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Construction of Prediction Model for Atrial Fibrillation with Valvular Heart Disease Based on Machine Learning
Qiaoqiao Li1,2, Shenghong Lei1,2, Xueshan Luo1,2
1Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080 Guangzhou, Guangdong, China.
Insights
Valvular heart disease (VHD) leading to atrial fibrillation (AF) poses serious stroke risks. This study identifies key genes, like CSRP3, involved in AF-VHD progression, offering potential therapeutic targets.
Area of Science:
- Cardiovascular Biology
- Molecular Medicine
- Genomics
Background:
- Valvular heart disease (VHD) is a significant cause of atrial fibrillation (AF).
- Atrial fibrillation with VHD (AF-VHD) increases the risk of stroke and heart failure.
- Understanding the molecular mechanisms of AF-VHD is crucial for effective treatment.
Purpose of the Study:
- To investigate the molecular mechanisms underlying VHD progression to AF.
- To identify potential therapeutic targets for AF-VHD.
Main Methods:
- Analysis of public mRNA microarray datasets to identify differentially expressed genes (DEGs).
- Weighted gene correlation network analysis to detect key gene modules.
- Machine learning and bioinformatics tools to screen for candidate hub genes.
- Validation of gene expression in human atrial tissue samples.
Main Results:
- Identified 819 common DEGs and 14 significant gene modules.
- The cyan and purple modules were most associated with AF-VHD.
- Highlighted CSRP3, MCOLN3, SLC25A5, and FIBP as important genes.
- CSRP3 demonstrated potential clinical significance and cardiac-specific expression.
Conclusions:
- Identified key genes potentially driving AF-VHD pathogenesis.
- Provides novel insights into VHD progression to AF.
- Suggests CSRP3 and other identified genes as potential biomarkers and therapeutic targets for AF-VHD.
Background:
Valvular heart disease (VHD) is a major precipitating factor of atrial fibrillation (AF) that contributes to decreased cardiac function, heart failure, and stroke. Stroke induced by VHD combined with atrial fibrillation (AF-VHD) is a much more serious condition in comparison to VHD alone. The aim of this study was to explore the molecular mechanism governing VHD progression and to provide candidate treatment targets for AF-VHD.
Methods:
Four public mRNA microarray datasets were downloaded and differentially expressed genes (DEGs) screening was performed. Weighted gene correlation network analysis was carried out to detect key modules and explore their relationships and disease status. Candidate hub signature genes were then screened within the key module using machine learning methods. The receiver operating characteristic curve and nomogram model analysis were used to determine the potential clinical significance of the hub genes. Subsequently, target gene protein levels in independent human atrial tissue samples were detected using western blotting. Specific expression analysis of the hub genes in the tissue and cell samples was performed using single-cell sequencing analysis in the Human Protein Atlas tool.
Results:
A total of 819 common DEGs in combined datasets were screened. Fourteen modules were identified using the cut tree dynamic function. The cyan and purple modules were considered the most clinically significant for AF-VHD. Then, 25 hub genes in the cyan and purple modules were selected for further analysis. The pathways related to dilated cardiomyopathy, hypertrophic cardiomyopathy, and heart contraction were concentrated in the purple and cyan modules of the AF-VHD. Genes of importance (CSRP3, MCOLN3, SLC25A5, and FIBP) were then identified based on machine learning. Of these, CSRP3 had a potential clinical significance and was specifically expressed in the heart tissue.
Conclusions:
The identified genes may play critical roles in the pathophysiological process of AF-VHD, providing new insights into VHD development to AF and helping to determine potential biomarkers and therapeutic targets for treating AF-VHD.
