Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Zhao J

PROVIDER: S-EPMC7341399 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Zhao Jun J Su Yu Y Jiao Jianfei J Wang Zhengchun Z Fang Xiangchun X He Xuefeng X Zhang Xiaofeng X Liu Zhao Z Xu Xilin X

BioMed research international 20200520

<h4>Background</h4>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).<h4>Methods</h4>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 ...[more]

PMID: 32685450

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Project description:BackgroundPulpitis is a common inflammatory disease that affects dental pulp. It is important to understand the molecular signals of inflammation and repair associated with this process. Increasing evidence has revealed that long noncoding RNAs (lncRNAs), via competitively sponging microRNAs (miRNAs), can act as competing endogenous RNAs (ceRNAs) to regulate inflammation and reparative responses. The aim of this study was to elucidate the potential roles of lncRNA, miRNA and messenger RNA (mRNA) ceRNA networks in pulpitis tissues compared to normal control tissues.MethodsThe oligo and limma packages were used to identify differentially expressed lncRNAs and mRNAs (DElncRNAs and DEmRNAs, respectively) based on expression profiles in two datasets, GSE92681 and GSE77459, from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were further analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein-protein interaction (PPI) networks and modules were established to screen hub genes using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and the Molecular Complex Detection (MCODE) plugin for Cytoscape, respectively. Furthermore, an lncRNA-miRNA-mRNA-hub genes regulatory network was constructed to investigate mechanisms related to the progression and prognosis of pulpitis. Then, quantitative real-time polymerase chain reaction (qRT-PCR) was applied to verify critical lncRNAs that may significantly affect the pathogenesis in inflamed and normal human dental pulp.ResultsA total of 644 upregulated and 264 downregulated differentially expressed genes (DEGs) in pulpitis samples were identified from the GSE77459 dataset, while 8 up- and 19 downregulated probes associated with lncRNA were identified from the GSE92681 dataset. Protein-protein interaction (PPI) based on STRING analysis revealed a network of DEGs containing 4,929 edges and 623 nodes. Upon combined analysis of the constructed PPI network and the MCODE results, 10 hub genes, including IL6, IL8, PTPRC, IL1B, TLR2, ITGAM, CCL2, PIK3CG, ICAM1, and PIK3CD, were detected in the network. Next, a ceRNA regulatory relationship consisting of one lncRNA (PVT1), one miRNA (hsa-miR-455-5p) and two mRNAs (SOCS3 and PLXNC1) was established. Then, we constructed the network in which the regulatory relationship between ceRNA and hub genes was summarized. Finally, our qRT-PCR results confirmed significantly higher levels of PVT1 transcript in inflamed pulp than in normal pulp tissues (p = 0.03).ConclusionOur study identified a novel lncRNA-mediated ceRNA regulatory mechanisms in the pathogenesis of pulpitis.

Project description:BackgroundIncreasing evidence has underscored the role of long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in the development and progression of tumors. Nevertheless, lncRNA biomarkers in lncRNA-related ceRNA network that can predict the prognosis of breast cancer (BC) are still lacking. The aim of our study was to identify potential lncRNA signatures capable of predicting overall survival (OS) of BC patients.MethodsThe RNA sequencing data and clinical characteristics of BC patients were obtained from the Cancer Genome Atlas database, and differentially expressed lncRNA (DElncRNAs), DEmRNAs, and DEmiRNAs were then identified between BC and normal breast tissue samples. Subsequently, the lncRNA-miRNA-mRNA ceRNA network of BC was established, and the gene oncology enrichment analyses for the DEmRNAs interacting with lncRNAs in the ceRNA network was implemented. Using univariate and multivariate Cox regression analyses, a four-lncRNA signature was developed and used for predicting the survival in BC patients. We applied receiver operating characteristic analysis to assess the performance of our model.ResultsA total of 1061 DElncRNAs, 2150 DEmRNAs, and 82 DEmiRNAs were identified between BC and normal breast tissue samples. A lncRNA-miRNA-mRNA ceRNA network of BC was established, which comprised of 8 DEmiRNAs, 48 DElncRNAs, and 10 DEmRNAs. Further gene oncology enrichment analyses revealed that the DEmRNAs interacting with lncRNAs in the ceRNA network participated in cell leading edge, protease binding, alpha-catenin binding, gamma-catenin binding, and adenylate cyclase binding. A univariate regression analysis of the DElncRNAs revealed 7 lncRNAs (ADAMTS9-AS1, AC061992.1, LINC00536, HOTAIR, AL391421.1, TLR8-AS1 and LINC00491) that were associated with OS of BC patients. A multivariate Cox regression analysis demonstrated that 4 of those lncRNAs (ADAMTS9-AS1, LINC00536, AL391421.1 and LINC00491) had significant prognostic value, and their cumulative risk score indicated that this 4-lncRNA signature independently predicted OS in BC patients. Furthermore, the area under the curve of the 4-lncRNA signature associated with 3-year survival was 0.696.ConclusionsThe current study provides novel insights into the lncRNA-related ceRNA network in BC and the 4 lncRNA biomarkers may be independent prognostic signatures in predicting the survival of BC patients.

Dataset Information

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

Publications

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

Similar Datasets

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