Clinical assessment and molecular mechanism of the upregulation of Toll-like receptor 2 (TLR2) in myocardial infarction

Li, Mingjie; Yan, Shi-Bai; Dong, Hao; Huang, Zhi‐Guang; Li, Dongming; Tang, Yunhua; Pan, Yan-Fang; Yang, Zhen; Pan, Hong-Bo; Chen, Gang

doi:10.1186/s12872-022-02754-y

Cited by 6 publications

(3 citation statements)

References 52 publications

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“…CD274 [175], CEACAM1 [176], STAT1 [177], ARG1 [178], TLR4 [179], LRRK2 [180], ABCA1 [181], IFIH1 [182], TLR5 [183], PTGS2 [184], CYP2D6 [185], RNF213 [186], C9ORF72 [187], JAK2 [188], TLR8 [189], NOTCH2 [190], PDGFC (platelet derived growth factor C) [191], TLR2 [192], PRKAB2 [193], HDAC9 [194], NCOA4 [195], LATS2 [196], DICER1 [197], IL1RN [198], GCH1 [148], EGR1 [199], HIPK3 [200], ZEB2 [201], HIF1A [202], PLA2G7 [203], DOCK8 [204], CCR2 [205], PPP1R15B [206], GCLC (glutamate-cysteine ligase catalytic subunit) [207], VEGFA (vascular endothelial growth factor A) [208], SORL1 [209], OGT (O-linked N-acetylglucosamine (GlcNAc) transferase) [210], EIF2AK3 [211], SLC40A1 [212], PHLPP1 [213], IGF2R [214], LPIN2 [215], SIRT1 [216], VPS13C [217], ACSL4 [218], ELOVL6 [219], FGL2 [220], ERO1B [221], XAF1 [222], TET2 [223], TET3 [224], PF4 [225], POU2F1 [226], PC (pyruvate carboxylase) [227], NDUFS8 [228], PRDX2 [229], RUNX3 [230], HSPB1 [231], TCF4 [169], TCF7 [232], IGFBP4 [233], HSPG2 [234], NMRAL1 [173], AQP3 [235] and LGALS3 [236] might be a potential therapeutic target for diabetes mellitus. Studies have found that CEACAM1 [237], STAT1 [238], ARG1 [239], TLR4 [240], LRRK2 [241], ABCA1 [242], PTGS2 [243], CYP2D6 [244], JAK2 [245], TLR2 […”

Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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Coronary artery disease (CAD) is the most common cardiovascular disorder and the leading cause of heart related deaths in world. Increasing molecular targets have been discovered for CAD and CAD - related complications prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might help the diagnosis and treatment of CAD and CAD related complications. We searched next generation sequencing (NGS) dataset (GSE202625) and identified differentially expressed genes (DEGs) by comparing CAD and normal control samples using DESeq2. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g:Profiler online database. The protein protein interaction (PPI) network was plotted with IMEx interactome and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. MiRNA hub gene regulatory network and TF hub gene regulatory network analysis was performed to identify the hub genes, miRNAs and TFs. Receiver operating characteristic (ROC) curve analysis was used to predict the diagnostic effectiveness of the hub genes. A total of 118 DEGs (479 up regulated genes and 479 down regulated genes) were detected. The GO enrichment analysis indicated that the DEGs most significantly enriched in cellular response to stimulus and biosynthetic process. The REACTOME pathway enrichment analysis revealed that the DEGs were most significantly enriched in immune system and eukaryotic translation elongation. PPI network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network analysis demonstrated that EGR1, SIRT1, STAT1, LRRK2, HIF1A, CSNK2B, RPS3, RPS2, RPS4X and HDAC11 were the hub genes. On the whole, the findings of this study enhance our understanding of the potential molecular mechanisms of CAD and CAD-related complications, and provide potential targets for further research.

