Cargando…

Diagnosis, clustering, and immune cell infiltration analysis of m6A-related genes in patients with acute myocardial infarction—a bioinformatics analysis

BACKGROUND: Accurate myocardial infarction (AMI) is one of the leading causes of mortality worldwide. N6-methyladenosine (m6A) modification plays an important role in the development of cardiac remodeling and the cardiomyocyte contractile function. The aim of this study is to analyze the m6A-related...

Descripción completa

Detalles Bibliográficos
Autores principales: Liang, Changzai, Wang, Shen, Zhang, Meng, Li, Tianzhu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9186242/
https://www.ncbi.nlm.nih.gov/pubmed/35693610
http://dx.doi.org/10.21037/jtd-22-569
_version_ 1784724888905318400
author Liang, Changzai
Wang, Shen
Zhang, Meng
Li, Tianzhu
author_facet Liang, Changzai
Wang, Shen
Zhang, Meng
Li, Tianzhu
author_sort Liang, Changzai
collection PubMed
description BACKGROUND: Accurate myocardial infarction (AMI) is one of the leading causes of mortality worldwide. N6-methyladenosine (m6A) modification plays an important role in the development of cardiac remodeling and the cardiomyocyte contractile function. The aim of this study is to analyze the m6A-related molecular biological mechanisms of AMI in terms of accurate diagnosis and prognosis. METHODS: The platform data and probe data of the GSE66360 data set were downloaded. The differential analysis was conducted by combining the m6A-related gene expression. Thereafter, a diagnostic model was established using the random-forest method. The diagnostic accuracy of the diagnostic models was assessed by using the area under the receiver operating characteristic (ROC) curve (AUC). Next, the patients with AMI were clustered by unsupervised machine learning using the R software. Finally, an immune cell clustering analysis for each cluster was conducted to determine the correlations between m6A-related gene expression and the infiltration amount of the immune cells. The case and control groups were not matched in terms of demographics. RESULTS: The GSE6636 data set comprised 99 participants (49 patients with AMI and 50 without in control group). The differential analysis identified 10 m6A-related genes: 5 writers [Methyltransferase-like 3 (METTL3), Methyltransferase-like 14 (METTL14), Wilms tumor 1-associated protein (WTAP), Zinc Finger CCCH-Type Containing 13 (ZC3H13), and Casitas B-lineage proto-oncogene like 1 (CBLL1)], 4 readers [YT521-B homology domain-containing family 3 (YTHDF3), Fragile X mental retardation type 1 (FMR1), YT521-B homology-domain-containing protein 1 (YTHDC1), and insulin-like growth factor binding protein 3 (IGFBP3)] and 1 eraser [fat mass and obesity associated (FTO) gene]. The Mean Decrease Gini (MDG) values of these 10 genes were greater than 2. The FTO, WTAP, YTHDC1, IGFBP3, and CBLL1 were included in the model with a C index of 0.842. METTL3, ZC3H13, WTAP, and CBLL1 were highly expressed in Type A, and YTHDF3 was highly expressed in Type B. CONCLUSIONS: A diagnostic model of AMI was established based on the genes of FTO, WTAP, YTHDC1, IGFBP3, and CBLL1. Additionally, 2 molecular subtypes were successfully identified from the above-mentioned gene. Our findings could provide a novel method for the accurate diagnosis of AMI.
format Online
Article
Text
id pubmed-9186242
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher AME Publishing Company
record_format MEDLINE/PubMed
spelling pubmed-91862422022-06-11 Diagnosis, clustering, and immune cell infiltration analysis of m6A-related genes in patients with acute myocardial infarction—a bioinformatics analysis Liang, Changzai Wang, Shen Zhang, Meng Li, Tianzhu J Thorac Dis Original Article BACKGROUND: Accurate myocardial infarction (AMI) is one of the leading causes of mortality worldwide. N6-methyladenosine (m6A) modification plays an important role in the development of cardiac remodeling and the cardiomyocyte contractile function. The aim of this study is to analyze the m6A-related molecular biological mechanisms of AMI in terms of accurate diagnosis and prognosis. METHODS: