in all datasets. CONCLUSIONS: In this study, we explored a potentially new mechanism of STEMI from an immune molecular perspective, which might provide insights into the pathogenesis of STEMI. ADM positively correlated with monocytes and neutrophils, suggesting its potential role in the immune response during STEMI. Additionally, we validated the diagnostic performance of ADM in two external datasets, which could help to develop new diagnostic tools or therapeutic strategies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01579-8.
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spelling pubmed-103118142023-07-01 Exploring an immune cells-related molecule in STEMI by bioinformatics analysis Zhang, Min Li, Jiaxing Hua, Cuncun Niu, Jiayin Liu, Pengfei Zhong, Guangzhen BMC Med Genomics Research BACKGROUND: ST-elevated myocardial infarction (STEMI) is the leading cause of mortality worldwide. The mortality rate of heart attacks has decreased due to various preventive factors and the development of early diagnostic resuscitation measures, but the long-term prognosis remains poor. The present study aimed to identify novel serum biomarkers in STEMI patients and explored a possible new mechanism of STEMI from an immune molecular angle with bioinformatics analysis. METHODS: Gene expression profiles were obtained from Gene Expression Omnibus (GEO) database. Differential gene analysis, machine learning algorithms, gene set enrichment analysis, and immune cell infiltration analysis were conducted using R software. RESULTS: We identified 146 DEGs (differentially expressed genes) in the integrated dataset between the STEMI and CAD (coronary artery disease) groups. Immune infiltration analysis indicated that eleven cell types were differentially infiltrated. Through correlation analysis, we further screened 25 DEGs that showed a high correlation with monocytes and neutrophils. Afterwards, five genes consistently selected by all three machine learning algorithms were considered candidate genes. Finally, we identified a hub gene (ADM) as a biomarker of STEMI. AUC curves showed that ADM had more than 80% high accuracy in all datasets. CONCLUSIONS: In this study, we explored a potentially new mechanism of STEMI from an immune molecular perspective, which might provide insights into the pathogenesis of STEMI. ADM positively correlated with monocytes and neutrophils, suggesting its potential role in the immune response during STEMI. Additionally, we validated the diagnostic performance of ADM in two external datasets, which could help to develop new diagnostic tools or therapeutic strategies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01579-8. BioMed Central 2023-06-30 /pmc/articles/PMC10311814/ /pubmed/37391746 http://dx.doi.org/10.1186/s12920-023-01579-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhang, Min
Li, Jiaxing
Hua, Cuncun
Niu, Jiayin
Liu, Pengfei
Zhong, Guangzhen
Exploring an immune cells-related molecule in STEMI by bioinformatics analysis
title Exploring an immune cells-related molecule in STEMI by bioinformatics analysis
title_full Exploring an immune cells-related molecule in STEMI by bioinformatics analysis
title_fullStr Exploring an immune cells-related molecule in STEMI by bioinformatics analysis
title_full_unstemmed Exploring an immune cells-related molecule in STEMI by bioinformatics analysis
title_short Exploring an immune cells-related molecule in STEMI by bioinformatics analysis
title_sort exploring an immune cells-related molecule in stemi by bioinformatics analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311814/
https://www.ncbi.nlm.nih.gov/pubmed/37391746
http://dx.doi.org/10.1186/s12920-023-01579-8
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