scholarly journals Competing endogenous RNA network analysis for screening inflammation‑related long non‑coding RNAs for acute ischemic stroke

2020 ◽  
Author(s):  
Li Zhang ◽  
Baihui Liu ◽  
Jinhua Han ◽  
Tingting Wang ◽  
Lin Han
Keyword(s):  
Ischemic Stroke ◽  
Network Analysis ◽  
Acute Ischemic Stroke ◽  
Competing Endogenous Rna ◽  
Competing Endogenous Rna Network ◽  
Non Coding Rnas

scholarly journals Non-Coding RNA in Acute Ischemic Stroke: Mechanisms, Biomarkers and Therapeutic Targets

2018 ◽  
Vol 27 (12) ◽  
pp. 1763-1777 ◽  
Author(s):  
Sheng-Wen Wang ◽  
Zhong Liu ◽  
Zhong-Song Shi
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Neural Development ◽  
Therapeutic Targets ◽  
Circular Rnas ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
Cerebral Ischemic ◽  
Functional Rnas ◽  
The Brain

Non-coding RNAs (ncRNAs) are a class of functional RNAs that regulate gene expression in a post-transcriptional manner. NcRNAs include microRNAs, long non-coding RNAs and circular RNAs. They are highly expressed in the brain and are involved in the regulation of physiological and pathophysiological processes, including cerebral ischemic injury, neurodegeneration, neural development, and plasticity. Stroke is one of the leading causes of death and physical disability worldwide. Acute ischemic stroke (AIS) occurs when brain blood flow stops, and that stoppage results in reduced oxygen and glucose supply to cells in the brain. In this article, we review the latest progress on ncRNAs in relation to their implications in AIS, as well as their potential as diagnostic and prognostic biomarkers. We also review ncRNAs acting as possible therapeutic targets in future precision medicine. Finally, we conclude with a brief discussion of current challenges and future directions for ncRNAs studies in AIS, which may facilitate the translation of ncRNAs research into clinical practice to improve clinical outcome of AIS.

Identifying Molecular Markers of Cervical Cancer Based on Competing Endogenous RNA Network Analysis

2019 ◽  
Vol 84 (4) ◽  
pp. 350-359 ◽  
Author(s):  
Shanshan Qin ◽  
Yingchun Gao ◽  
Yijun Yang ◽  
Lei Zhang ◽  
Ting Zhang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Molecular Markers ◽  
Network Analysis ◽  
Competing Endogenous Rna ◽  
Competing Endogenous Rna Network

scholarly journals Competing endogenous RNA network analysis identifies critical genes among the different breast cancer subtypes

Oncotarget ◽  
2016 ◽  
Vol 8 (6) ◽  
pp. 10171-10184 ◽  
Author(s):  
Juan Chen ◽  
Juan Xu ◽  
Yongsheng Li ◽  
Jinwen Zhang ◽  
Hong Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Breast Cancer Subtypes ◽  
Competing Endogenous Rna ◽  
Cancer Subtypes ◽  
Competing Endogenous Rna Network

scholarly journals Identification of lncRNA and mRNA Biomarkers in Osteoarthritic Degenerative Meniscus by Weighted Gene Coexpression Network and Competing Endogenous RNA Network Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jun Zhao ◽  
Yu Su ◽  
Jianfei Jiao ◽  
Zhengchun Wang ◽  
Xiangchun Fang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Roc Curve ◽  
Diagnostic Value ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Coexpression Network ◽  
Competing Endogenous Rna ◽  
Roc Curve Analysis ◽  
Competing Endogenous Rna Network ◽  
Meniscus Degeneration

