Association of OLR1 gene polymorphisms with the risk of coronary artery disease: A systematic review and meta-analysis

Salehipour, Pouya; Rezagholizadeh, Farzaneh; Mahdiannasser, Mojdeh; Kazerani, Reihane; Modarressi, Mohammad Hossein

doi:10.1016/j.hrtlng.2021.01.015

Cited by 15 publications

(10 citation statements)

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“…It is involved in the in ammatory reactions of macrophages, monocytes, neutrophils and other in ammatory cells and may be involved in the in ammatory reactions of the IA wall. OLR1 is mainly involved in atherosclerosis, which plays an important role in the pathogenesis of IA 34,35 . OSM has been reported in breast cancer, intestinal in ammation, lupus nephritis, systemic sclerosis, etc [47][48][49][50] .…”

Section: Discussionmentioning

confidence: 99%

“…MSR1 is expressed in macrophages, monocytes, neutrophils and other in ammatory cells and mediates in ammation through multiple pathways 32,33 . Gene polymorphism of OLR1 is closely related to blood glucose and lipid levels and the occurrence of cardiovascular and cerebrovascular diseases, and it also has a proin ammatory effect 34,35 . Oncostatin M (OSM) is a proin ammatory cytokine in the interleukin-6 (IL-6) family.…”

Section: Discussionmentioning

confidence: 99%

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Identifying the diagnostic value of inflammatory genes in intracranial aneurysms based on machine learning

Zhou

Liang

et al. 2023

Preprint

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Background Intracranial aneurysm (IA) is a cerebrovascular disease that can be caused by a variety of factors. Clinical trials have indicated that inflammation and inflammatory cells play critical roles in the pathophysiology of IA. Nonetheless, the roles of inflammation-related genes (IRGs) in IA remain unclear. Methods The GSE75436 and GSE54083 datasets were acquired from the Gene Expression Omnibus (GEO) database, and the IRGs were extracted from the MSigDB database. First, the two GEO datasets were combined, and the batch effects were removed. The differentially expressed inflammation-related genes (DEIRGs) were identified by overlapping the IRGs with the set of differentially expressed genes (DEGs) between IA and control samples. The functions of the DEIRGs were evaluated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Then, ROC curve analysis was used to verify the diagnostic ability of the DEIRGs, and diagnostic models were constructed with 7 machine learning methods. Furthermore, gene set enrichment analysis (GSEA) was performed to explore the potential biological functions of the biomarkers. Immune cell relevance was assessed by single-sample gene set enrichment analysis (ssGSEA). In addition, a TF–mRNA‒miRNA network was established, and potential biomarkers were predicted. Ultimately, the mRNA expression levels of the biomarkers were validated by quantitative real-time PCR (qRT‒PCR). Results In total, 35 DEIRGs were retrieved by overlapping the 964 DEGs and 200 IRGs. Functional enrichment analysis revealed that the DEIRGs were significantly enriched in the regulation of the inflammatory response, immune receptor activity, and lipid and atherosclerosis pathways. Moreover, 13 genes with an AUC greater than 0.85 were selected for diagnostic model construction by the RF algorithm, and 7 biomarkers were obtained in the final model. GSEA indicated that these 7 biomarkers were mainly associated with inflammation. The significantly differentially abundant immune cells exhibited positive correlations with the biomarkers. Subsequently, we proposed that SERPINE might be modulated by TBX3, MLX, and NR1I3 and that SLC11A2 might be modulated by hsa-miR-6838-5p, hsa-miR-4784, and hsa-miR-3663-5p. In addition, 22 drugs were predicted to interact with the biomarkers, including fluoxetine, aleplasinin, and orlistat. Finally, qRT‒PCR results showed that the expression levels of the 7 biomarkers were significantly higher in IA tissue than in superficial temporal artery tissue. Conclusion This research provides a new perspective for understanding the molecular mechanism of IA pathogenesis and valuable evidence for the pathological diagnosis of IRGs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identifying the diagnostic value of inflammatory genes in intracranial aneurysms based on machine learning

Zhou

Liang

et al. 2023

Preprint

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“…Oxidized low–density lipoprotein receptor 1 (OLR1) encodes a low–density lipoprotein receptor, which binds, internalizes, and degrades oxidized low–density lipoprotein. The previous meta–analysis demonstrated a significant association between OLR1 gene polymorphisms and CAD risk ( 35 ). OLR1 promotes endothelial dysfunction by inducing pro–atherogenic signaling via the endothelial uptake of oxidized LDL (oxLDL), which contributes to the initiation, progression, and destabilization of atheromatous plaques ( 36 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes as Early Warning Signals of Acute Myocardial Infarction Based on Weighted Gene Correlation Network Analysis and Dynamic Network Biomarker Algorithm

