Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles.

Xu Yi,Zhiqiang Xu,Yujia Yang,Yuan Zhang,Yue Cai

doi:10.3389/fmolb.2021.628546

Abstract

Atherosclerotic cardiovascular disease (ASCVD) caused by atherosclerosis (AS) is one of the highest causes of mortality worldwide. Although there have been many studies on AS, its etiology remains unclear. In order to carry out molecular characterization of different types of AS, we retrieved two datasets composed of 151 AS samples and 32 normal samples from the Gene Expression Omnibus database. Using the non-negative matrix factorization (NMF) algorithm, we successfully divided the 151 AS samples into two subgroups. We then compared the molecular characteristics between the two groups using weighted gene co-expression analysis (WGCNA) and identified six key modules associated with the two subgroups. Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis were used to identify the potential functions and pathways associated with the modules. In addition, we used the cytoscape software to construct and visualize protein–protein networks so as to identify key genes in the modules of interest. Three hub genes including PTGER3, GNAI1, and IGFBP5 were further screened using the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Since the modules were associated with immune pathways, we performed immune cell infiltration analysis. We discovered a significant difference in the level of immune cell infiltration by naïve B cells, CD8 T cells, T regulatory cells (Tregs), resting NK cells, Monocytes, Macrophages M0, Macrophages M1, and Macrophages M2 between the two subgroups. In addition, we observed the three hub genes were positively correlated with Tregs but negatively correlated with Macrophages M0. We also found that the three key genes are differentially expressed between normal and diseased tissue, as well as in the different subgroups. Receiver operating characteristic (ROC) results showed a good performance in the validation dataset. These results may provide novel insight into cellular and molecular characteristics of AS and potential markers for diagnosis and targeted therapy.

Highlights

Atherosclerosis (AS) is a chronic inflammatory disease in which atherosclerotic plaques are deposited on the walls of blood vessels and induce arterial stenosis
These studies only concentrated on the characterization of potential biomarkers, and the samples size of the dataset is small, which leads to low accuracy
Considering that, we incorporated multiple data sets to identify the robust subgroups of AS to better understand the underlying molecular pathogenesis of AS

Summary

Introduction

Atherosclerosis (AS) is a chronic inflammatory disease in which atherosclerotic plaques are deposited on the walls of blood vessels and induce arterial stenosis. Atherosclerosis is usually asymptomatic in the early stages. Increased severity of the disease causes various diseases, such as coronary artery disease, peripheral artery disease, and cerebrovascular disease (Kong et al, 2014; Tern et al, 2018). It has been reported that there was sharp increase in the incidence of AS in 2017, with the AS-related mortality rising to 31% (Stefanadis et al, 2017). The AS is characterized by vascular inflammation, endothelial dysfunction, plaque formation, and diminished oxygen supply to target organs (Jeong, 2010). The underlying molecular mechanism of AS is still unclear, and an understanding of the molecular pathogenesis of atherosclerosis process could contribute to develop individualized treatment strategies for AS patients

Methods

Results

Conclusion