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Integration of RNA-Seq and Machine Learning Identifies Hub Genes for Empagliflozin Benefitable Heart Failure with Reduced Ejection Fraction

PURPOSE: This study aimed to analyze the hub genes of heart failure with reduced ejection fraction (HFrEF) treated with Empagliflozin using RNA sequencing (RNA-seq) and bioinformatics methods, including machine learning. METHODS: From February 2021 to February 2023, nine patients with HFrEF were enr...

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Autores principales: Yang, Qiang, Gao, Jing, Wang, Tian-Yu, Ding, Jun-Can, Hu, Peng-Fei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10590560/
https://www.ncbi.nlm.nih.gov/pubmed/37872956
http://dx.doi.org/10.2147/JIR.S429096
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author Yang, Qiang
Gao, Jing
Wang, Tian-Yu
Ding, Jun-Can
Hu, Peng-Fei
author_facet Yang, Qiang
Gao, Jing
Wang, Tian-Yu
Ding, Jun-Can
Hu, Peng-Fei
author_sort Yang, Qiang
collection PubMed
description PURPOSE: This study aimed to analyze the hub genes of heart failure with reduced ejection fraction (HFrEF) treated with Empagliflozin using RNA sequencing (RNA-seq) and bioinformatics methods, including machine learning. METHODS: From February 2021 to February 2023, nine patients with HFrEF were enrolled from our hospital’s cardiovascular department. In addition to routine drug treatment, these patients received 10 mg of Empagliflozin once daily for two months. Efficacy was assessed and RNA-seq was performed on peripheral blood before and after treatment with empagliflozin. HFrEF-related hub genes were identified through bioinformatics analyses including differential gene expression analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, immune infiltration analysis, machine learning, immune cell correlation analysis and clinical indicator correlation analysis. RESULTS: The nine patients included in this study completed a two-month treatment regimen, with an average age of 62.11 ± 6.36 years. By performing bioinformatics analysis on the transcriptome from the treatment groups, 42 differentially expressed genes were identified, with six being up-regulated and 36 being down-regulated (|log2FC|>1 and adj.pvalue<0.05). Immune infiltration analysis of these genes demonstrated a significant difference in the proportion of plasma between the pre-treatment and post-treatment groups (p<0.05). Two hub genes, GTF2IP14 and MTLN, were finally identified through machine learning. Further analysis of the correlation between the hub genes and immune cells suggested a negative correlation between GTF2IP14 and naive B cells, and a positive correlation between MTLN and regulatory T cells and resting memory CD4+ T cells (p<0.05). CONCLUSION: Through RNA-seq and bioinformatics analysis, this study identified GTF2IP14 and MTLN as the hub genes of HFrEF, and their mechanisms may be related to immune inflammatory responses and various immune cells.
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spelling pubmed-105905602023-10-23 Integration of RNA-Seq and Machine Learning Identifies Hub Genes for Empagliflozin Benefitable Heart Failure with Reduced Ejection Fraction Yang, Qiang Gao, Jing Wang, Tian-Yu Ding, Jun-Can Hu, Peng-Fei J Inflamm Res Original Research PURPOSE: This study aimed to analyze the hub genes of heart failure with reduced ejection fraction (HFrEF) treated with Empagliflozin using RNA sequencing (RNA-seq) and bioinformatics methods, including machine learning. METHODS: From February 2021 to February 2023, nine patients with HFrEF were enrolled from our hospital’s cardiovascular department. In addition to routine drug treatment, these patients received 10 mg of Empagliflozin once daily for two months. Efficacy was assessed and RNA-seq was performed on peripheral blood before and after treatment with empagliflozin. HFrEF-related hub genes were identified through bioinformatics analyses including differential gene expression analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, immune infiltration analysis, machine learning, immune cell correlation analysis and clinical indicator correlation analysis. RESULTS: The nine patients included in this study completed a two-month treatment regimen, with an average age of 62.11 ± 6.36 years. By performing bioinformatics analysis on the transcriptome from the treatment groups, 42 differentially expressed genes were identified, with six being up-regulated and 36 being down-regulated (|log2FC|>1 and adj.pvalue<0.05). Immune infiltration analysis of these genes demonstrated a significant difference in the proportion of plasma between the pre-treatment and post-treatment groups (p<0.05). Two hub genes, GTF2IP14 and MTLN, were finally identified through machine learning. Further analysis of the correlation between the hub genes and immune cells suggested a negative correlation between GTF2IP14 and naive B cells, and a positive correlation between MTLN and regulatory T cells and resting memory CD4+ T cells (p<0.05). CONCLUSION: Through RNA-seq and bioinformatics analysis, this study identified GTF2IP14 and MTLN as the hub genes of HFrEF, and their mechanisms may be related to immune inflammatory responses and various immune cells. Dove 2023-10-18 /pmc/articles/PMC10590560/ /pubmed/37872956 http://dx.doi.org/10.2147/JIR.S429096 Text en © 2023 Yang et al. 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
Yang, Qiang
Gao, Jing
Wang, Tian-Yu
Ding, Jun-Can
Hu, Peng-Fei
Integration of RNA-Seq and Machine Learning Identifies Hub Genes for Empagliflozin Benefitable Heart Failure with Reduced Ejection Fraction
title Integration of RNA-Seq and Machine Learning Identifies Hub Genes for Empagliflozin Benefitable Heart Failure with Reduced Ejection Fraction
title_full Integration of RNA-Seq and Machine Learning Identifies Hub Genes for Empagliflozin Benefitable Heart Failure with Reduced Ejection Fraction
title_fullStr Integration of RNA-Seq and Machine Learning Identifies Hub Genes for Empagliflozin Benefitable Heart Failure with Reduced Ejection Fraction
title_full_unstemmed Integration of RNA-Seq and Machine Learning Identifies Hub Genes for Empagliflozin Benefitable Heart Failure with Reduced Ejection Fraction
title_short Integration of RNA-Seq and Machine Learning Identifies Hub Genes for Empagliflozin Benefitable Heart Failure with Reduced Ejection Fraction
title_sort integration of rna-seq and machine learning identifies hub genes for empagliflozin benefitable heart failure with reduced ejection fraction
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10590560/
https://www.ncbi.nlm.nih.gov/pubmed/37872956
http://dx.doi.org/10.2147/JIR.S429096
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