et al. 2022

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PurposeThe specific mechanisms and biomarkersunderlying the progression of stable coronary artery disease (CAD) to acute myocardial infarction (AMI) remain unclear. The current study aims to explore novel gene biomarkers associated with CAD progression by analyzing the transcriptomic sequencing data of peripheral blood monocytes in different stages of CAD.Material and MethodsA total of 24 age- and sex- matched patients at different CAD stages who received coronary angiography were enrolled, which included 8 patients with normal coronary angiography, 8 patients with angiographic intermediate lesion, and 8 patients with AMI. The RNA from peripheral blood monocytes was extracted and transcriptome sequenced to analyze the gene expression and the differentially expressed genes (DEG). A Gene Oncology (GO) enrichment analysis was performed to analyze the biological function of genes. Weighted gene correlation network analysis (WGCNA) was performed to classify genes into several gene modules with similar expression profiles, and correlation analysis was carried out to explore the association of each gene module with a clinical trait. The dynamic network biomarker (DNB) algorithm was used to calculate the key genes that promote disease progression. Finally, the overlapping genes between different analytic methods were explored.ResultsWGCNA analysis identified a total of nine gene modules, of which two modules have the highest positive association with CAD stages. GO enrichment analysis indicated that the biological function of genes in these two gene modules was closely related to inflammatory response, which included T-cell activation, cell response to inflammatory stimuli, lymphocyte activation, cytokine production, and the apoptotic signaling pathway. DNB analysis identified a total of 103 genes that may play key roles in the progression of atherosclerosis plaque. The overlapping genes between DEG/WGCAN and DNB analysis identified the following 13 genes that may play key roles in the progression of atherosclerosis disease: SGPP2, DAZAP2, INSIG1, CD82, OLR1, ARL6IP1, LIMS1, CCL5, CDK7, HBP1, PLAU, SELENOS, and DNAJB6.ConclusionsThe current study identified a total of 13 genes that may play key roles in the progression of atherosclerotic plaque and provides new insights for early warning biomarkers and underlying mechanisms underlying the progression of CAD.

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“…SNPs located in the OLR1 gene could have clinical significance and could be considered CAD candidate biomarkers, their identification being useful in assessing the genetic risk of CAD. The OLR1 gene has six non-coding SNPs, which form a haplotype [ 100 ]. In a meta-analysis, Salehipour et al [ 100 ] identified a significant association between SNPs rs1050283 (3′UTR*188 C>T) and rs3736235 (IVS4-14 A>G) located in OLR1 haplotype and the occurrence of CAD [ 100 ].…”

Section: Polygenic Cad: Genes and Polymorphisms Associated With Cadmentioning

confidence: 99%

“…The OLR1 gene has six non-coding SNPs, which form a haplotype [ 100 ]. In a meta-analysis, Salehipour et al [ 100 ] identified a significant association between SNPs rs1050283 (3′UTR*188 C>T) and rs3736235 (IVS4-14 A>G) located in OLR1 haplotype and the occurrence of CAD [ 100 ]. They suggested that the precise determination of CAD association with polymorphisms located in a haplotype requires the analysis of all SNPs located in that specific haplotype [ 100 ].…”

Section: Polygenic Cad: Genes and Polymorphisms Associated With Cadmentioning

confidence: 99%

Etiologic Puzzle of Coronary Artery Disease: How Important Is Genetic Component?

Butnariu

Florea

Bădescu

et al. 2022

Life

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In the modern era, coronary artery disease (CAD) has become the most common form of heart disease and, due to the severity of its clinical manifestations and its acute complications, is a major cause of morbidity and mortality worldwide. The phenotypic variability of CAD is correlated with the complex etiology, multifactorial (caused by the interaction of genetic and environmental factors) but also monogenic. The purpose of this review is to present the genetic factors involved in the etiology of CAD and their relationship to the pathogenic mechanisms of the disease. Method: we analyzed data from the literature, starting with candidate gene-based association studies, then continuing with extensive association studies such as Genome-Wide Association Studies (GWAS) and Whole Exome Sequencing (WES). The results of these studies revealed that the number of genetic factors involved in CAD etiology is impressive. The identification of new genetic factors through GWASs offers new perspectives on understanding the complex pathophysiological mechanisms that determine CAD. In conclusion, deciphering the genetic architecture of CAD by extended genomic analysis (GWAS/WES) will establish new therapeutic targets and lead to the development of new treatments. The identification of individuals at high risk for CAD using polygenic risk scores (PRS) will allow early prophylactic measures and personalized therapy to improve their prognosis.

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Association of OLR1 gene polymorphisms with the risk of coronary artery disease: A systematic review and meta-analysis

Cited by 15 publications

References 35 publications

Identifying the diagnostic value of inflammatory genes in intracranial aneurysms based on machine learning

Identifying the diagnostic value of inflammatory genes in intracranial aneurysms based on machine learning

Identification of Key Genes as Early Warning Signals of Acute Myocardial Infarction Based on Weighted Gene Correlation Network Analysis and Dynamic Network Biomarker Algorithm

Etiologic Puzzle of Coronary Artery Disease: How Important Is Genetic Component?

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