scholarly journals Secondary data mining of GEO database for long non-coding RNA and Competing endogenous RNA network in keloid-prone individuals

Aging ◽  
2020 ◽  
Author(s):  
Yu Deng ◽  
Yangbin Xu ◽  
Shuqia Xu ◽  
Yujing Zhang ◽  
Bing Han ◽  
...  
Keyword(s):  
Data Mining ◽  
Secondary Data ◽  
Competing Endogenous Rna ◽  
Non Coding Rna ◽  
Competing Endogenous Rna Network ◽  
Long Non Coding Rna ◽  
Geo Database

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.

scholarly journals Identification of Long Non-Coding RNA-Associated Competing Endogenous RNA Network in the Differentiation of Chicken Preadipocytes

Genes ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 795 ◽  
Author(s):  
Chen ◽  
Zhang ◽  
Zhang ◽  
Huang ◽  
Zhang ◽  
...  
Keyword(s):  
Adipogenic Differentiation ◽  
Target Prediction ◽  
Competing Endogenous Rna ◽  
Chicken Fat ◽  
Cerna Network ◽  
Non Coding Rna ◽  
Competing Endogenous Rna Network ◽  
Gene Expression Correlation ◽  
Long Non Coding Rna

Emerging evidence indicates that long noncoding RNAs (lncRNAs) play important roles in the regulation of cell differentiation by acting as competing endogenous RNA (ceRNA). However, the regulatory mechanisms of lncRNA and the lncRNA-associated ceRNA network involved in adipogenic differentiation of chicken preadipocytes remain elusive. Here, we first constructed the chicken preadipocyte in vitro induction model. Then, we identified differentially expressed lncRNAs (DELs), miRNAs (DEMis), and mRNAs (DEMs) between differentiated and undifferentiated preadipocytes. Furthermore, we constructed the lncRNA associated ceRNA network by gene expression correlation analysis and target prediction of DELs, DEMis, and DEMs. Finally, we determined twelve candidate lncRNA-miRNA-mRNA interactions from the lncRNA associated ceRNA network. Eight out of the twelve interactions were validated by RT-qPCR, indicating their potential role in the regulation of chicken preadipocytes differentiation. Among the eight interactions, TCONS_00026544-gga-miR-128-1-5p-RASD1, TCONS_00055280-gga-miR-135a-5p-JAM3, TCONS_00055280-gga-miR-135a-5p-GPR133, TCONS_00055280-gga-miR-135a-5p-CLDN1, and TCONS_00055280-gga-miR-135a-5p-TMEM123 may promote adipogenic differentiation of chicken preadipocytes while TCONS_00057272-gga-miR-146a-3p-FOXO6, TCONS_00057242-gga-miR-6615-3p-FOXO6, and TCONS_00057242-gga-miR-6615-3p-ENSGALT00000043224 have the opposite effects. Our results not only provide novel insights into ceRNA roles of lncRNAs in chicken preadipocytes differentiation and but also contribute to a better understanding of chicken fat deposition.

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