A Functional Indel Polymorphism Within MIR155HG Is Associated With Sudden Cardiac Death Risk in a Chinese Population

Zhang, Qing; Yu, Huan; Yang, Zhenzhen; Li, Lijuan; Zhu, Shijia; Li, Chengtao; Zhang, Suhua; Luo, Baoming; Gao, Yuzhen

doi:10.3389/fcvm.2021.671168

Cited by 3 publications

(2 citation statements)

References 45 publications

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“…Sudden cardiac death risk has been linked to a functional indel polymorphism in the lncRNA MIR155 host gene (MIR155HG) [ 105 ]. Overexpression of lncRNA MIR155HG in MSCs enhanced their survival, migration, and antiapoptotic properties [ 70 ].…”

Section: The Role Of Msc-derived Lncrnas In Cardiac Injury and Repairmentioning

confidence: 99%

Mesenchymal Stem Cell-Derived Long Noncoding RNAs in Cardiac Injury and Repair

Tran,

Cruz,

Chan

et al. 2023

Cells

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Cardiac injury, such as myocardial infarction and heart failure, remains a significant global health burden. The limited regenerative capacity of the adult heart poses a challenge for restoring its function after injury. Mesenchymal stem cells (MSCs) have emerged as promising candidates for cardiac regeneration due to their ability to differentiate into various cell types and secrete bioactive molecules. In recent years, attention has been given to noncoding RNAs derived from MSCs, particularly long noncoding RNAs (lncRNAs), and their potential role in cardiac injury and repair. LncRNAs are RNA molecules that do not encode proteins but play critical roles in gene regulation and cellular responses including cardiac repair and regeneration. This review focused on MSC-derived lncRNAs and their implications in cardiac regeneration, including their effects on cardiac function, myocardial remodeling, cardiomyocyte injury, and angiogenesis. Understanding the molecular mechanisms of MSC-derived lncRNAs in cardiac injury and repair may contribute to the development of novel therapeutic strategies for treating cardiovascular diseases. However, further research is needed to fully elucidate the potential of MSC-derived lncRNAs and address the challenges in this field.

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Section: The Role Of Msc-derived Lncrnas In Cardiac Injury and Repairmentioning

confidence: 99%

Mesenchymal Stem Cell-Derived Long Noncoding RNAs in Cardiac Injury and Repair

Tran,

Cruz,

Chan

et al. 2023

Cells

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“…Consequently, genetic alterations in miR-155, such as specific polymorphisms, are associated with cardiac pathology. In a 2021 study [ 36 ], researchers screened the entire region of the miR-155 target gene MIR155HG and identified a positive association between rs72014506 and the risk of SCD. Additionally, Yan et al .…”

Section: The Value Of Ncrnas In Determining Causes Of Deathmentioning

confidence: 99%

The potential of using non-coding RNAs in forensic science applications

Wang

Ishmael

et al. 2023

Forensic Sciences Research

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With the continuous development and integration of molecular biology and forensic science, non-coding RNAs (ncRNAs), especially ncRNAs with regulatory functions such as microRNA, long non-coding RNA (lncRNA) and circular RNA (circRNA), have recently been actively explored by forensic scholars. In this study, we review the literature on these ncRNAs in various fields of forensic science, including post-mortem interval (PMI) determination, wound age estimation, forensic age assessment, cause of death analysis, and body fluid identification, aiming to evaluate the current research and provide a perspective for future applications.

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Screening and identification of key biomarkers in diabetic kidney disease and its complications: Evidence from bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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Diabetic patients are prone to diabetic kidney disease (DKD), which may cause cardiovascular damage, hypertension and obesity, and reduce quality of life. As a result, the life quality of patients was seriously reduced. However, the pathogenesis of diabetic kidney disease (DKD) has not been fully elucidated, and current treatments remain inadequate. Therefore, it is essential to explore the molecular mechanism of DKD and its complications. Next Generation Sequancing (GSE217709) dataset was obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were picked out by R software. Then Gene ontology (GO) and REACTOME pathway enrichment analysis were performed by g:Profiler database, protein-protein interaction (PPI) of DEGs was constructed by Human Integrated Protein-Protein Interaction rEference (HIPPIE) database . Module analysis was carried out by Cytoscape plug-in PEWCC. Subsequently, miRNA-hub gene regulatory network and TF- hub gene regulatory network were performed by miRNet database and NetworkAnalyst database. Finally, validation of hub genes was performed by receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes. In total, 958 DEGs, including 479 up regulated and 479 down regulated genes, were identified. The GO and pathway enrichment changes of DEGs were mainly enriched in biological regulation, multicellular organismal process, signaling by GPCR and extracellular matrix organization. Ten hub genes (HSPA8, HSP90AA1, HSPA5, SDCBP, HSP90B1, VCAM1, MYH9, FLNA, MDFI and PML) associated with DKD and its complications were identified. Bioinformatics analysis is a useful tool to explore the molecular mechanism and pathogenesis of DKD and its complications. The identified hub genes may participate in the onset and development of DKD and its complications and serve as therapeutic targets.

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A Functional Indel Polymorphism Within MIR155HG Is Associated With Sudden Cardiac Death Risk in a Chinese Population

Cited by 3 publications

References 45 publications

Mesenchymal Stem Cell-Derived Long Noncoding RNAs in Cardiac Injury and Repair

Mesenchymal Stem Cell-Derived Long Noncoding RNAs in Cardiac Injury and Repair

The potential of using non-coding RNAs in forensic science applications

Screening and identification of key biomarkers in diabetic kidney disease and its complications: Evidence from bioinformatics and next generation sequencing data analysis

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