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Identification of Five Hub Genes Based on Single-Cell RNA Sequencing Data and Network Pharmacology in Patients With Acute Myocardial Infarction

Acute myocardial infarction (AMI) has a high mortality. The single-cell RNA sequencing (scRNA-seq) method was used to analyze disease heterogeneity at the single-cell level. From the Gene Expression Omnibus (GEO) database (GSE180678), AMI scRNA-seq were downloaded and preprocessed by the Seurat pack...

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Autores principales: Song, Ziguang, Gao, Pingping, Zhong, Xiao, Li, Mingyang, Wang, Mengmeng, Song, Xiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9219909/
https://www.ncbi.nlm.nih.gov/pubmed/35757636
http://dx.doi.org/10.3389/fpubh.2022.894129
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author Song, Ziguang
Gao, Pingping
Zhong, Xiao
Li, Mingyang
Wang, Mengmeng
Song, Xiang
author_facet Song, Ziguang
Gao, Pingping
Zhong, Xiao
Li, Mingyang
Wang, Mengmeng
Song, Xiang
author_sort Song, Ziguang
collection PubMed
description Acute myocardial infarction (AMI) has a high mortality. The single-cell RNA sequencing (scRNA-seq) method was used to analyze disease heterogeneity at the single-cell level. From the Gene Expression Omnibus (GEO) database (GSE180678), AMI scRNA-seq were downloaded and preprocessed by the Seurat package. Gene expression data came from GSE182923. Cell cluster analysis was conducted. Cell types were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were performed on hub genes. Drugs were predicted by protein–protein interaction (PPI) and molecular docking. In total, 7 cell clusters were defined based on the scRNA-seq dataset, and the clusters were labeled as 5 cell types by marker genes. Hematopoietic stem cell types as a differential subgroups were higher in AMI than in healthy tissues. From available databases and PPI analysis, 52 common genets were identified. Based on 52 genes, 5 clusters were obtained using the MCODE algorithm, and genes in these 5 clusters involved in immune and inflammatory pathways were determined. Correlation analysis showed that hematopoietic stem cell types were negatively correlated with ATM, CARM1, and CASP8 but positively correlated with CASP3 and PPARG. This was reversed with immune cells. Molecular docking analysis showed that DB05490 had the lowest docking score with PPARG. We identified 5 hub genes (ATM, CARM1, CASP8, CASP3, and PPARG) involved in AMI progression. Compound DB05490 was a potential inhibitor of PPAG.
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spelling pubmed-92199092022-06-24 Identification of Five Hub Genes Based on Single-Cell RNA Sequencing Data and Network Pharmacology in Patients With Acute Myocardial Infarction Song, Ziguang Gao, Pingping Zhong, Xiao Li, Mingyang Wang, Mengmeng Song, Xiang Front Public Health Public Health Acute myocardial infarction (AMI) has a high mortality. The single-cell RNA sequencing (scRNA-seq) method was used to analyze disease heterogeneity at the single-cell level. From the Gene Expression Omnibus (GEO) database (GSE180678), AMI scRNA-seq were downloaded and preprocessed by the Seurat package. Gene expression data came from GSE182923. Cell cluster analysis was conducted. Cell types were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were performed on hub genes. Drugs were predicted by protein–protein interaction (PPI) and molecular docking. In total, 7 cell clusters were defined based on the scRNA-seq dataset, and the clusters were labeled as 5 cell types by marker genes. Hematopoietic stem cell types as a differential subgroups were higher in AMI than in healthy tissues. From available databases and PPI analysis, 52 common genets were identified. Based on 52 genes, 5 clusters were obtained using the MCODE algorithm, and genes in these 5 clusters involved in immune and inflammatory pathways were determined. Correlation analysis showed that hematopoietic stem cell types were negatively correlated with ATM, CARM1, and CASP8 but positively correlated with CASP3 and PPARG. This was reversed with immune cells. Molecular docking analysis showed that DB05490 had the lowest docking score with PPARG. We identified 5 hub genes (ATM, CARM1, CASP8, CASP3, and PPARG) involved in AMI progression. Compound DB05490 was a potential inhibitor of PPAG. Frontiers Media S.A. 2022-06-09 /pmc/articles/PMC9219909/ /pubmed/35757636 http://dx.doi.org/10.3389/fpubh.2022.894129 Text en Copyright © 2022 Song, Gao, Zhong, Li, Wang and Song. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Song, Ziguang
Gao, Pingping
Zhong, Xiao
Li, Mingyang
Wang, Mengmeng
Song, Xiang
Identification of Five Hub Genes Based on Single-Cell RNA Sequencing Data and Network Pharmacology in Patients With Acute Myocardial Infarction
title Identification of Five Hub Genes Based on Single-Cell RNA Sequencing Data and Network Pharmacology in Patients With Acute Myocardial Infarction
title_full Identification of Five Hub Genes Based on Single-Cell RNA Sequencing Data and Network Pharmacology in Patients With Acute Myocardial Infarction
title_fullStr Identification of Five Hub Genes Based on Single-Cell RNA Sequencing Data and Network Pharmacology in Patients With Acute Myocardial Infarction
title_full_unstemmed Identification of Five Hub Genes Based on Single-Cell RNA Sequencing Data and Network Pharmacology in Patients With Acute Myocardial Infarction
title_short Identification of Five Hub Genes Based on Single-Cell RNA Sequencing Data and Network Pharmacology in Patients With Acute Myocardial Infarction
title_sort identification of five hub genes based on single-cell rna sequencing data and network pharmacology in patients with acute myocardial infarction
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9219909/
https://www.ncbi.nlm.nih.gov/pubmed/35757636
http://dx.doi.org/10.3389/fpubh.2022.894129
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