International Journal of Cardiology

2021

DOI: 10.1016/j.ijcard.2021.05.014

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LncRNA TUG1 silencing enhances proliferation and migration of ox-LDL-treated human umbilical vein endothelial cells and promotes atherosclerotic vascular injury repairing via the Runx2/ANPEP axis

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Vascular Damage Due To Hyperglycemia and Dyslipidemia1

The Research Status Of Lncrnas Related To Osteogenic Differe...1

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Cited by 14 publications

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“…In order to verify the key sequencing data in the subsequent experiment, we initially selected 10 lncRNAs, including NONRATT001612.2, AABR07060133.1, NONRATT030019.2, TCONS_00039587, NONRATT030152.2, Tug1, AABR07011996.1, NONRATT024063.2, NONRATT020278.2 and TCONS_00029145, from 812 up-regulated lncRNAs based on fold change, raw intensity and relevant literature evidence [ 26 ]. The results displayed by the heat map (Fig.…”

Section: Resultsmentioning

confidence: 99%

Comprehensive analysis of lncRNA-miRNA-mRNA networks during osteogenic differentiation of bone marrow mesenchymal stem cells

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Background Long non-coding RNA (lncRNA) plays crucial role in osteogenic differentiation of bone marrow mesenchymal stem cells (BMMSCs), involving in regulation of competing endogenous RNA (ceRNA) mechanisms and conduction of signaling pathways. However, its mechanisms are poorly understood. This study aimed to investigate lncRNAs, miRNAs and mRNAs expression profiles in rat BMMSCs (rBMMSCs) osteogenic differentiation, screen the potential key lncRNA-miRNA-mRNA networks, explore the putative functions and identify the key molecules, as the basis of studying potential mechanism of rBMMSCs osteogenic differentiation driven by lncRNA, providing molecular targets for the management of bone defect. Methods High-throughput RNA sequencing (RNA-seq) was used to determine lncRNAs, miRNAs, and mRNAs expression profiles at 14-day rBMMSCs osteogenesis. The pivotal lncRNA-miRNA and miRNA-mRNA networks were predicted from sequencing data and bioinformatic analysis, and the results were exported by Cytoscape 3.9.0 software. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used for functional exploration. Real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed to validate lncRNAs, miRNAs and mRNAs. Results rBMMSCs were identified, and the osteogenic and adipogenic differentiation ability were detected. A total of 8634 lncRNAs were detected by RNA-seq, and 1524 differential expressed lncRNAs, of which 812 up-regulated and 712 down-regulated in osteo-inductive groups compared with control groups. 30 up-regulated and 61 down-regulated miRNAs, 91 miRNAs were differentially expressed in total. 2453 differentially expressed mRNAs including 1272 up-expressed and 1181 down-expressed were detected. 10 up-regulated lncRNAs were chosen to predict 21 down-regulated miRNAs and 650 up-regulated mRNAs. 49 lncRNA-miRNA and 1515 miRNA–mRNA interactive networks were constructed. GO analysis showed the most important enrichment in cell component and molecular function were “cytoplasm” and “protein binding”, respectively. Biological process related to osteogenic differentiation such as “cell proliferation”, “wound healing”, “cell migration”, “osteoblast differentiation”, “extracellular matrix organization” and “response to hypoxia” were enriched. KEGG analysis showed differentially expressed genes were mainly enriched in “PI3K-Akt signaling pathway”, “Signaling pathway regulating pluripotency of stem cells”, “cGMP-PKG signaling pathway”, “Axon guidance” and “Calcium signaling pathway”. qRT-PCR verified that lncRNA Tug1, lncRNA AABR07011996.1, rno-miR-93-5p, rno-miR-322-5p, Sgk1 and Fzd4 were consistent with the sequencing results, and 4 lncRNA-miRNA-mRNA networks based on validations were constructed, and enrichment pathways were closely related to “PI3K-Akt signaling pathway”, “Signaling pathway regulating pluripotency of stem cells” and “Wnt signaling pathway”. Conclusions lncRNAs, miRNAs and mRNAs expression profiles provide clues for future studies on their roles for BMMSCs osteogenic differentiation. Furthermore, lncRNA–miRNA–mRNA networks give more information on potential new mechanisms and targets for management on bone defect.

