scholarly journals Construction and analysis for differentially expressed long non-coding RNAs and MicroRNAs mediated competing endogenous RNA network in colon cancer

PLoS ONE ◽  
2018 ◽  
Vol 13 (2) ◽  
pp. e0192494 ◽  
Author(s):  
Fengxi Li ◽  
Qian Li ◽  
Xianghua Wu
Keyword(s):  
Colon Cancer ◽  
Differentially Expressed ◽  
Competing Endogenous Rna ◽  
Competing Endogenous Rna Network ◽  
Non Coding Rnas

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 Genome-wide characterization of coding and non-coding RNAs in the ovary of honeybee workers and queens

Apidologie ◽  
2020 ◽  
Vol 51 (5) ◽  
pp. 777-792
Author(s):  
Xiao Chen ◽  
Wei Shi
Keyword(s):  
Egg Laying ◽  
Differentially Expressed ◽  
Sequencing Data ◽  
Competing Endogenous Rna ◽  
Genome Wide ◽  
Competing Endogenous Rna Network ◽  
Non Coding Rnas ◽  
Virgin Queens ◽  
Potential Interactions

Abstract Adult honeybee queens and workers drastically differ in ovary state and ovary size. However, this reproductive bias is only partially understood from the view of a single RNA type. In this study, we predicted 10,271 mRNAs, 7235 lncRNAs, 11,794 circRNAs, and 164 miRNAs in the ovary of honeybee workers through bioinformatics. Combining RNA sequencing data of honeybee virgin queens, 4385 mRNAs, 2390 lncRNAs, 5602 circRNAs, and 75 miRNAs were differentially expressed in workers compared with virgins. Compared with egg-laying queens, 6536 mRNAs, 3130 lncRNAs, 5751 circRNAs, and 81 miRNAs were differentially expressed in workers. Further, functional annotation revealed that neural regulation was closely related to ovary state. Moreover, the potential interactions among circRNAs, miRNAs, lncRNAs, and mRNAs revealed that vitellogenin, ecdysone-induced protein 74, ame_circ_0001176, and ame_circ_0001243 might play critical roles in the competing endogenous RNA network. These findings suggest that the integrative RNA networks have potential effects in ovarian phenotype differences in honeybees.

scholarly journals Comprehensive Analysis of lncRNA–miRNA– mRNA Network Ascertains Prognostic Factors in Patients with Colon Cancer

2019 ◽  
Vol 18 ◽  
pp. 153303381985323 ◽  
Author(s):  
Zhenzhen Gao ◽  
Peng Fu ◽  
Zhengyi Yu ◽  
Fuxi Zhen ◽  
Yanhong Gu
Keyword(s):  
Colon Cancer ◽  
Gene Ontology ◽  
Differentially Expressed ◽  
Messenger Rnas ◽  
Competing Endogenous Rna ◽  
Normal Colon ◽  
Kaplan Meier ◽  
Non Coding Rna ◽  
Competing Endogenous Rnas ◽  
Non Coding Rnas

Background: Non-coding RNAs are competing endogenous RNAs in the occurrence and development of tumorigenesis; numerous microRNAs are aberrantly expressed in colon cancer tissues and play significant roles in oncogenesis development and metastasis. However, large clinical and RNA data are lacking to further confirm the exact role of these RNAs in tumors. This study aimed to ascertain differential RNA expression between colon cancer and normal colon tissues. Materials and Methods: RNA sequencing and clinical data of patients with colon cancer were procured from The Cancer Genome Atlas database; differentially expressed long non-coding RNA, differentially expressed messenger RNAs, and differentially expressed microRNAs were achieved using the limma package in edgeR to generate competing endogenous RNAs networks. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were conducted with ggplot2 package, the Kaplan-Meier survival method was used to predict survival in patients with colon cancer. Results: In total, 1174 differentially expressed long non-coding RNAs, 2068 differentially expressed messenger RNAs, and 239 differentially expressed microRNAs were generated between 480 colon cancer and 41 normal colon tissue samples. Three competing endogenous RNA networks were established. Gene Ontology analysis indicated that the genes of the up-regulated microRNA network were involved in negative regulation of transcription, DNA-template, and those of down-regulated microRNA network were involved in transforming growth factor β receptor signaling pathways, response to hypoxia, cell migration, while Kyoto Encyclopedia of Genes and Genomes analyses of these networks turned out to be negative. Three long non-coding RNAs (AP004609.1, ARHGEF26-AS1, and LINC00491), 3 microRNAs (miRNA-141, miRNA-216a, and miRNA-193b) and 3 RNAs (ULBP2, PHLPP2, and TPM2) were detected to be associated with prognosis by the Kaplan-Meier survival analysis. Additionally, univariate and multivariate Cox regression analyses showed that the microRNA-216a of the competing endogenous RNA might be an independent prognostic factor in colon cancer. Conclusions: This study constructed the non-coding RNA-related competing endogenous RNA networks in colon cancer and sheds lights on underlying biomarkers for colon cancer cohorts.

