非编码rna在慢性阻塞性肺疾病中的作用

IF 1.5 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY
International Journal for Uncertainty Quantification Pub Date : 2023-07-05 eCollection Date: 2023-06-01 DOI:10.2478/jtim-2023-0084
Xin Qiao, Yuxiao Ding, Abdullah Altawil, Yan Yin, Qiuyue Wang, Wei Wang, Jian Kang
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引用次数: 0

摘要

慢性阻塞性肺疾病(COPD)是一种慢性异质性疾病,其特征是持续的气流阻塞和多种临床表现。[1,2]由于对慢性阻塞性肺病的分子机制缺乏了解,因此鉴定参与慢性阻塞性肺病的关键分子对于开发新的诊断措施和治疗策略至关重要。近几十年来,微阵列和下一代测序等广泛的分析方法使鉴定不编码蛋白质的RNA转录本(称为非编码RNA (ncRNAs))变得更容易。[3]NcRNAs包括各种各样的RNA物种,根据它们的长度、形状和位置来表征。许多ncrna参与表观遗传和转录后基因调控,包括microRNAs (miRNAs), trna衍生的小rna (tsRNAs)和piwi相互作用rna (piRNAs)。[4]长链非编码RNA (lncRNAs)和环状RNA (circRNAs)可以折叠成复杂的二级结构,促进它们与DNA、RNA和蛋白质的相互作用。[4]此外,lncrna和circrna可以在竞争性内源性RNA (ceRNA)网络中与mirna结合,从而阻止靶向mRNA降解。[5,6]最近的研究表明,ncrna在COPD相关的多个病理生理过程中发挥重要作用。[5,7,8]更好地了解ncrna在COPD中的作用有助于检测生物标志物和确定新的治疗靶点。在这里,我们总结了目前关于ncrna,特别是mirna、lncrna和circrna的潜在作用的研究结果。此外,我们还就该领域当前和未来的研究提出了几点考虑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Roles of noncoding RNAs in chronic obstructive pulmonary disease.
Chronic obstructive pulmonary disease (COPD) is a chronic heterogeneous disease characterized by persistent airflow obstruction and variable clinical presentations.[1,2] A lack of understanding regarding the molecular mechanisms underlying COPD makes the identification of critical molecules involved in COPD crucial for the development of novel diagnostic measures and therapeutic strategies. In recent decades, wide-ranging profiling methods such as microarrays and next-generation sequencing have made it easier to identify RNA transcripts that do not encode proteins, referred to as noncoding RNAs (ncRNAs).[3] NcRNAs comprise a diverse range of RNA species, characterized according to their length, shape, and location. Many ncRNAs are involved in epigenetic and posttranscriptional gene regulation, including microRNAs (miRNAs), tRNA-derived small RNAs (tsRNAs) and PIWI-interacting RNAs (piRNAs).[4] Long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) can fold into complex secondary structures that facilitate their interactions with DNA, RNA, and protein.[4] Additionally, lncRNAs and circRNAs can bind to miRNAs in a competitive endogenous RNA (ceRNA) network that prevents targeted mRNA degradation.[5,6] Recent studies have shown that ncRNAs play crucial roles in multiple pathophysiological processes associated with COPD.[5,7,8] A better understanding of the role of ncRNAs in COPD could contribute to the detection of biomarkers and the identification of new therapeutic targets. Here, we summarize the current findings regarding the potential role of ncRNAs, especially miRNAs, lncRNAs, and circRNAs. Additionally, we propose considerations regarding present and future research in this area.
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来源期刊
International Journal for Uncertainty Quantification
International Journal for Uncertainty Quantification ENGINEERING, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
CiteScore
3.60
自引率
5.90%
发文量
28
期刊介绍: The International Journal for Uncertainty Quantification disseminates information of permanent interest in the areas of analysis, modeling, design and control of complex systems in the presence of uncertainty. The journal seeks to emphasize methods that cross stochastic analysis, statistical modeling and scientific computing. Systems of interest are governed by differential equations possibly with multiscale features. Topics of particular interest include representation of uncertainty, propagation of uncertainty across scales, resolving the curse of dimensionality, long-time integration for stochastic PDEs, data-driven approaches for constructing stochastic models, validation, verification and uncertainty quantification for predictive computational science, and visualization of uncertainty in high-dimensional spaces. Bayesian computation and machine learning techniques are also of interest for example in the context of stochastic multiscale systems, for model selection/classification, and decision making. Reports addressing the dynamic coupling of modern experiments and modeling approaches towards predictive science are particularly encouraged. Applications of uncertainty quantification in all areas of physical and biological sciences are appropriate.
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