Identification of Key lncRNAs Associated With Atherosclerosis Progression Based on Public Datasets

Abstract

Atherosclerosis is one of the most common type of cardiovascular disease and the prime cause of mortality in the aging population worldwide. However, the detail mechanisms and special biomarkers of atherosclerosis remain to be further investigated. Lately, long non-coding RNAs (lncRNAs) has attracted much more attention than other types of ncRNAs. In our work, we found and confirmed differently expressed lncRNAs and mRNAs in atherosclerosis by analyzing GSE28829. We performed the weighted gene co-expression network analysis (WGCNA) by analyzing GSE40231 to confirm highly correlated genes. Gene Ontology (GO) analysis were utilized to assess the potential functions of differential expressed lncRNAs in atherosclerosis. Co-expression networks were also constructed to confirm hub lncRNAs in atherosclerosis. A total of 5784 mRNAs and 654 lncRNAs were found to be dysregulated in the progression of atherosclerosis. A total of 15 lncRNA-mRNA co-expression modules were identified in this study based on WGCNA analysis. Moreover, a few lncRNAs, such as ZFAS1, LOC100506730, LOC100506691, DOCK9-AS2, RP11-6I2.3, LOC100130219, were confirmed as important lncRNAs in atherosclerosis. Taken together, bioinformatics analysis revealed these lncRNAs were involved in regulating the leukotriene biosynthetic process, gene expression, actin filament organization, t-circle formation, antigen processing, and presentation, interferon-gamma-mediated signaling pathway, and activation of GTPase activity. We believed that this study would provide potential novel therapeutic and prognostic targets for atherosclerosis.

Keywords: WGCNA analysis; atherosclerosis; biomarker; co-expression analysis; long non-coding RNA.

FIGURE 1

Identification of atherosclerosis progression related mRNAs and lncRNA. (A,B) Hierarchical clustering analysis shows differential mRNAs (A) and lncRNAs (B) expression in advanced atherosclerosis samples compared to early atherosclerosis samples by using GSE28829.

FIGURE 2

Result of weighted gene correlation network analysis (WGCNA) analysis. (A,B) Cluster result and trait heatmap of data samples (A) and determination of parameter β of the adjacency function in the WGCNA algorithm (B). (C) The scale independence of WGCNA analysis. (D) The mean connectivity of WGCNA analysis.

FIGURE 3

Result of atherosclerosis related lncRNA-mRNA co-expression networks. Construction of module 7–12 lncRNA-mRNA co-expression networks based on WCGNA analysis. (A–F) The co-expression networks of module 1 (A), module 2 (B), module 3 (C), module 4 (D), module 5 (E), and module 6 (F).

FIGURE 4

Result of atherosclerosis related lncRNA-mRNA co-expression networks. Construction of module 7–12 lncRNA-mRNA co-expression networks based on WCGNA analysis. (A–F) The co-expression networks of module 7 (A), module 8 (B), module 9 (C), module 10 (D), module 11 (E), and module 12 (F).

FIGURE 5

Result of atherosclerosis related lncRNA-mRNA co-expression networks. Construction of module 13–15 lncRNA-mRNA co-expression networks based on WCGNA analysis. The co-expression networks of module 13 (A), module 14 (B), and module 15 (C).

FIGURE 6

Function annotation of atherosclerosis related lncRNAs. Gene Ontology (GO) analysis shows lncRNAs in module 1 (A), module 2 (B), module 3 (C), module 4 (D), module 5 (E), module 6 (F), and module 9 (G) regulate multiple biological processes.