Dataset Information

Screening of prognostic biomarkers for endometrial carcinoma based on a ceRNA network.

ABSTRACT: Objective:This study aims to reveal the regulation network of lncRNAs-miRNAs-mRNA in endometrial carcinoma (EC), to investigate the underlying mechanisms of EC occurrence and progression, to screen prognostic biomarkers. Methods:RNA-seq and miRNA-seq data of endometrial carcinoma were downloaded from the TCGA database. Edge.R package was used to screen differentially expressed genes. A database was searched to determine differentially expressed lncRNA-miRNA and miRNA-mRNA pairs, to construct the topological network of ceRNA, and to elucidate the key RNAs that are for a prognosis of survival. Results:We screened out 2632 mRNAs, 1178 lncRNAs and 189 miRNAs that were differentially expressed. The constructed ceRNA network included 97 lncRNAs, 20 miRNAs and 73 mRNAs. Analyzing network genes for associations with prognosies revealed 169 prognosis-associated RNAs, including 92 lncRNAs, 16miRNAs and 61 mRNAs. Conclusion:Our results reveal new potential mechanisms underlying the carcinogenesis and progression of endometrial carcinoma.

SUBMITTER: Zheng MJ

PROVIDER: S-EPMC6292375 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Screening of prognostic biomarkers for endometrial carcinoma based on a ceRNA network.

Zheng Ming-Jun MJ Gou Rui R Zhang Wen-Chao WC Nie Xin X Wang Jing J Gao Ling-Ling LL Liu Juan-Juan JJ Li Xiao X Lin Bei B

PeerJ 20181210

<h4>Objective</h4>This study aims to reveal the regulation network of lncRNAs-miRNAs-mRNA in endometrial carcinoma (EC), to investigate the underlying mechanisms of EC occurrence and progression, to screen prognostic biomarkers.<h4>Methods</h4>RNA-seq and miRNA-seq data of endometrial carcinoma were downloaded from the TCGA database. Edge.R package was used to screen differentially expressed genes. A database was searched to determine differentially expressed lncRNA-miRNA and miRNA-mRNA pairs, t ...[more]

PMID: 30581678

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Project description:BackgroundThis study aimed to identify the biomarkers related to myocardial infarction based on building lncRNA-miRNA-mRNA ceRNA regulation network.MethodsThe expression profile data were obtained from the Gene Expression Omnibus database. The differentially expressed RNAs (DElncRNAs, DEmiRNAs, and DEmRNAs) were analyzed using the limma package of R. In addition, the differential myocardial infarction-related genes were obtained and the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway on the differential myocardial infarction-related genes were analyzed. The co-expression network of lncRNA-mRNA was constructed, and the Kyoto Encyclopedia of Genes and Genomes pathway of such a co-expression network was analyzed. Moreover, the lncRNA-miRNA-mRNA ceRNA network associated with myocardial infarction was built, and the key biomarkers of the ceRNA network were verified using the GSE141512 and GSE66752 datasets.ResultsIn total, we acquired 51 DElncRNAs, 276 DEmiRNAs, and 1200 DEmRNAs as well as 291 differential myocardial infarction-related genes. Moreover, the co-expression network of lncRNA-mRNA was built, and mRNAs were found to be associated with 109 Kyoto Encyclopedia of Genes and Genomes pathways (the target gene of RP3-394A18.1 was significantly related to lipid and atherosclerosis). In addition, the ceRNA network related to myocardial infarction was constructed, and the dataset analyses of the RNAs were validated between the myocardial infarction and normal groups. It was also found that the expression levels of hsa-miR-1291 and Retinoid acid receptor-related orphan receptor α (RORA) in the myocardial infarction group were noticeably lower than those in the normal group, whereas the expression levels of ENTPD1, QSOX1, and TIMP2 in the myocardial infarction group were noticeably higher than those in the normal group.ConclusionIn this study, we built a ceRNA regulation network of myocardial infarction. This study could help identify the biomarkers related to myocardial infarction.

Project description:Background: Increasing evidence supports that competing endogenous RNAs (ceRNAs) and tumor immune infiltration act as pivotal players in tumor progression of hepatocellular carcinoma (HCC). Nonetheless, comprehensive analysis focusing on ceRNAs and immune infiltration in HCC is lacking. Methods: RNA and miRNA sequencing information, corresponding clinical annotation, and mutation data of HCC downloaded from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) project were employed to identify significant differentially expressed mRNAs (DEMs), miRNAs (DEMis), and lncRNAs (DELs) to establish a ceRNA regulatory network. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO) enrichment pathways were analyzed to functionally annotate these DEMs. A multigene-based risk signature was developed utilizing least absolute shrinkage and selection operator method (LASSO) algorithm. Moreover, survival analysis and receiver operating characteristic (ROC) analysis were applied for prognostic value validation. Seven algorithms (TIMER, XCELL, MCPcounter, QUANTISEQ, CIBERSORT, EPIC, and CIBERSORT-ABS) were utilized to characterize tumor immune microenvironment (TIME). Finally, the mutation data were analyzed by employing "maftools" package. Results: In total, 136 DELs, 128 DEMis, and 2,028 DEMs were recognized in HCC. A specific lncRNA-miRNA-mRNA network consisting of 3 lncRNAs, 12 miRNAs, and 21 mRNAs was established. A ceRNA-based prognostic signature was established to classify samples into two risk subgroups, which presented excellent prognostic performance. In additional, prognostic risk-clinical nomogram was delineated to assess risk of individual sample quantitatively. Besides, risk score was significantly associated with contexture of TIME and immunotherapeutic targets. Finally, potential interaction between risk score with tumor mutation burden (TMB) was revealed. Conclusion: In this work, comprehensive analyses of ceRNAs coexpression network will facilitate prognostic prediction, delineate complexity of TIME, and contribute insight into precision therapy for HCC.

Dataset Information

Screening of prognostic biomarkers for endometrial carcinoma based on a ceRNA network.

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Screening of prognostic biomarkers for endometrial carcinoma based on a ceRNA network.

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