scholarly journals Identification of Non-coding RNA Biomarkers in Stress-induced Depression via Comprehensive Analysis of Competing Endogenous RNA Network

2020 ◽  
Author(s):  
xuanjun liu ◽  
Lan Yan ◽  
Chun Lin ◽  
Yiliang Zhang ◽  
Haofei Miao ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Comprehensive Analysis ◽  
Differentially Expressed ◽  
Psychiatric Disease ◽  
Circular Rnas ◽  
Messenger Rnas ◽  
Competing Endogenous Rna ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
Promising Perspective

Abstract BackgroundDepression is one of the most common psychiatric disease worldwide. Although the research about the pathogenesis of depression have achieved progress, the detailed effect of non-coding RNAs (ncRNAs) in depression are still not clearly elucidated. This study was aimed to identify non-coding RNA biomarkers in stress-induced depression via comprehensive analysis of competing endogenous RNA networkMethodsIn this present study, we acquired RNA expression from RNA seq expression profile in three mice with depressive-like behaviors using chronic restraint stress paradigm and three C57BL/6J wild-type mice as control mice. ResultsA total of 41 differentially expressed circular RNAs (circRNAs) and 181 differentially expressed messenger RNAs (mRNAs) were up-regulated, and 65 differentially expressed circRNAs and 289 differentially expressed mRNAs were down-regulated, which were selected by a threshold of fold change ≥2 and a p-value < 0.05. Gene Ontology was performed to analyze the biological functions, and we predicted potential signaling pathways based on Kyoto Encyclopedia of Genes and Genomes pathway database. In addition, we constructed a circRNA-microRNA (miRNA)-mRNA regulatory network to further identify non-coding RNAs biomarkers. ConclusionsOur findings provide a promising perspective for further research into molecular mechanisms of depression, and targeting circRNA -mediated competing endogenous RNA (ceRNA) network is a useful strategy to early recognize the depression.

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 Comprehensive Analysis of the Expression Profiles of Long Non-Coding RNAs with Associated ceRNA Network Involved in the Colon Cancer Staging and Progression

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Meini Wu ◽  
Wenliang Li ◽  
Fengchang Huang ◽  
Jing Sun ◽  
Kang ping Li ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Expression Profiles ◽  
Comprehensive Analysis ◽  
Differentially Expressed ◽  
Stage Iii ◽  
Aberrant Expression ◽  
Normal Tissues ◽  
Cerna Network ◽  
Tumor Tissues ◽  
Non Coding Rnas

AbstractLong non-coding RNAs (lncRNAs) act as competing endogenous RNAs (ceRNAs) to compete with microRNAs (miRNAs) in cancer occurrence and development. However, the differential expression of RNAs and their ceRNA network during the development of colon cancer (CC) remains unclear. This study was aimed at comprehensive analysis of the lncRNAs and their ceRNA networks associated with CC. Whole transcriptome sequencing was performed on colorectal and adjacent normal tissues at different pathological stages. Forty-nine lncRNAs were differently expressed between the CC tissues and their adjacent normal tissues at all stages. Aberrant expression of lncRNA CDKN2B-AS1 and lncRNA MIR4435-2HG was confirmed by TCGA database. Moreover, 14 lncRNAs were differentially expressed between early and advance stages of the tumor tissues, and 117 miRNAs were specifically expressed in stage III & IV. Weighted gene co-expression network analysis of 17105 differently expressed mRNAs revealed that the mRNAs shown in module pink, midnight blue, black, and light cyan were related to TNM and pathological stage, and that these mRNAs were enriched in cancer related functions and pathways. As DElncRNA showed a trend of change similar to that of the DEmRNA and opposite to that of DEmiRNA, ceRNA network was constructed with 3 DEmiRNAs, 5 DElncRNAs, and 130 DEmRNAs. Real time PCR revealed that expression of MEG3 was decreased in the tumor tissues belonging to stage III and IV as compared to that in stage I. Moreover, hsa-miR-324-5p was upregulated, while FGFR3, PLCB4, and IKBKB were downregulated in the tumor tissues as compared to that in the adjacent normal tissues. Thus, this study revealed differentially expressed lncRNA between different stages of CC as well as suggested that lncRNA CDKN2B-AS1, MIR4435-2HG, and MEG3 may act as diagnostic biomarkers for the development of CC.

