Long Noncoding RNAs in the Progression of Atherosclerosis: An Integrated Analysis Based on Competing Endogenous RNA Theory

Role of neutrophil extracellular trap and immune infiltration in atherosclerotic plaque instability: Novel insight from bioinformatics analysis and machine learning

The instability of atherosclerotic plaques increases the risk of acute coronary syndrome. Neutrophil extracellular traps (NETs), mesh-like complexes consisting of extracellular DNA adorned with various protein substances, have been recently discovered to play an essential role in atherosclerotic plaque formation and development. This study aimed to investigate novel diagnostic biomarkers that can identify unstable plaques for early distinction and prevention of plaque erosion or disruption. Differential expression analysis was used to identify the differentially expressed NET-related genes, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed. We filtered the characteristic genes using machine learning and estimated diagnostic efficacy using receiver operating characteristic curves. Immune infiltration was detected using single-sample gene set enrichment analysis and the biological signaling pathways involved in characteristic genes utilizing gene set enrichment analysis were explored. Finally, miRNAs- and transcription factors-target genes networks were established. We identified 8 differentially expressed NET-related genes primarily involved in immune-related pathways. Four were identified as capable of distinguishing unstable plaques. More immune cells infiltrated unstable plaques than stable plaques, and these cells were predominantly positively related to characteristic genes. These 4 diagnostic genes are involved in immune responses and the modulation of smooth muscle contractility. Several miRNAs and transcription factors were predicted as upstream regulatory factors, providing further information on the identification and prevention of atherosclerotic plaques rupture. We identified several promising NET-related genes (AQP9, C5AR1, FPR3, and SIGLEC9) and immune cell subsets that may identify unstable atherosclerotic plaques at an early stage and prevent various complications of plaque disruption.

Identification of hub genes and regulatory networks in histologically unstable carotid atherosclerotic plaque by bioinformatics analysis

Objective

This study identified underlying genetic molecules associated with histologically unstable carotid atherosclerotic plaques through bioinformatics analysis that may be potential biomarkers and therapeutic targets.

Methods

Three transcriptome datasets (GSE41571, GSE120521 and E-MTAB-2055) and one non-coding RNA dataset (GSE111794) that met histological grouping criteria of unstable plaque were downloaded. The common differentially expressed genes (co-DEGs) of unstable plaques identified from three mRNA datasets were annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG). A protein–protein interaction (PPI) network was constructed to present the interaction between co-DEGs and screen out hub genes. MiRNet database and GSE111794 dataset were used to identify the miRNAs targeting hub genes. Associated transcription factors (TFs) and drugs were also predicted. These predicted results were used to construct miRNA/TFs-hub gene and drug-hub gene regulatory networks.

Results

A total of 105 co-DEGs were identified, including 42 up-regulated genes and 63 down-regulated genes, which were mainly enriched in collagen-containing extracellular matrix, focal adhesion, actin filament bundle, chemokine signaling pathway and regulates of actin cytoskeleton. Ten hub genes (up-regulated: HCK, C1QC, CD14, FCER1G, LCP1 and RAC2; down-regulated: TPM1, MYH10, PLS3 and FMOD) were screened. HCK and RAC2 were involved in chemokine signaling pathway, MYH10 and RAC2 were involved in regulation of actin cytoskeleton. We also predicted 12 miRNAs, top5 TFs and 25 drugs targeting hub genes. In the miRNA/TF-hub gene regulatory network, PLS3 was the most connected hub genes and was targeted by six miRNAs and all five screened TFs. In the drug-hub gene regulatory network, HCK was targeted by 20 drugs including 10 inhibitors.

Conclusions

We screened 10 hub genes and predicted miRNAs and TFs targeting them. These molecules may play a crucial role in the progression of histologically unstable carotid plaques and serve as potential biomarkers and therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01257-1.

Exosomes Derived from Mesenchymal Stem Cells Ameliorate the Progression of Atherosclerosis in ApoE-/- Mice via FENDRR

Exosomes (EXO) are extracellular vesicles with lipid bilayer membrane structure containing noncoding RNA, DNA, and other molecules which mediate biological functions. The importance of EXO derived from mesenchymal stem cells (MSCs) has been underlined in cardiovascular diseases. However, the functional role of long non-coding RNA (lncRNA) released by MSCs-EXO on atherosclerosis (AS) was unknown. We aimed to investigate the effects of lncRNA fetal-lethal non-coding developmental regulatory RNA (FENDRR) released from MSC-derived EXO on AS. The accumulation of oxidized low-density lipoprotein (oxLDL) caused AS in mice and damage to human vascular endothelial cells (HUV-EC-C). MSC-EXO restored HUV-EC-C activity and alleviated arterial injury. LncRNA microarrays revealed that FENDRR was delivered to cells and tissues by MSC-EXO. FENDRR bound to microRNA (miR)-28 to regulate TEA domain transcription factor 1 (TEAD1) expression. Moreover, FENDRR knockdown exacerbated cell injury and arterial injury in mice. miR-28 inhibitor reversed the effects of FENDRR silencing and reduced atherosclerotic plaque formation. While loss of TEAD1 mitigated the effect of miR-28 inhibitor and accentuated HUV-EC-C injury in vitro and AS symptoms in vivo. Our results demonstrated that MSC-EXO secreted FENDRR to treat AS. FENDRR competed with TEAD1 to bind to miR-28, thereby reducing HUV-EC-C injury and atherosclerotic plaque formation.

