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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.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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 \nChildren ’s Hospital of Philadelphia, United States  \nREVIEWED BY  \nAshwini Kumar Ray, University of Delhi, India Huanxin 丁,  \nShandong Provincial Qianfoshan Hospital, China  \n*CORRESPONDENCE  \nLiankai Wang,  \n [wanglk_minda@163.com](wanglk_minda@163.com)[ ](wanglk_minda@163.com)Lihua Ni,  \n [nilihua@whu.edu.cn](nilihua@whu.edu.cn),  \n [nilihua2016@163.com](nilihua2016@163.com)  \n†These authors have contributed equally to this work  \nRECEIVED 11 March 2025  \nACCEPTED 04 August 2025  \nPUBLISHED 12 September 2025  \nCITATION  \nNi 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.  \nFront. Genet. 16:1587274 .  \ndoi: 10.3389/fgene.2025.1587274  \nCOPYRIGHT  \n© 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.  \nDissecting and validation the biomarker of heart failure progression in patients with atherosclerosis by single-cell sequencing, bioinformatics, and machine learning  \nLihua Ni 1*†, Huabo Li 2,3†, Juan Du 2,3, Ke Zhou 2,3, Fugui Zhang 2,3 and Liankai Wang 2,3*  \n1Department 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  \nObjective: 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.  \nMethod: 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 .  \nResult: 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.  \nConclusion: 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.  \nKEYWORDS  \nHeart failure progression, atherosclerosis, single-cell sequencing, bioinformatics, machine learning  \nFrontiers in Genetics 01 [frontiersin.org](frontiersin.org)  \n1 I","cbCaiaOciT6cNiCm","https://ap.wps.com/l/cbCaiaOciT6cNiCm","pdf",4483667,15,"English","# Objective\n# Methods\n## Data sources and preprocessing\n## Biomarker screening and validation\n# Results\n# Conclusion\n# Keywords","[{\"question\":\"What was the main objective of the study?\",\"answer\":\"To identify early biomarkers associated with the progression from atherosclerosis to heart failure and explore underlying mechanisms.\"},{\"question\":\"Which data types and analysis methods were used to screen biomarkers?\",\"answer\":\"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.\"},{\"question\":\"What biomarker was validated for the AS to HF transition?\",\"answer\":\"CD48 was consistently identified as a robust signature gene and validated as a potential biomarker across independent datasets.\"}]","Dissecting and validating the biomarker of heart failure progression in patients with atherosclerosis - single-cell sequencing, bioinformatics, and machine learning | PDF",38]