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Updated: Jul 12, 2025
Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
Published on: November 11, 2014
Integration of RNA-Seq and Machine Learning Identifies Hub Genes for Empagliflozin Benefitable Heart Failure with
Qiang Yang1, Jing Gao2, Tian-Yu Wang1
1Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, 310053, People's Republic of China.
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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