Uncovering key biomarkers, potential therapeutic targets and development of deep learning model in heart failure
Ming Du, Shuang He, Jiaojiao Liu, Long Yuan
Abstract
Heart failure (HF) represents a significant public health concern, characterized by elevated rates of mortality and morbidity. Recent advancements in gene sequencing technologies have led to the identification of numerous genes associated with heart failure. By utilizing available gene expression data from the Gene Expression Omnibus (GEO) database, we conducted a screening for differentially expressed genes (DEGs) related to heart failure.
Introduction
Despite significant advances in understanding the risk factors associated with incident heart failure (HF), research indicates that HF continues to represent a substantial clinical and public health challenge [1]. While lifestyle modifications, secondary prevention strategies for coronary heart disease, and effective blood pressure control contribute to HF management, there are currently no more effective primary prevention interventions for HF [2,3]. This emerging body of knowledge facilitates the identification of novel biological mechanisms linked to HF events and holds the potential to guide the development of innovative interventions aimed at the primary prevention of HF.
Materials and method
2.1 Data acquisition and processing
The dataset related to the disease was obtained from the GEO database [16]. For HF, we chose GSE17800 [17] and GSE57338 [18] as the main datasets. We selected GSE57338 as our primary dataset for HF analysis due to its extensive sample size, including 177 HF patients and 136 individuals without HF.
Results
3.1 Differential expression analysis
The gene expression matrices were acquired, which included 144 control samples and 217 HF disease samples. A two-dimensional PCA clustering diagram was employed to visualize the integrated gene expression matrix from the two datasets, both prior to and following the batch effect removal. The batch effects in the two HF gene datasets are evident, all samples in the dataset achieved acceptable homogeneity following PCA analysis (S1 Fig A and B).
Discussion
HF has become a rising public health challenge, marked by chronic cardiac dysfunction stemming from a variety of etiologies. Patients with HF experience a range of debilitating symptoms that significantly impair their quality of life [43,44]. In clinical practice, BNP remains the gold standard biomarker for diagnosing and assessing HF. Nonetheless, recent research has revealed certain limitations, as extremely low levels of BNP (<50 pg/ml) were noted in 4.9% of patients with HF, and a minor percentage (ranging from 0.1% to 1.1%) exhibited BNP levels that fell beneath detection thresholds [45].
Conclusion
In this study, we employed an integrated bioinformatics approach to identify key diagnostic genes and potential therapeutic compounds for HF. Specifically, we identified four candidate diagnostic genes (ASPN, ITIH5, ISLR, and FNDC1) associated with fibroblast activation and myocardial remodeling and two candidate compounds (pirinixic acid and resveratrol) targeting PPAR and SIRT1 pathways, respectively.
Citation: Du M, He S, Liu J, Yuan L (2025) Uncovering key biomarkers, potential therapeutic targets and development of deep learning model in heart failure. PLoS One 20(9): e0330780. https://doi.org/10.1371/journal.pone.0330780
Editor: Li Yang,, Sichuan University, CHINA
Received: March 3, 2025; Accepted: August 5, 2025; Published: September 3, 2025
Copyright: © 2025 Du et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files. Raw transcriptomic data were obtained from the Gene Expression Omnibus (GEO) public database under the following accession numbers: GSE17800, GSE57338, GSE29819, and GSE145154. These datasets are freely accessible at: - GSE17800: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17800-GSE57338: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57338-GSE29819: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29819-GSE145154: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE145154.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
