Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular Endothelial Cells.
Yaochen XuQinglan MaJingxin RenLei ChenWei GuoKaiyan FengZhenbing ZengTao HuangYu-Dong CaiPublished in: Life (Basel, Switzerland) (2023)
Corona Virus Disease 2019 (COVID-19) not only causes respiratory system damage, but also imposes strain on the cardiovascular system. Vascular endothelial cells and cardiomyocytes play an important role in cardiac function. The aberrant expression of genes in vascular endothelial cells and cardiomyocytes can lead to cardiovascular diseases. In this study, we sought to explain the influence of respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on the gene expression levels of vascular endothelial cells and cardiomyocytes. We designed an advanced machine learning-based workflow to analyze the gene expression profile data of vascular endothelial cells and cardiomyocytes from patients with COVID-19 and healthy controls. An incremental feature selection method with a decision tree was used in building efficient classifiers and summarizing quantitative classification genes and rules. Some key genes, such as MALAT1, MT-CO1, and CD36, were extracted, which exert important effects on cardiac function, from the gene expression matrix of 104,182 cardiomyocytes, including 12,007 cells from patients with COVID-19 and 92,175 cells from healthy controls, and 22,438 vascular endothelial cells, including 10,812 cells from patients with COVID-19 and 11,626 cells from healthy controls. The findings reported in this study may provide insights into the effect of COVID-19 on cardiac cells and further explain the pathogenesis of COVID-19, and they may facilitate the identification of potential therapeutic targets.
Keyphrases
- endothelial cells
- high glucose
- sars cov
- coronavirus disease
- respiratory syndrome coronavirus
- gene expression
- machine learning
- genome wide
- bioinformatics analysis
- dna methylation
- deep learning
- vascular endothelial growth factor
- high resolution
- cardiovascular disease
- big data
- oxidative stress
- heart failure
- risk assessment
- left ventricular
- metabolic syndrome
- cell death
- transcription factor
- long non coding rna
- cardiovascular risk factors