Dissecting and validating the biomarker of heart failure progression in patients with atherosclerosis - single-cell sequencing, bioinformatics, and machine learning

Aug 6, 2026

Upload by Violet

This study identifies early biomarkers linked to progression from atherosclerosis (AS) to heart failure (HF) by integrating single-cell RNA sequencing with bulk transcriptomic analyses and mechanistic exploration. Transcriptomic datasets were retrieved from GEO and single-cell data from the Human Cell Landscape platform. Candidate genes were derived by combining WGCNA, differential expression analysis, and cell-type-specific expression patterns, then refined using LASSO, Random Forest, and SVM-RFE. External validation across independent datasets supported CD48 as a robust signature for AS-to-HF transition.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Objective

Methods

Data sources and preprocessing

Biomarker screening and validation

Results

Conclusion

Keywords

TYPE Original Research PUBLISHED 12 September 2025 DOI 10.3389/fgene.2025.1587274 OPEN ACCESS EDITED BY Xiao Chang, Children ’s Hospital of Philadelphia, United States REVIEWED BY Ashwini Kumar Ray, University of Delhi, India Huanxin 丁, Shandong Provincial Qianfoshan Hospital, China *CORRESPONDENCE Liankai Wang, [wanglk_minda@163.com](wanglk_minda@163.com)[ ](wanglk_minda@163.com)Lihua Ni, [nilihua@whu.edu.cn](nilihua@whu.edu.cn), [nilihua2016@163.com](nilihua2016@163.com) †These authors have contributed equally to this work RECEIVED 11 March 2025 ACCEPTED 04 August 2025 PUBLISHED 12 September 2025 CITATION Ni L, Li H, Du J, Zhou K, Zhang F and Wang L (2025) Dissecting and validation the biomarker of heart failure progression in patients with atherosclerosis by single-cell sequencing, bioinformatics, and machine learning. Front. Genet. 16:1587274 . doi: 10.3389/fgene.2025.1587274 COPYRIGHT © 2025 Ni, Li, Du, Zhou, Zhang and Wang. This isan 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. Dissecting and validation the biomarker of heart failure progression in patients with atherosclerosis by single-cell sequencing, bioinformatics, and machine learning Lihua Ni 1*†, Huabo Li 2,3†, Juan Du 2,3, Ke Zhou 2,3, Fugui Zhang 2,3 and Liankai Wang 2,3* 1Department of Nephrology, Zhongnan Hospital of Wuhan University, Wuhan, China, 2Department of Cardiology, Minda Hospital of Hubei Minzu University, Enshi, China, 3Hubei Provincial Key Laboratory of Occurrence and Intervention of Rheumatic Diseases, Minda Hospital of Hubei Minzu University, Enshi, China Objective: This study aimed to identify early biomarkers associated with the progression from atherosclerosis (AS) to heart failure (HF) by integrating singlecell 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. Conclusion: 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 Frontiers in Genetics 01 [frontiersin.org](frontiersin.org) 1 I

FAQs

What was the main objective of the study?

To identify early biomarkers associated with the progression from atherosclerosis to heart failure and explore underlying mechanisms.

Which data types and analysis methods were used to screen biomarkers?

The study combined single-cell RNA sequencing with bulk transcriptomic data, using WGCNA, differential expression analysis, and cell-type-specific expression patterns, then applied LASSO, Random Forest, and SVM-RFE for candidate screening.

What biomarker was validated for the AS to HF transition?

CD48 was consistently identified as a robust signature gene and validated as a potential biomarker across independent datasets.

Aug 6, 2026

Upload by Violet