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Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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“…Besides, it has also been found that TLR2, together with NETs, may play a crucial role in plaque erosion through the induction of endoplasmic reticulum stress and apoptosis (34). Moreover, the upregulation of TLR2 may have a favorable screening value for AMI (35).…”

Section: Discussionmentioning

confidence: 99%

Identifying potential biomarkers in acute myocardial infarction based on neutrophil extracellular traps associated genes

Cao

Wang

et al. 2023

Preprint

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Background Acute myocardial infarction (AMI) represents one of the major critical cardiovascular disorders due to its high mortality and morbidity. Neutrophil extracellular traps(NETs) are essential throughout the thrombotic process of AMI. However, genes associated with NETs in AMI have not been fully described.Methods NETs-associated gene candidates were identified by literature review. AMI-associated datasets(GSE66360) were retrieved from Gene Expression Omnibus (GEO) database. Differentially expressed NETs-associated genes were subjected to Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis. The marker genes were subsequently selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms and calculated based on the receiver operating characteristic (ROC) curve. To further probe the potential features of these marker genes, single-gene gene set enrichment analysis (GSEA) was performed. To further discuss immune microenvironment modulations, immune infiltration analysis was performed by CIBERSORT algorithms. Accordingly, an mRNA-miRNA-lncRNA network was constructed. Finally, gene expression levels of these marker gene were verified according to an external dataset (GSE66145).Results Forty-five differentially expressed NETs-associated genes were screened out from the GSE66360 dataset, which was closely linked to myeloid leukocyte activation and inflammatory response. FCAR, LILRB2, PDE4B, S100A12, DNASE1, IL1B, IL6, MMP9, and TLR2 were identified as marker genes. The AUC of marker genes was higher than 0.6 and the AUC of the marker genes-based logistic regression model was 0.981. Functional enrichment analysis results suggested that these marker genes might exert consequential effects in AMI through regulating immune responses. CIBERSORT analysis further revealed that the immune microenvironment alterations may be associated with TLR2, S100A12, LILRB2, IL1B, and FCAR. In addition, the ceRNA network demonstrated a complex regulatory interaction.Conclusion Here we identified and validated 9 NETs-associated genes (FCAR, LILRB2, PDE4B, S100A12, DNASE1, IL1B, IL6, MMP9, and TLR2) as novel biomarkers in AMI pathogenesis. These genes may be involved in the onset and development of AMI through NETs formation. Collectively, our findings have provided potential targets for the diagnosis and treatment of AMI.

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“…Early inflammation is beneficial for cardiac function as it removes necrotic material and heals the infarct area. However, excessive inflammation is detrimental as it contributes to ventricular arrhythmias and heart failure, thus resulting in high mortality in MI patients [89]. These studies highlight apoptosis, necrosis, inflammation, and fibrosis as the critical pathways in MI.…”

Section: Myocardial Infarctionmentioning

confidence: 99%

The Role of Pro-Opiomelanocortin Derivatives in the Development of Type 2 Diabetes-Associated Myocardial Infarction: Possible Links with Prediabetes

Gumede,

Khathi

2024

Biomedicines

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Myocardial infarction is a major contributor to CVD-related mortality. T2DM is a risk factor for MI. Stress activates the HPA axis, SNS, and endogenous OPS. These POMC derivatives increase the blood glucose and cardiovascular response by inhibiting the PI3K/AkT insulin signaling pathway and increasing cardiac contraction. Opioids regulate the effect of the HPA axis and SNS and they are cardioprotective. The chronic activation of the stress response may lead to insulin resistance, cardiac dysfunction, and MI. Stress and T2DM, therefore, increase the risk of MI. T2DM is preceded by prediabetes. Studies have shown that prediabetes is associated with an increased risk of MI because of inflammation, hyperlipidemia, endothelial dysfunction, and hypertension. The HPA axis is reported to be dysregulated in prediabetes. However, the SNS and the OPS have not been explored during prediabetes. The effect of prediabetes on POMC derivatives has yet to be fully explored and understood. The impact of stress and prediabetes on the cardiovascular response needs to be investigated. This study sought to review the potential impact of prediabetes on the POMC derivatives and pathways that could lead to MI.

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Clinical assessment and molecular mechanism of the upregulation of Toll-like receptor 2 (TLR2) in myocardial infarction

Cited by 6 publications

References 52 publications

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Identifying potential biomarkers in acute myocardial infarction based on neutrophil extracellular traps associated genes

The Role of Pro-Opiomelanocortin Derivatives in the Development of Type 2 Diabetes-Associated Myocardial Infarction: Possible Links with Prediabetes

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