Long non-coding RNA OIP5-AS1 (Cyrano): A context-specific regulator of normal and disease processes

Long non-coding (lnc) RNAs have been implicated in a plethora of normal biological functions, and have also emerged as key molecules in various disease processes. OIP5-AS1, also commonly known by the alias Cyrano, is a lncRNA that displays broad expression across multiple tissues, with significant enrichment in particular contexts including within the nervous system and skeletal muscle. Thus far, this multifaceted lncRNA has been found to have regulatory functions in normal cellular processes including cell proliferation and survival, as well as in the development and progression of a myriad disease states. These widespread effects on normal and disease states have been found to be mediated through context-specific intermolecular interactions with dozens of miRNAs and proteins identified to date. This review explores recent studies to highlight OIP5-AS1's contextual yet pleiotropic roles in normal homeostatic functions as well as disease oetiology and progression, which may influence its utility in the generation of future theranostics.

The Integrative Analysis of Competitive Endogenous RNA Regulatory Networks in Coronary Artery Disease

Background: Coronary artery disease (CAD) is the leading cause of cardiovascular death. The competitive endogenous RNAs (ceRNAs) hypothesis is a new theory that explains the relationship between lncRNAs and miRNAs. The mechanism of ceRNAs in the pathological process of CAD has not been fully elucidated. The objective of this study was to explore the ceRNA mechanism in CAD using the integrative bioinformatics analysis and provide new research ideas for the occurrence and development of CAD.

Methods: The GSE113079 dataset was downloaded, and differentially expressed lncRNAs (DElncRNAs) and genes (DEGs) were identified using the limma package in the R language. Weighted gene correlation network analysis (WGCNA) was performed on DElncRNAs and DEGs to explore lncRNAs and genes associated with CAD. Functional enrichment analysis was performed on hub genes in the significant module identified via WGCNA. Four online databases, including TargetScan, miRDB, miRTarBase, and Starbase, combined with an online tool, miRWalk, were used to construct ceRNA regulatory networks.

Results: DEGs were clustered into ten co-expression modules with different colors using WGCNA. The brown module was identified as the key module with the highest correlation coefficient. 188 hub genes were identified in the brown module for functional enrichment analysis. DElncRNAs were clustered into sixteen modules, including seven modules related to CAD with the correlation coefficient more than 0.5. Three ceRNA networks were identified, including OIP5-AS1-miR-204-5p/miR-211-5p-SMOC1, OIP5-AS1-miR-92b-3p-DKK3, and OIP5-AS1-miR-25-3p-TMEM184B.

Conclusion: Three ceRNA regulatory networks identified in this study may play crucial roles in the occurrence and development of CAD, which provide novel insights into the ceRNA mechanism in CAD.

Comprehensive analysis of dysregulated genes associated with atherosclerotic plaque destabilization

Atherosclerotic plaque destabilization is a dominating cause of acute cardiovascular events such as myocardial infarction and stroke. This study aims to identify genetic biomarkers related to atherosclerotic plaque destabilization using bioinformatics. Three transcriptome datasets of human carotid atherosclerotic plaque samples were downloaded from ArrayExpress and Gene Expression Omnibus databases, including E-MATB-2055, E-TABM-190, and GSE120521. With Robust Rank Aggregation analysis, we documented 46 differentially expressed genes between stable and unstable/ruptured plaques. Functional enrichment analysis using DAVID tool demonstrated that these genes were mainly related to biological functions such as extracellular matrix disassembly, collagen catabolic process, response to mechanical stimulus, and PPAR signaling pathway. A protein-protein interaction network for the differentially expressed genes was constructed, and eight pivotal genes (ITGAM, MMP9, PLAUR, CCR1, CD163, CD36, ADAM8, and IL1RN) were obtained from the network with a connective degree > 5. The expression patterns of these hub differentially expressed genes could be verified in atherosclerotic plaque samples with intraplaque hemorrhage. Using gene set variation analysis, the eight genes were integrated to generate an atherosclerotic plaque destabilization score, which showed a high performance in not only discriminating individuals with myocardial infarction from those with stable coronary illness, but also in predicting future acute cardiovascular events in atherosclerotic patients. In conclusion, the findings of this study will enhance our knowledge on the pathological mechanisms involved in atherosclerotic plaque destabilization, and provide potential gene biomarkers for risk stratification of patients with atherosclerotic cardiovascular disease.