Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Apr 26:9:724262.
doi: 10.3389/fcvm.2022.724262. eCollection 2022.

Upregulation of the Long Non-coding RNA LINC01480 Is Associated With Immune Infiltration in Coronary Artery Disease Based on an Immune-Related lncRNA-mRNA Co-expression Network

Ting Xiong  1   2 , Botao Xiao  1 , Yueheng Wu  3 , Yunfeng Liu  1 , Quhuan Li  1   2
Affiliations

Upregulation of the Long Non-coding RNA LINC01480 Is Associated With Immune Infiltration in Coronary Artery Disease Based on an Immune-Related lncRNA-mRNA Co-expression Network

Ting Xiong et al. Front Cardiovasc Med. .

Abstract

Coronary artery disease (CAD) is considered one of the leading causes of death worldwide. Although dysregulation of long non-coding RNAs (lncRNAs) has been reported to be associated with the initiation and progression of CAD, the knowledge regarding their specific functions as well their physiological/pathological significance in CAD is very limited. In this study, we aimed to systematically analyze immune-related lncRNAs in CAD and explore the relationship between key immune-related lncRNAs and the immune cell infiltration process. Based on differential expression analysis of mRNAs and lncRNAs, an immune-related lncRNA-mRNA weighted gene co-expression network containing 377 lncRNAs and 119 mRNAs was constructed. LINC01480 and AL359237.1 were identified as the hub immune-related lncRNAs in CAD using the random forest-recursive feature elimination and least absolute shrinkage and selection operator logistic regression. Furthermore, 93 CAD samples were divided into two subgroups according to the expression values of LINC01480 and AL359237.1 by consensus clustering analysis. By performing gene set enrichment analysis, we found that cluster 2 enriched more cardiovascular risk pathways than cluster 1. The immune cell infiltration analysis of ischemic cardiomyopathy (ICM; an advanced stage of CAD) samples revealed that the proportion of macrophage M2 was upregulated in the LINC01480 highly expressed samples, thus suggesting that LINC01480 plays a protective role in the progression of ICM. Based on the findings of this study, lncRNA LINC01480 may be used as a novel biomarker and therapeutic target for CAD.

