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A three long non-coding RNA signature to improve survival prediction in patients with Wilms' tumor.

ABSTRACT: The prognostic significance of long non-coding RNAs (lncRNAs) in Wilms' tumor (WT) remains unclear. The present study identified differential lncRNA expression patterns between WT and normal tissues; and evaluated the prognostic value of these lncRNAs. Multidimensional data from 136 samples were retrieved from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Computational data analyses were performed to detect survival-associated molecular signatures. Kaplan-Meier survival curves revealed the prognostic prediction values for three of these lncRNAs, namely DLGAP1 antisense RNA 2 (DLGAP1-AS2), RP11-93B14.6 and RP11554F20.1. Cox regression analysis revealed that the three-lncRNA signature was significantly associated with patient survival (P<0.01). Functional enrichment analyses suggested that the target genes of these three lncRNAs may be involved in known cancer-associated pathways. The present study revealed a three-lncRNA signature consisting of DLGAP1-AS2, RP11-93B14.6 and RP11554F20.1 that may be used as a prognostic marker for patients with WT.

SUBMITTER: Ren P

PROVIDER: S-EPMC6865675 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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A three long non-coding RNA signature to improve survival prediction in patients with Wilms' tumor.

Ren Ping P Hu Meiling M

Oncology letters 20191014 6

The prognostic significance of long non-coding RNAs (lncRNAs) in Wilms' tumor (WT) remains unclear. The present study identified differential lncRNA expression patterns between WT and normal tissues; and evaluated the prognostic value of these lncRNAs. Multidimensional data from 136 samples were retrieved from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Computational data analyses were performed to detect survival-associated molecular signatures. K ...[more]

PMID: 31788091

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Project description:BackgroundWilms tumor (WT) is the most frequent malignancy of the kidney in children, and a subset of patients remains with a poor prognosis. This study aimed to identify key long non-coding RNAs (lncRNAs) related to prognosis and establish a genomic-clinicopathologic nomogram to predict survival in children with WT.MethodsClinical data of 124 WT patients and the relevant RNA sequencing data including lncRNAs expression signature of primary WT samples were obtained from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) Data Matrix. Then, lncRNAs associated with overall survival (OS) were identified through univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. The risk scores of 124 participants were calculated, and survival analyses were performed between low- and high-risk groups. A genomic-clinicopathologic nomogram was then developed and evaluated by time-dependent receiver operating characteristic (ROC) curves, including the area under the curve (AUC), calibration curve, and decision curve analysis. Subsequently, bioinformatics analyses were performed to explore the potential molecular mechanisms that affect the prognosis of WT. The package "DESeq2" was used to identify differentially expressed protein-coding genes (DEPCGs) between groups. Gene Set Enrichment Analysis (GSEA) was applied to explore the differences in pathways enrichment. The analytical tools CIBERSORTx and ESTIMATE were used to investigate the discrepancies of the immune microenvironment.ResultsA total of 10 lncRNAs were selected as independent predictors associated with OS (P<0.05). Participants in the high-risk group had a significantly worse OS and event-free survival (EFS) than those in the low-risk group (P<2E-16 and P=2.03E-04, respectively). The risk score and 3 clinicopathological features (gender, cooperative group protocol, and stage) were identified to construct the nomogram (combined model) (P=5.11E-17). The combined model (1-year AUC: 0.9272, 3-year AUC: 0.9428, 5-year AUC: 0.9259) and risk score model (1-year AUC: 0.9285, 3-year AUC: 0.9399, 5-year AUC: 0.9266) displayed higher predictive accuracy than that of the other models. Subsequently, 105 DEPCGs were identified. The GSEA revealed 4 significant pathways. Analysis with CIBERSORTx demonstrated that monocytes, macrophages M1, activated dendritic cells, and resting mast cells had significant infiltration differences between groups.ConclusionsThis study constructed a genomic-clinicopathologic nomogram, which might present a novel and efficient method for treating patients with WT.

Dataset Information

A three long non-coding RNA signature to improve survival prediction in patients with Wilms' tumor.

Publications

A three long non-coding RNA signature to improve survival prediction in patients with Wilms' tumor.

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