Background. Long noncoding RNAs (lncRNAs) play a crucial role in varieties of biological processes. This study is aimed at investigating meniscal degeneration-specific lncRNAs and mRNAs and their related networks in knee osteoarthritis (KOA). Methods. The dataset GSE98918, which included 24 meniscus samples and related clinical data, was downloaded from the Gene Expression Omnibus database. The differentially expressed lncRNAs and mRNAs in the meniscus between KOA and control groups were identified. Based on the enriched differentially expressed lncRNAs and mRNAs, we constructed the coexpression network using WGCNA (weighted correlation network analysis) and identified the critical module related to KOA. For mRNAs in the key module, gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were carried out using the DAVID database. A competing endogenous RNA network (ceRNA) based on the screened mRNAs, lncRNAs, and related miRNAs was constructed to reveal presumptive biomarkers further. Finally, the hub lncRNAs and mRNAs were screened, and the diagnostic value was evaluated using a receiver operating characteristic (ROC) curve. Hub mRNAs were validated using the dataset GSE113825. Results. We screened 208 significantly differentially expressed lncRNAs and mRNAs in menisci between the KOA and non-KOA samples, which were enriched in sixteen modules using WGCNA, especially the green module. Coexpression network based on the enriched differentially expressed lncRNAs and mRNAs in the green module uncovered 5 lncRNAs and 56 mRNAs. The lncRNA-miRNA-mRNA ceRNA network revealed that lnc-HLA-DQA1-5, lnc-RP11-127H5.1.1-1, lnc-RTN2-1, IGFBP4 (insulin-like growth factor binding protein 4), and KLF2 (Kruppel-like factor 2) were significantly correlated with the meniscus degeneration of KOA. ROC curve analysis revealed that these hub lncRNAs and mRNAs showed excellent diagnostic value for KOA. Conclusions. These hub lncRNAs and mRNAs were potential prognostic biomarkers for the meniscus degeneration of KOA. Further studies are required to validate these new biomarkers and better understand the pathological process of the meniscus degeneration of KOA.

scholarly journals Construction of asthma related competing endogenous RNA network revealed novel long non-coding RNAs and potential new drugs

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yifang Liao ◽  
Ping Li ◽  
Yanxia Wang ◽  
Hong Chen ◽  
Shangwei Ning ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Repositioning ◽  
New Drugs ◽  
Differentially Expressed ◽  
Interaction Data ◽  
Competing Endogenous Rna ◽  
Non Coding Rna ◽  
Competing Endogenous Rna Network ◽  
Non Coding Rnas ◽  
Long Non Coding Rna

Abstract Background Asthma is a heterogeneous disease characterized by chronic airway inflammation. Long non-coding RNA can act as competing endogenous RNA to mRNA, and play significant role in many diseases. However, there is little known about the profiles of long non-coding RNA and the long non-coding RNA related competing endogenous RNA network in asthma. In current study, we aimed to explore the long non-coding RNA-microRNA-mRNA competing endogenous RNA network in asthma and their potential implications for therapy and prognosis. Methods Asthma-related gene expression profiles were downloaded from the Gene Expression Omnibus database, re-annotated with these genes and identified for asthma-associated differentially expressed mRNAs and long non-coding RNAs. The long non-coding RNA-miRNA interaction data and mRNA-miRNA interaction data were downloaded using the starBase database to construct a long non-coding RNA-miRNA-mRNA global competing endogenous RNA network and extract asthma-related differentially expressed competing endogenous RNA network. Finally, functional enrichment analysis and drug repositioning of asthma-associated differentially expressed competing endogenous RNA networks were performed to further identify key long non-coding RNAs and potential therapeutics associated with asthma. Results This study constructed an asthma-associated competing endogenous RNA network, determined 5 key long non-coding RNAs (MALAT1, MIR17HG, CASC2, MAGI2-AS3, DAPK1-IT1) and identified 8 potential new drugs (Tamoxifen, Ruxolitinib, Tretinoin, Quercetin, Dasatinib, Levocarnitine, Niflumic Acid, Glyburide). Conclusions The results suggested that long non-coding RNA played an important role in asthma, and these novel long non-coding RNAs could be potential therapeutic target and prognostic biomarkers. At the same time, potential new drugs for asthma treatment have been discovered through drug repositioning techniques, providing a new direction for the treatment of asthma.

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