Keywords: atherosclerosis; coronary artery disease; immune molecule; ischemic cardiomyopathy; long noncoding RNA.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Flowchart of the data analysis. The analytical procedure for step1 to step3 was performed based on the microarray dataset [GSE113079, derived from PBMCs of coronary artery disease (CADs) and healthy controls], and the analysis of step4 was based on the RNAseq datasets (GSE48166, GSE116250, GSE46224, GSE120825, derived from left ventricles of ICMs and healthy controls). CAD, coronary artery disease; ICM, ischemic cardiomyopathy; DELncs, differentially expressed long non-coding RNAs; DEIRGs, differentially expressed immune-related genes; lncRNA, long non-coding RNA; mRNA, messenger RNA; RF-RFE, random forest-recursive feature elimination; LASSO, the least absolute shrinkage and selection operator; PCA, principal component analysis; Hclust, hierarchical clustering; GSEA, gene set enrichment analysis; GSVA, gene set variable analysis; RNAseq, RNA sequencing.
FIGURE 2
FIGURE 2
Identification of aberrantly expressed molecules. Volcano plots of DELncs (A) and DEMs (B) in the CAD samples and controls. Heatmaps of DELncs (C) and DEMs (D). Brown and green points correspond to upregulated and downregulated molecules, respectively, and black points indicate that there are no differences in the expression levels of the molecules. DELncs, differentially expressed lncRNAs; DEMs, differentially expressed mRNAs; CAD, coronary artery disease.
FIGURE 3
FIGURE 3
Potential biological pathways of DEIRGs in CAD. (A) Venn diagram of the intersections between the DEMs and IRGs and the heatmap of DEIRGs. (B) The network of enriched terms of upregulated DEIRGs (C) and downregulated DEIRGs (D) via the Metascape online website; nodes of the same color belong to the same term. DEIRGs: differentially expressed immune-related genes; CAD, coronary artery disease; DEMs, differentially expressed mRNAs; IRGs, immune-related genes.
FIGURE 4
FIGURE 4
The immune-related lncRNA-mRNA weighted gene co-expression network in CAD. (A) Selection of the soft threshold to analyze the network topology; if a soft-threshold power of 3 is selected, the appropriate scale-free fit index can be attained. (B) The immune-related lncRNA-mRNA weighted co-expression network including 496 network nodes (377 lncRNAs and 119 mRNAs). Orange nodes that denote lncRNAs and green nodes that denote mRNAs. CAD, coronary artery disease; lncRNA, long non-coding RNA.
FIGURE 5
FIGURE 5
Screening the hub immune-related lncRNAs. (A) RF-RFE algorithm to screen the immune-related lncRNAs; at the highest accuracy level, the minimum number of variables was 11. (B) The minimum number of variables was 16 based on Bootstrap. (C) 10-fold cross-validation for the penalty parameters-lambda selection in the LASSO logistic regression model; 12 variables were reserved when lambda.min was selected. (D) Venn diagram showing the intersections between the hub immune-related lncRNAs obtained by the three algorithms. LASSO, the least absolute shrinkage and selection operator; lncRNAs, long non-coding RNAs; RF-RFE, random forest-recursive feature elimination.
FIGURE 6
FIGURE 6
Consensus clustering analysis of the hub immune-related lncRNAs in the CAD samples. (A) The heatmap of the consensus matrix with a cluster count of 2. (B) Two-dimensional PCA cluster plot of the CAD samples; green points denote the samples of cluster 1 and yellow triangles denote to the samples of cluster 2. (C) Unsupervised hierarchical clustering. Clustering was performed for 93 CAD samples based on the expression values of LINC01480 and AL359237.1. The cluster 1 containing 59 samples is colored in blue, and cluster 2 including 34 samples is colored in purple. lncRNA, long non-coding RNA; CAD, coronary artery disease; PCA, principal component analysis; Hclust, hierarchical clustering.
FIGURE 7
FIGURE 7
The Gene set enrichment analysis (GSEA) of different subgroup in the CAD samples. GSEA was used to explore the differences between the biological pathways of cluster 1 (A) and cluster 2 (B); x-axis represents the NES and the asterisks denote the pathways associated with cardiovascular disease. In addition, the unique aberrant pathways were marked in red in cluster 2 compared to cluster 1. GSEA, gene set enrichment analysis; NES, normalized enrichment score.
FIGURE 8
FIGURE 8
Correlation of the proportion of infiltrating immune cell with the expression of LINC01480. (A) LINC01480 showed significant upregulation both in CAD dataset (GSE113079) and ICM datasets (GSE_merge, matrix of the merged and removing batch effects of GSE48166, GSE116250, GSE46224, and GSE120825). (B) Violin plot showing the ratio of differentiation of 22 kinds of immune cells among the ICM samples with low or high expression levels of LINC01480 relative to the median of LINC01480 expression level; Wilcoxon rank-sum test was used as the significance test. The red mark indicates the difference in the infiltration between the two groups of samples. (C) Spearman’s correlation analysis of LINC01480 and the infiltrating immune cells; immune cells with statistically significant were marked in red color with P < 0.05. (D) Spearman’s correlation analysis of LINC01480 and GSVA scores of the KEGG pathways; the statistically significant pathways were visualized with P < 0.01. CAD, coronary artery disease; ICM, ischemic cardiomyopathy; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSVA, gene set variable analysis. 0.001 < **P < 0.01, ***P < 0.001.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Wong ND. Epidemiological studies of CHD and the evolution of preventive cardiology. Nat Rev Cardiol. (2014). 11:276–89. 10.1038/nrcardio.2014.26 - DOI - PubMed
    1. Álvarez-Álvarez MM, Zanetti D, Carreras-Torres R, Moral P, Athanasiadis G. A survey of sub-Saharan gene flow into the Mediterranean at risk loci for coronary artery disease. Eur J Hum Genet. (2017) 25:472–476. 10.1038/ejhg.2016.200 - DOI - PMC - PubMed
    1. Boudoulas KD, Triposciadis F, Geleris P, Boudoulas H. Coronary atherosclerosis: pathophysiologic basis for diagnosis and management. Prog Cardiovasc Dis. (2016) 58:676–92. 10.1016/j.pcad.2016.04.003 - DOI - PubMed
    1. Pantely G, Bristow JD. Ischemic cardiomyopathy. Prog Cardiovasc Dis. (1984) 27:95–114. 10.1016/0033-0620(84)90021-5 - DOI - PubMed
    1. Sekulic M, Zacharias M, Medalion B. Ischemic cardiomyopathy and heart failure. Cir Heart Fail. (2019) 12:e006006. 10.1161/CIRCHEARTFAILURE.119.006006 - DOI - PubMed