LncRNA Kcnq1ot1 renders cardiomyocytes apoptosis in acute myocardial infarction model by up-regulating Tead1

Liao, Bihong; Dong, Shaohong; Xu, Zefeng; Gao, Fei; Zhang, Suihao; Liang, Ruijuan

doi:10.1016/j.lfs.2020.117811

Cited by 32 publications

(16 citation statements)

References 29 publications

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“…The miRNA-3473 is specifically up-regulated in the glomerular injury models, may become one of the early and sensitive indicators to detect tubular and glomerular injuries [ 28 ]. The miR-466i and miR-466k are sponged by circRNA-Kcnq1ot1 participate in cardiomyocytes apoptosis in acute myocardial infarction [ 29 ]. By analyzing the ceRNA results, we reveal that the up-regulated circRNA-3109 or circRNA-14838 is able to suppress miR-615-5p, which binds to collagen I, may promote renal fibrosis [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of circular RNA expression profiles in renal fibrosis induced by obstructive injury

et al. 2021

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Introduction Advancing renal fibrosis is the common histopathological feature of chronic obstructive nephropathy, representing the final pathway of nearly all chronic and progressive nephropathies. Increasing evidences suggest that circular RNAs (circRNAs) are crucial regulatory molecules present at virtually every level of the cellular pathophysiological process. Nonetheless, there are a few evidences for the role of circRNAs in renal fibrosis induced by obstructive nephropathy. Aims We performed RNA-seq analysis to analyze the expression profiles of circRNAs in the obstructed kidneys to identify the potential circRNAs and their network. Methods With silk ligated the left ureter to establish a mice unilateral ureteral obstruction (UUO) model. Renal tissue circRNAs were obtained and were screened by a circRNA microarray. The circRNA-miRNA-mRNA regulatory network and the target genes were visualized using Cytoscape software. Results The microarray results showed that 5454 and 2935 circRNAs were detected in the control and UUO group, respectively. There were 605 circRNAs up-regulated and 745 circRNAs down-regulated in the obstructive kidneys. The top 5 up-regulated and down-regulated circRNAs were chosen for predicting the circRNA/miRNA/target mRNAs triple network. The GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis showed that these circRNAs and the triple network were enriched in the process of apoptosis, p53 signaling pathway, cell growth and cell death, which might participate in the pathogenesis of obstructive nephrology. Conclusion Our results show that the dis-regulated circRNAs might play crucial roles in the pathogenesis of obstructive nephropathy, which proceeds to identify novel therapeutic targets for chronic kidney disease.

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Section: Discussionmentioning

confidence: 99%

Identification of circular RNA expression profiles in renal fibrosis induced by obstructive injury

et al. 2021

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“…Increasing evidence indicates that lncRNAs can competitively bind miRNAs through sponge adsorption to modulate cell proliferation, metastasis, differentiation, and apoptosis to regulate the initiation and progression of diseases (23). For example, the lncRNA KCNQ1OT1 mediates miR-466i-5p downregulation, inducing high expression of the target gene Tead1 and leading to cardiomyocyte damage (24). In addition, the lncRNAs SNHG14 and SNHG7 competitively sponge miR-322-5p and miR-34-5p, respectively, increasing the expression levels of PCDH17 and ROCK1, leading to cardiomyocyte hypertrophy and fibrosis (25,26).…”

Section: Discussionmentioning

confidence: 99%

Role of competitive endogenous RNA networks in the pathogenesis of coronary artery disease

Zuo¹,

Xu²,

Wang³

et al. 2021

Ann Transl Med

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Background: The present study aimed to construct a network of competitive endogenous RNAs (ceRNAs) related to the pathogenesis of coronary artery disease (CAD), to provide a novel rationale for CAD treatment.Methods: Bioinformatics methods were applied to screen for differentially expressed long non-coding RNAs (DElncRNAs), microRNAs (DEmiRNAs), and mRNAs (DEmRNAs) from the GSE68506, GSE59421, and GSE20129 datasets of the Gene Expression Omnibus (GEO) database. The miRcode database was used to predict lncRNA-binding miRNAs. The miRTarBase, miRDB, and TargetScan databases were used to predict the target genes of these miRNAs. An mRNA-miRNA-lncRNA ceRNA network of CAD was established.Results: Between the CAD and normal control groups there were 264 DElncRNAs, 106 DEmiRNAs, and 1,879 DEmRNAs. We screened these differentially expressed gens (DEGs) respectively. There were 21 DElncRNAs, 13 DEmiRNAs, and 143 DEmRNAs in the ceRNA network by using Cytoscape application. The DEmRNAs were involved in the PI3K-Akt signaling pathway and the NF-κB signaling pathway. The key genes in the protein-protein interaction (PPI) network were HSP90AA1, CDKN1A,

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“…The subsequent construction of the miRNA-hub gene regulatory network and TF- hub gene regulatory network using the respective hub gens identified miRNA and TFs as potential key markers involved in MI. Chen et al [222], Li and Zhang [223], Wang et al [224], Zhao et al [225], Lin et al [226], Liao et al [227], Izadpanah et al [228], Wang et al [229] and Hakobjanyan et al [230] reported that the expression of the hsa-mir-409-3p, hsa-mir-320d, hsa-mir-107, hsa-mir-139-5p, NRF1, TEAD1, GATA2, E2F1 and TP53 are correlated with disease grades of MI. hsa-mir-301a-5p [231], hsa-mir-29a-5p [232], hsa-mir-296-5p [233], CREB1 [234] and FOXA1 [235] were linked with progression of diabetes mellitus, but these genes might be novel target for MI.…”

Section: Discussionmentioning

confidence: 99%

Identification and Interaction Analysis of Molecular Markers in Myocardial Infarction by Integrated Bioinformatics Analysis

Vastrad¹,

Vastrad²

2021

Preprint

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Myocardial infarction (MI) is the leading cardiovascular diseases in worldwide, yet relatively little is known about the genes and signaling pathways involved in MI progression. The present investigation aimed to elucidate potential crucial candidate genes and pathways in MI. expression profiling by high throughput sequencing dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 20 MI samples and 12 normal control samples. Differentially expressed genes (DEGs) were identified using t-tests in the DESeq2 R package. These DEGs were subsequently investigated by Gene Ontology (GO) and pathway enrichment analysis, a protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed and analyzed. Hub genes were validated by receiver operating characteristic curve (ROC) analysis. In total, 958 DEGs were identified, of which 480 were up regulated and 478 were down regulated. GO and pathway enrichment analysis results revealed that the DEGs were mainly enriched in, immune system, neuronal system, response to stimulus, and multicellular organismal process. A PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network was constructed by using Cytoscape software, and CFTR, CDK1, RPS13, RPS15A, RPS27, NOTCH1, MRPL12, NOS2, CCDC85B and ATN1 were identified as the hub genes. Our results highlight the important roles of the genes including CFTR, CDK1, RPS13, RPS15A, RPS27, NOTCH1, MRPL12, NOS2, CCDC85B and ATN1 in MI pathogenesis or therapeutic management.

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LncRNA Kcnq1ot1 renders cardiomyocytes apoptosis in acute myocardial infarction model by up-regulating Tead1

Cited by 32 publications

References 29 publications

Identification of circular RNA expression profiles in renal fibrosis induced by obstructive injury

Identification of circular RNA expression profiles in renal fibrosis induced by obstructive injury

Role of competitive endogenous RNA networks in the pathogenesis of coronary artery disease

Identification and Interaction Analysis of Molecular Markers in Myocardial Infarction by Integrated Bioinformatics Analysis

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