The platform data and probe data of the GSE66360 data set were downloaded. The differential analysis was conducted by combining the m6A-related gene expression. Thereafter, a diagnostic model was established using the random-forest method. The diagnostic accuracy of the diagnostic models was assessed by using the area under the receiver operating characteristic (ROC) curve (AUC). Next, the patients with AMI were clustered by unsupervised machine learning using the R software. Finally, an immune cell clustering analysis for each cluster was conducted to determine the correlations between m6A-related gene expression and the infiltration amount of the immune cells. The case and control groups were not matched in terms of demographics. RESULTS: The GSE6636 data set comprised 99 participants (49 patients with AMI and 50 without in control group). The differential analysis identified 10 m6A-related genes: 5 writers [Methyltransferase-like 3 (METTL3), Methyltransferase-like 14 (METTL14), Wilms tumor 1-associated protein (WTAP), Zinc Finger CCCH-Type Containing 13 (ZC3H13), and Casitas B-lineage proto-oncogene like 1 (CBLL1)], 4 readers [YT521-B homology domain-containing family 3 (YTHDF3), Fragile X mental retardation type 1 (FMR1), YT521-B homology-domain-containing protein 1 (YTHDC1), and insulin-like growth factor binding protein 3 (IGFBP3)] and 1 eraser [fat mass and obesity associated (FTO) gene]. The Mean Decrease Gini (MDG) values of these 10 genes were greater than 2. The FTO, WTAP, YTHDC1, IGFBP3, and CBLL1 were included in the model with a C index of 0.842. METTL3, ZC3H13, WTAP, and CBLL1 were highly expressed in Type A, and YTHDF3 was highly expressed in Type B. CONCLUSIONS: A diagnostic model of AMI was established based on the genes of FTO, WTAP, YTHDC1, IGFBP3, and CBLL1. Additionally, 2 molecular subtypes were successfully identified from the above-mentioned gene. Our findings could provide a novel method for the accurate diagnosis of AMI. AME Publishing Company 2022-05 /pmc/articles/PMC9186242/ /pubmed/35693610 http://dx.doi.org/10.21037/jtd-22-569 Text en 2022 Journal of Thoracic Disease. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Liang, Changzai
Wang, Shen
Zhang, Meng
Li, Tianzhu
Diagnosis, clustering, and immune cell infiltration analysis of m6A-related genes in patients with acute myocardial infarction—a bioinformatics analysis
title Diagnosis, clustering, and immune cell infiltration analysis of m6A-related genes in patients with acute myocardial infarction—a bioinformatics analysis
title_full Diagnosis, clustering, and immune cell infiltration analysis of m6A-related genes in patients with acute myocardial infarction—a bioinformatics analysis
title_fullStr Diagnosis, clustering, and immune cell infiltration analysis of m6A-related genes in patients with acute myocardial infarction—a bioinformatics analysis
title_full_unstemmed Diagnosis, clustering, and immune cell infiltration analysis of m6A-related genes in patients with acute myocardial infarction—a bioinformatics analysis
title_short Diagnosis, clustering, and immune cell infiltration analysis of m6A-related genes in patients with acute myocardial infarction—a bioinformatics analysis
title_sort diagnosis, clustering, and immune cell infiltration analysis of m6a-related genes in patients with acute myocardial infarction—a bioinformatics analysis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9186242/
https://www.ncbi.nlm.nih.gov/pubmed/35693610
http://dx.doi.org/10.21037/jtd-22-569
work_keys_str_mv AT liangchangzai diagnosisclusteringandimmunecellinfiltrationanalysisofm6arelatedgenesinpatientswithacutemyocardialinfarctionabioinformaticsanalysis
AT wangshen diagnosisclusteringandimmunecellinfiltrationanalysisofm6arelatedgenesinpatientswithacutemyocardialinfarctionabioinformaticsanalysis
AT zhangmeng diagnosisclusteringandimmunecellinfiltrationanalysisofm6arelatedgenesinpatientswithacutemyocardialinfarctionabioinformaticsanalysis
AT litianzhu diagnosisclusteringandimmunecellinfiltrationanalysisofm6arelatedgenesinpatientswithacutemyocardialinfarctionabioinformaticsanalysis