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Comprehensive analysis of competing endogenous RNA network and 3-mRNA signature predicting survival in papillary renal cell cancer.
Medicine (Baltimore). 2019 Jul; 98(30):e16672.M

Abstract

Long non-coding RNAs (lncRNAs) can act as competing endogenous RNAs (ceRNAs) to exert significant roles in regulating the expression of mRNAs by sequestering and binding miRNAs. To elucidate the functional roles and regulatory mechanism of lncRNAs in papillary renal cell cancer (pRCC), we conducted a comprehensive analysis of ceRNA network and constructed a mRNA signature to predict prognosis of pRCC.We collected mRNAs and lncRNAs expression profiles of 289 pRCC samples and 32 normal renal tissues, and miRNA expression profiles of 292 pRCC samples and 34 normal samples from The Cancer Genome Atlas (TCGA) database. Differential expressions of RNAs were evaluated by the "edgeR" package in R. Functional enrichment analysis of DEmRNA was performed by DAVID 6.8 and KEGG, while PPI network of top 200 DEmRNAs was conducted using the STRING database. The univariate and multivariate Cox regression were conducted to figure out the candidate DEmRNAs with predictive values in prognosis. Receiver operator characteristic (ROC) curve estimation was performed to achieve the area under the curve (AUC) of the ROC curve to judge mRNA-associated prognosic model. A ceRNA network was established relying on the basis of combination of lncRNA-miRNA interactions and miRNA-mRNA interactions.A total of 1928 DEmRNAs, 981 DElncRNAs, and 52 DEmiRNAs were identified at significance level of |log2Fold Change |>2 and adjusted P-value < .01. A 3-mRNA signatures consisting of ERG, RRM2, and EGF was constructed to predict survival in pRCC. Moreover, a pRCC-associated ceRNA network was constructed, with 57 lncRNAs, 11 miRNAs, and 28 mRNAs.Our study illustrated the regulatory mechanism of ceRNA network in papillary renal cancer. The identified mRNA signatures could be used to predict survival of pRCC.

Authors+Show Affiliations

Department of Urology.Department of Urology.Department of Urology.Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Pub Type(s)

Journal Article
Observational Study

Language

eng

PubMed ID

31348324

Citation

Zhu, Xin, et al. "Comprehensive Analysis of Competing Endogenous RNA Network and 3-mRNA Signature Predicting Survival in Papillary Renal Cell Cancer." Medicine, vol. 98, no. 30, 2019, pp. e16672.
Zhu X, Tan J, Liang Z, et al. Comprehensive analysis of competing endogenous RNA network and 3-mRNA signature predicting survival in papillary renal cell cancer. Medicine (Baltimore). 2019;98(30):e16672.
Zhu, X., Tan, J., Liang, Z., & Zhou, M. (2019). Comprehensive analysis of competing endogenous RNA network and 3-mRNA signature predicting survival in papillary renal cell cancer. Medicine, 98(30), e16672. https://doi.org/10.1097/MD.0000000000016672
Zhu X, et al. Comprehensive Analysis of Competing Endogenous RNA Network and 3-mRNA Signature Predicting Survival in Papillary Renal Cell Cancer. Medicine (Baltimore). 2019;98(30):e16672. PubMed PMID: 31348324.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Comprehensive analysis of competing endogenous RNA network and 3-mRNA signature predicting survival in papillary renal cell cancer. AU - Zhu,Xin, AU - Tan,Jianyu, AU - Liang,Zongjian, AU - Zhou,Mi, PY - 2019/7/27/entrez PY - 2019/7/28/pubmed PY - 2019/8/14/medline SP - e16672 EP - e16672 JF - Medicine JO - Medicine (Baltimore) VL - 98 IS - 30 N2 - Long non-coding RNAs (lncRNAs) can act as competing endogenous RNAs (ceRNAs) to exert significant roles in regulating the expression of mRNAs by sequestering and binding miRNAs. To elucidate the functional roles and regulatory mechanism of lncRNAs in papillary renal cell cancer (pRCC), we conducted a comprehensive analysis of ceRNA network and constructed a mRNA signature to predict prognosis of pRCC.We collected mRNAs and lncRNAs expression profiles of 289 pRCC samples and 32 normal renal tissues, and miRNA expression profiles of 292 pRCC samples and 34 normal samples from The Cancer Genome Atlas (TCGA) database. Differential expressions of RNAs were evaluated by the "edgeR" package in R. Functional enrichment analysis of DEmRNA was performed by DAVID 6.8 and KEGG, while PPI network of top 200 DEmRNAs was conducted using the STRING database. The univariate and multivariate Cox regression were conducted to figure out the candidate DEmRNAs with predictive values in prognosis. Receiver operator characteristic (ROC) curve estimation was performed to achieve the area under the curve (AUC) of the ROC curve to judge mRNA-associated prognosic model. A ceRNA network was established relying on the basis of combination of lncRNA-miRNA interactions and miRNA-mRNA interactions.A total of 1928 DEmRNAs, 981 DElncRNAs, and 52 DEmiRNAs were identified at significance level of |log2Fold Change |>2 and adjusted P-value < .01. A 3-mRNA signatures consisting of ERG, RRM2, and EGF was constructed to predict survival in pRCC. Moreover, a pRCC-associated ceRNA network was constructed, with 57 lncRNAs, 11 miRNAs, and 28 mRNAs.Our study illustrated the regulatory mechanism of ceRNA network in papillary renal cancer. The identified mRNA signatures could be used to predict survival of pRCC. SN - 1536-5964 UR - https://www.unboundmedicine.com/medline/citation/31348324/Comprehensive_analysis_of_competing_endogenous_RNA_network_and_3_mRNA_signature_predicting_survival_in_papillary_renal_cell_cancer_ L2 - http://dx.doi.org/10.1097/MD.0000000000016672 DB - PRIME DP - Unbound Medicine ER -