“…In order to verify the key sequencing data in the subsequent experiment, we initially selected 10 lncRNAs, including NONRATT001612.2, AABR07060133.1, NONRATT030019.2, TCONS_00039587, NONRATT030152.2, Tug1, AABR07011996.1, NONRATT024063.2, NONRATT020278.2 and TCONS_00029145, from 812 up-regulated lncRNAs based on fold change, raw intensity and relevant literature evidence [ 26 ]. The results displayed by the heat map (Fig.…”

Section: Resultsmentioning

confidence: 99%

Comprehensive analysis of lncRNA-miRNA-mRNA networks during osteogenic differentiation of bone marrow mesenchymal stem cells

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,

²

,

³

et al. 2022

View full text Add to dashboard Cite

Background Long non-coding RNA (lncRNA) plays crucial role in osteogenic differentiation of bone marrow mesenchymal stem cells (BMMSCs), involving in regulation of competing endogenous RNA (ceRNA) mechanisms and conduction of signaling pathways. However, its mechanisms are poorly understood. This study aimed to investigate lncRNAs, miRNAs and mRNAs expression profiles in rat BMMSCs (rBMMSCs) osteogenic differentiation, screen the potential key lncRNA-miRNA-mRNA networks, explore the putative functions and identify the key molecules, as the basis of studying potential mechanism of rBMMSCs osteogenic differentiation driven by lncRNA, providing molecular targets for the management of bone defect. Methods High-throughput RNA sequencing (RNA-seq) was used to determine lncRNAs, miRNAs, and mRNAs expression profiles at 14-day rBMMSCs osteogenesis. The pivotal lncRNA-miRNA and miRNA-mRNA networks were predicted from sequencing data and bioinformatic analysis, and the results were exported by Cytoscape 3.9.0 software. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used for functional exploration. Real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed to validate lncRNAs, miRNAs and mRNAs. Results rBMMSCs were identified, and the osteogenic and adipogenic differentiation ability were detected. A total of 8634 lncRNAs were detected by RNA-seq, and 1524 differential expressed lncRNAs, of which 812 up-regulated and 712 down-regulated in osteo-inductive groups compared with control groups. 30 up-regulated and 61 down-regulated miRNAs, 91 miRNAs were differentially expressed in total. 2453 differentially expressed mRNAs including 1272 up-expressed and 1181 down-expressed were detected. 10 up-regulated lncRNAs were chosen to predict 21 down-regulated miRNAs and 650 up-regulated mRNAs. 49 lncRNA-miRNA and 1515 miRNA–mRNA interactive networks were constructed. GO analysis showed the most important enrichment in cell component and molecular function were “cytoplasm” and “protein binding”, respectively. Biological process related to osteogenic differentiation such as “cell proliferation”, “wound healing”, “cell migration”, “osteoblast differentiation”, “extracellular matrix organization” and “response to hypoxia” were enriched. KEGG analysis showed differentially expressed genes were mainly enriched in “PI3K-Akt signaling pathway”, “Signaling pathway regulating pluripotency of stem cells”, “cGMP-PKG signaling pathway”, “Axon guidance” and “Calcium signaling pathway”. qRT-PCR verified that lncRNA Tug1, lncRNA AABR07011996.1, rno-miR-93-5p, rno-miR-322-5p, Sgk1 and Fzd4 were consistent with the sequencing results, and 4 lncRNA-miRNA-mRNA networks based on validations were constructed, and enrichment pathways were closely related to “PI3K-Akt signaling pathway”, “Signaling pathway regulating pluripotency of stem cells” and “Wnt signaling pathway”. Conclusions lncRNAs, miRNAs and mRNAs expression profiles provide clues for future studies on their roles for BMMSCs osteogenic differentiation. Furthermore, lncRNA–miRNA–mRNA networks give more information on potential new mechanisms and targets for management on bone defect.

