ORIGINAL RESEARCH article
Front. Genet.
Sec. Computational Genomics
Volume 16 - 2025 | doi: 10.3389/fgene.2025.1587274
Dissecting and Validation the Biomarker of Heart Failure Progression in Patients With Atherosclerosis by Single-cell Sequencing, Bioinformatics, and Machine Learning
Provisionally accepted- 1Hubei Minzu University, Enshi, China
- 2Zhongnan Hospital, Wuhan University, Wuhan, Hubei Province, China
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Objective This study aimed to identify early biomarkers associated with the progression from atherosclerosis (AS) to heart failure (HF) by integrating single-cell RNA sequencing (scRNA-seq) and bulk transcriptomic data, and to explore the potential underlying mechanisms.Method Transcriptomic datasets (GSE28829 and GSE57345) were obtained from the Gene Expression Omnibus (GEO) database, and single-cell RNA sequencing (scRNA-seq) data were downloaded from the Human Cell Landscape (HCL) platform. Genes of interest were identified by integrating results from weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs) analysis, and cell-type-specific expression patterns. Three machine learning algorithms (LASSO, Random Forest, and SVM-RFE) were employed to screen for robust candidate biomarkers. External validation was performed using three independent datasets: GSE53274, GSE5406, and GSE59867.Result ScRNA-seq data screened for 2828 cardiac-related genes. WGCNA identified 918 genes highly associated with AS. In addition, the limma package identified 9675 DEGs associated with HF progression. A total of 119 overlapping genes were obtained by intersecting the results from the above three analyses. Based on these 119 overlapping genes, three machine learning algorithms (LASSO, Random Forest, and SVM-RFE) were applied to datasets GSE28829 and GSE57345, and consistently identified CD48 as a robust signature gene, with an area under the curve (AUC) greater than 0.7.External validation confirmed CD48 as a potential biomarker for the progression from AS to HF.CD48 was identified as a potential early biomarker for the transition from AS to HF, which may offer new insights for risk stratification and early intervention in disease progression.
Keywords: Heart failure progression, Atherosclerosis, single-cell sequencing, bioinformatics, machine learning
Received: 11 Mar 2025; Accepted: 04 Aug 2025.
Copyright: © 2025 Wang, Ni, Li, Du, Zhou and Zhang. 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: Liankai Wang, Hubei Minzu University, Enshi, China
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