IMR Press / RCM / Volume 23 / Issue 7 / DOI: 10.31083/j.rcm2307247
Open Access Original Research
Construction of Prediction Model for Atrial Fibrillation with Valvular Heart Disease Based on Machine Learning
Qiaoqiao Li1,2,†Shenghong Lei1,2,†Xueshan Luo1,2Jintao He1,2Yuan Fang1,2Hui Yang1,2Yang Liu1,2Chun-Yu Deng1,2Shulin Wu1,2Yu-Mei Xue1,2,*Fang Rao1,2,*
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1 Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 510080 Guangzhou, Guangdong, China
2 Research Center of Medical Sciences, Provincial Key Laboratory of Clinical Pharmacology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 510080 Guangzhou, Guangdong, China
*Correspondence: xueyumei@gdph.org.cn (Yu-Mei Xue); raofang@gdph.org.cn (Fang Rao)
These authors contributed equally.
Academic Editor: Carmela Rita Balistreri
Rev. Cardiovasc. Med. 2022, 23(7), 247; https://doi.org/10.31083/j.rcm2307247
Submitted: 31 March 2022 | Revised: 31 May 2022 | Accepted: 10 June 2022 | Published: 28 June 2022
(This article belongs to the Special Issue Promising Novel Biomarkers in Cardiovascular Diseases)
Copyright: © 2022 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.
Abstract

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.

Keywords
atrial fibrillation
valvular heart disease
WGCNA
machine leaning
specific markers
Figures
Fig. 1.
Funding
81870254/National Natural Science Foundation of China
81670314/National Natural Science Foundation of China
DFJH201808/High-level Hospital Construction Plan
DFJH201925/High-level Hospital Construction Plan
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