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Potential Five-MicroRNA Signature Model for the Prediction of Prognosis in Patients with Wilms Tumor

BACKGROUND: Wilms tumor (WT) is the most common type of pediatric renal malignancy, and is associated with poor prognosis. The aim of the present study was to identify microRNA (miRNA) signatures which might predict prognosis and categorize WTs into high- and low-risk subgroups. MATERIAL/METHODS: Th...

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Autores principales: Gong, Yihang, Zou, Baojia, Chen, Jianxu, Ding, Lei, Li, Peiping, Chen, Jiafan, Chen, Jiandi, Zhang, Baimeng, Li, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Scientific Literature, Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6668497/
https://www.ncbi.nlm.nih.gov/pubmed/31328722
http://dx.doi.org/10.12659/MSM.916230
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author Gong, Yihang
Zou, Baojia
Chen, Jianxu
Ding, Lei
Li, Peiping
Chen, Jiafan
Chen, Jiandi
Zhang, Baimeng
Li, Jian
author_facet Gong, Yihang
Zou, Baojia
Chen, Jianxu
Ding, Lei
Li, Peiping
Chen, Jiafan
Chen, Jiandi
Zhang, Baimeng
Li, Jian
author_sort Gong, Yihang
collection PubMed
description BACKGROUND: Wilms tumor (WT) is the most common type of pediatric renal malignancy, and is associated with poor prognosis. The aim of the present study was to identify microRNA (miRNA) signatures which might predict prognosis and categorize WTs into high- and low-risk subgroups. MATERIAL/METHODS: The miRNA expression profiles of WT patients and normal samples were obtained from the Therapeutically Applicable Research to Generate Effective Treatment database. Differentially expressed miRNAs between WT patients and normal samples were identified using the EdgeR package. Subsequently, correlations between differentially expressed miRNAs and the prognosis of overall survival were analyzed. Enrichment analyses for the targeted mRNAs were conducted via the Database for Annotation, Visualization, and Integration Discovery. RESULTS: A total of 154 miRNAs were identified as differentially expressed in WT. Of those, 18 miRNAs were associated with overall survival (P<0.05). A prognostic signature of 5 differentially expressed miRNAs (i.e., has-mir-149, has-mir-7112, has-mir-940, has-mir-1248, and has-mir-490) was constructed to classify the patients into high- and low-risk subgroups. The targeted mRNAs of these prognostic miRNAs were primarily enriched in Gene Ontology terms (i.e., protein autophosphorylation, protein dephosphorylation, and stress-activated MAPK cascade) and the Kyoto Encyclopedia of Genes and Genomes signaling pathways (i.e., MAPK, AMPK, and PI3K-Akt). CONCLUSIONS: The 5-miRNA signature model might be useful in determining the prognosis of WT patients. As a promising prediction tool, this prognosis signature might serve as a potential biomarker for WT patients.
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spelling pubmed-66684972019-08-16 Potential Five-MicroRNA Signature Model for the Prediction of Prognosis in Patients with Wilms Tumor Gong, Yihang Zou, Baojia Chen, Jianxu Ding, Lei Li, Peiping Chen, Jiafan Chen, Jiandi Zhang, Baimeng Li, Jian Med Sci Monit Clinical Research BACKGROUND: Wilms tumor (WT) is the most common type of pediatric renal malignancy, and is associated with poor prognosis. The aim of the present study was to identify microRNA (miRNA) signatures which might predict prognosis and categorize WTs into high- and low-risk subgroups. MATERIAL/METHODS: The miRNA expression profiles of WT patients and normal samples were obtained from the Therapeutically Applicable Research to Generate Effective Treatment database. Differentially expressed miRNAs between WT patients and normal samples were identified using the EdgeR package. Subsequently, correlations between differentially expressed miRNAs and the prognosis of overall survival were analyzed. Enrichment analyses for the targeted mRNAs were conducted via the Database for Annotation, Visualization, and Integration Discovery. RESULTS: A total of 154 miRNAs were identified as differentially expressed in WT. Of those, 18 miRNAs were associated with overall survival (P<0.05). A prognostic signature of 5 differentially expressed miRNAs (i.e., has-mir-149, has-mir-7112, has-mir-940, has-mir-1248, and has-mir-490) was constructed to classify the patients into high- and low-risk subgroups. The targeted mRNAs of these prognostic miRNAs were primarily enriched in Gene Ontology terms (i.e., protein autophosphorylation, protein dephosphorylation, and stress-activated MAPK cascade) and the Kyoto Encyclopedia of Genes and Genomes signaling pathways (i.e., MAPK, AMPK, and PI3K-Akt). CONCLUSIONS: The 5-miRNA signature model might be useful in determining the prognosis of WT patients. As a promising prediction tool, this prognosis signature might serve as a potential biomarker for WT patients. International Scientific Literature, Inc. 2019-07-22 /pmc/articles/PMC6668497/ /pubmed/31328722 http://dx.doi.org/10.12659/MSM.916230 Text en © Med Sci Monit, 2019 This work is licensed under Creative Common Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) )
spellingShingle Clinical Research
Gong, Yihang
Zou, Baojia
Chen, Jianxu
Ding, Lei
Li, Peiping
Chen, Jiafan
Chen, Jiandi
Zhang, Baimeng
Li, Jian
Potential Five-MicroRNA Signature Model for the Prediction of Prognosis in Patients with Wilms Tumor
title Potential Five-MicroRNA Signature Model for the Prediction of Prognosis in Patients with Wilms Tumor
title_full Potential Five-MicroRNA Signature Model for the Prediction of Prognosis in Patients with Wilms Tumor
title_fullStr Potential Five-MicroRNA Signature Model for the Prediction of Prognosis in Patients with Wilms Tumor
title_full_unstemmed Potential Five-MicroRNA Signature Model for the Prediction of Prognosis in Patients with Wilms Tumor
title_short Potential Five-MicroRNA Signature Model for the Prediction of Prognosis in Patients with Wilms Tumor
title_sort potential five-microrna signature model for the prediction of prognosis in patients with wilms tumor
topic Clinical Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6668497/
https://www.ncbi.nlm.nih.gov/pubmed/31328722
http://dx.doi.org/10.12659/MSM.916230
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