“…Alanyl aminopeptidase, membrane (ANPEP, also known as CD13 and APN) is essential for inflammatory transport and infarct healing after permanent coronary artery occlusion [ 21 ]. ANPEP can regulate the repair of atherosclerotic vascular injury [ 22 ]. The disorder of ANPEP is also related to the pathogenesis of hypertension [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

Uncovering potential diagnostic biomarkers of acute myocardial infarction based on machine learning and analyzing its relationship with immune cells

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et al. 2023

BMC Cardiovasc Disord

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Background Acute myocardial infarction (AMI) is a common cardiovascular disease. This study aimed to mine biomarkers associated with AMI to aid in clinical diagnosis and management. Methods All mRNA and miRNA data were downloaded from public database. Differentially expressed mRNAs (DEmRNAs) and differentially expressed miRNAs (DEmiRNAs) were identified using the metaMA and limma packages, respectively. Functional analysis of the DEmRNAs was performed. In order to explore the relationship between miRNA and mRNA, we construct miRNA-mRNA negative regulatory network. Potential biomarkers were identified based on machine learning. Subsequently, ROC and immune correlation analysis were performed on the identified key DEmRNA biomarkers. Results According to the false discovery rate < 0.05, 92 DEmRNAs and 272 DEmiRNAs were identified. GSEA analysis found that kegg_peroxisome was up-regulated in AMI and kegg_steroid_hormone_biosynthesis was down-regulated in AMI compared to normal controls. 5 key DEmRNA biomarkers were identified based on machine learning, and classification diagnostic models were constructed. The random forests (RF) model has the highest accuracy. This indicates that RF model has high diagnostic value and may contribute to the early diagnosis of AMI. ROC analysis found that the area under curve of 5 key DEmRNA biomarkers were all greater than 0.7. Pearson correlation analysis showed that 5 key DEmRNA biomarkers were correlated with most of the differential infiltrating immune cells. Conclusion The identification of new molecular biomarkers provides potential research directions for exploring the molecular mechanism of AMI. Furthermore, it is important to explore new diagnostic genetic biomarkers for the diagnosis and treatment of AMI.

“…Runt-related transcription factor 2 (Runx2) was detected as the downstream target of TUG1 by RNA pull down, and Runx2 knockdown could reverse the function of aminopeptidase N (ANPEP). TUG1 silencing can promote endothelial injury repair via the repression of Runx2 and ANPEP ( Du et al, 2021 ).…”

Section: Vascular Damage Due To Hyperglycemia and Dyslipidemiamentioning

confidence: 99%

The mechanisms of glycolipid metabolism disorder on vascular injury in type 2 diabetes

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et al. 2022

Front. Physiol.

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Patients with diabetes have severe vascular complications, such as diabetic nephropathy, diabetic retinopathy, cardiovascular disease, and neuropathy. Devastating vascular complications lead to increased mortality, blindness, kidney failure, and decreased overall quality of life in people with type 2 diabetes (T2D). Glycolipid metabolism disorder plays a vital role in the vascular complications of T2D. However, the specific mechanism of action remains to be elucidated. In T2D patients, vascular damage begins to develop before insulin resistance and clinical diagnosis. Endothelial dysregulation is a significant cause of vascular complications and the early event of vascular injury. Hyperglycemia and hyperlipidemia can trigger inflammation and oxidative stress, which impair endothelial function. Furthermore, during the pathogenesis of T2D, epigenetic modifications are aberrant and activate various biological processes, resulting in endothelial dysregulation. In the present review, we provide an overview and discussion of the roles of hyperglycemia- and hyperlipidemia-induced endothelial dysfunction, inflammatory response, oxidative stress, and epigenetic modification in the pathogenesis of T2D. Understanding the connections of glucotoxicity and lipotoxicity with vascular injury may reveal a novel potential therapeutic target for diabetic vascular complications.

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