scholarly journals An integrated analysis of the competing endogenous RNA network and co-expression network revealed seven hub long non-coding RNAs in osteoarthritis

2020 ◽  
Vol 9 (3) ◽  
pp. 90-98 ◽  
Author(s):  
Haitao Chen ◽  
Liaobin Chen
Keyword(s):  
Extracellular Matrix ◽  
Expression Profiles ◽  
Osteoclast Differentiation ◽  
Signalling Pathway ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Knee Cartilage ◽  
Competing Endogenous Rna ◽  
Cerna Network ◽  
Non Coding Rnas

Aims This study aimed to uncover the hub long non-coding RNAs (lncRNAs) differentially expressed in osteoarthritis (OA) cartilage using an integrated analysis of the competing endogenous RNA (ceRNA) network and co-expression network. Methods Expression profiles data of ten OA and ten normal tissues of human knee cartilage were obtained from the Gene Expression Omnibus (GEO) database (GSE114007). The differentially expressed messenger RNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified using the edgeR package. We integrated human microRNA (miRNA)-lncRNA/mRNA interactions with DElncRNA/DEmRNA expression profiles to construct a ceRNA network. Likewise, lncRNA and mRNA expression profiles were used to build a co-expression network with the WGCNA package. Potential hub lncRNAs were identified based on an integrated analysis of the ceRNA network and co-expression network. StarBase and Multi Experiment Matrix databases were used to verify the lncRNAs. Results We detected 1,212 DEmRNAs and 49 DElncRNAs in OA and normal knee cartilage. A total of 75 dysregulated lncRNA-miRNA interactions and 711 dysregulated miRNA-mRNA interactions were obtained in the ceRNA network, including ten DElncRNAs, 69 miRNAs, and 72 DEmRNAs. Similarly, 1,330 dysregulated lncRNA-mRNA interactions were used to construct the co-expression network, which included ten lncRNAs and 407 mRNAs. We finally identified seven hub lncRNAs, named MIR210HG, HCP5, LINC00313, LINC00654, LINC00839, TBC1D3P1-DHX40P1, and ISM1-AS1. Subsequent enrichment analysis elucidated that these lncRNAs regulated extracellular matrix organization and enriched in osteoclast differentiation, the FoxO signalling pathway, and the tumour necrosis factor (TNF) signalling pathway in the development of OA. Conclusion The integrated analysis of the ceRNA network and co-expression network identified seven hub lncRNAs associated with OA. These lncRNAs may regulate extracellular matrix changes and chondrocyte homeostasis in OA progress. Cite this article: Bone Joint Res. 2020;9(3):90–98.

scholarly journals LINC00184 involved in the regulatory network of ANGPT2 via ceRNA mediated miR-145 inhibition.

2019 ◽  
Vol 5 (suppl) ◽  
pp. 17-17
Author(s):  
Haiyan Piao
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Regulatory Network ◽  
Poor Prognosis ◽  
Differentially Expressed ◽  
Competing Endogenous Rna ◽  
Carcinogenic Effect ◽  
Study Methods ◽  
Non Coding Rnas ◽  
Potential Biomarkers

17 Background: Abnormal gene expression is closely related to the development and poor prognosis of gastric cancer (GC). Since gene does not work alone, we are aimed to elucidate the potential networks between mRNA and non-coding RNAs (ncRNAs) in this study. Methods: Based on the TCGA database, we obtained the differentially expressed RNAs and constructed the competing endogenous RNA (ceRNA) regulatory network. Results: We found a regulatory network based on ANGPT2. We observed ANGPT2 is overexpressed in GC cells and tissues and associated with poor prognosis. ANGPT2 promotes proliferation, invasion, and EMT in GC cells, which could be abolished by miR-145. In addition, LINC00184 can be used as a ceRNA to inhibit the expression of miR-145, thus enhancing the carcinogenic effect of ANGPT2. Conclusions: Our results suggested that LINC00184/miR-145/ANGPT2 axis plays an important role in the occurrence and development of GC and may be potential biomarkers and targets for the treatment of GC.

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