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ORIGINAL RESEARCH article

Front. Genet.

Sec. Computational Genomics

Volume 16 - 2025 | doi: 10.3389/fgene.2025.1618390

Identification of key genes for heart failure in dilated cardiomyopathy in different populations

Provisionally accepted
Yue  YuYue Yu1Chentian  XueChentian Xue2Dong  JiDong Ji2Wei  ShengWei Sheng1Xiang  GaoXiang Gao1*Xize  WuXize Wu3*Chengyan  WuChengyan Wu2*
  • 1Nantong Hospital of Traditional Chinese Medicine, Nantong, China
  • 2Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
  • 3Liaoning University of Traditional Chinese Medicine, Shenyang, China

The final, formatted version of the article will be published soon.

Background: Heart failure (HF) represents the end stage of cardiovascular disease and is the leading cause of mortality. The objective of this study was to identify potential biomarkers and elucidate the mechanisms underlying the development of HF across diverse populations and among different genders. Methods: This study strictly included five datasets of HF with dilated cardiomyopathy: GSE141910, GSE57345, GSE21610 , GSE17800, and GSE42955. Differentially expressed genes (DEGs) were identified through differential expression analysis, and module genes were identified using weighted gene co-expression network analysis. Subsequent stratification by gender and ethnicity (African American, Caucasian, German, and Spanish) was performed, followed by immune infiltration analysis. Finally, the least absolute shrinkage and selection operator (LASSO) regression, support vector machine-recursive feature elimination (SVM-REF), and random forest (RF) models were used to screen for Hub genes and to construct a nomogram predicting the occurrence of HF in different populations based on these Hub genes. Additionally, GSE3585, GSE120895, GSE5406, and GSE1145 serve as the validation set. Results: Functional enrichment analysis demonstrated that the pathogenesis of HF is closely related to the inflammatory response, immune response, vascular regulation, the Wnt signaling pathway, glutathione metabolism, sphingolipid metabolism, and apoptosis. Immune infiltration analysis showed that HF patients exhibited a high abundance of resting mast cells, resting NK cells, CD8T cells, resting memory CD4 T cells, activated memory CD4 T cells, M1 Macrophages, naive CD4 T cells, M0 Macrophages, regulatory T cells (Tregs), follicular helper T cells, Monocytes, and activated NK cells, and a lower abundance of plasma cells, neutrophils, and eosinophils. Multiple machine learning analyses identified MYH6, ASPN, and COL14A1 as Hub genes, NAP1L3, PLEKHH2, MOXD1, CCDC80, CA14, and SERPINE2 as male-specific, CX3CR1, SYN2, and SLC25A18 as female-specific, and NQO1, KAZALD1, and UBASH3A as African American male-specific, SYN2 as African American female-specific, CD83, C1QTNF3, GRB14, and MOXD1 as Caucasian male-specific, CD83, VIT, and PODXL2 as Caucasian female-specific, LSAMP and C14orf132 as German male-specific, and LSAMP and BMP4 as German female-specific, CIART and SNORA80E as Spanish-specific DEGs. Hub genes are strongly associated with M1 macrophages. Conclusions: MYH6, ASPN, and COL14A1 may be potential biomarkers for HF in dilated cardiomyopathy.

Keywords: dilated cardiomyopathy, Heart Failure, bioinformatics, machine learning models, Genes

Received: 29 Apr 2025; Accepted: 29 Sep 2025.

Copyright: © 2025 Yu, Xue, Ji, Sheng, Gao, Wu and Wu. 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) or licensor 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.

* Correspondence:
Xiang Gao, ntgx1964@163.com
Xize Wu, 953935269@qq.com
Chengyan Wu, chengyanwu1999@163.com

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