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Construction of a Nine-MicroRNA-Based Signature to Predict the Overall Survival of Esophageal Cancer Patients

BACKGROUND: Esophageal cancer (EC) is a common malignant tumor. MicroRNAs (miRNAs) play a key role in the occurrence and metastasis and are closely related to the prognosis of EC. Therefore, it will provide a powerful tool to predict the overall survival (OS) of EC patients based on miRNAs expressio...

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Autores principales: Zhang, Xiaobin, He, Yi, Gu, Haiyong, Liu, Zhichao, Li, Bin, Yang, Yang, Hao, Jie, Hua, Rong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8170160/
https://www.ncbi.nlm.nih.gov/pubmed/34093662
http://dx.doi.org/10.3389/fgene.2021.670405
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author Zhang, Xiaobin
He, Yi
Gu, Haiyong
Liu, Zhichao
Li, Bin
Yang, Yang
Hao, Jie
Hua, Rong
author_facet Zhang, Xiaobin
He, Yi
Gu, Haiyong
Liu, Zhichao
Li, Bin
Yang, Yang
Hao, Jie
Hua, Rong
author_sort Zhang, Xiaobin
collection PubMed
description BACKGROUND: Esophageal cancer (EC) is a common malignant tumor. MicroRNAs (miRNAs) play a key role in the occurrence and metastasis and are closely related to the prognosis of EC. Therefore, it will provide a powerful tool to predict the overall survival (OS) of EC patients based on miRNAs expression in EC tissues and blood samples. METHODS: Five independent databases, TCGA, GSE106817, GSE113486, GSE122497, and GSE112264, were used to construct nine-miRna signature and nomograms for prognosis. The bioinformatics analysis was used to predict the enrichment pathways of targets. RESULTS: A total of 132 overexpressed miRNAs and 23 suppressed miRNAs showed significant differential expression in both EC serum and tissue samples compared with normal samples. We also showed that nine miRNAs were related to the prognosis of EC. Higher levels of miR-15a-5p, miR-92a-3p, miR-92a-1-5p, miR-590-5p, miR-324-5p, miR-25-3p, miR-181b-5p, miR-421, and miR-93-5p were correlated to the shorter survival time in patients with EC. In addition, we constructed a risk prediction model based on the levels of nine differentially expressed miRNAs (DEMs) and found that the OS time of EC patients with high-risk score was shorter than that of EC patients with low-risk score. Furthermore, our results showed that the risk prediction scores of EC samples were higher than those of normal samples. Finally, the area under the curve (AUC) was used to analyze the risk characteristics of EC and normal controls. By calculating the AUC and the calibration curve, the RNA signature showed a good performance. Bioinformatics analysis showed that nine DEMs were associated with several crucial signaling, including p53, FoxO, PI3K-Akt, HIF-1, and TORC1 signaling. Finally, 14 messenger RNAs (mRNAs) were identified as hub targets of nine miRNAs, including BTRC, SIAH1, RNF138, CDC27, NEDD4L, MKRN1, RLIM, FBXO11, RNF34, MYLIP, FBXW7, RNF4, UBE3C, and RNF111. TCGA dataset validation showed that these hub targets were significantly differently expressed in EC tissues compared with normal samples. CONCLUSION: We have constructed maps and nomograms of nine-miRna risk signals associated with EC prognosis. Bioinformatics analysis revealed that nine DEMs were associated with several crucial signaling, including p53, FoxO, PI3K-Akt, HIF-1, and TORC1 signaling, in EC. We think that this study will provide clinicians with an effective decision-making tool.
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spelling pubmed-81701602021-06-03 Construction of a Nine-MicroRNA-Based Signature to Predict the Overall Survival of Esophageal Cancer Patients Zhang, Xiaobin He, Yi Gu, Haiyong Liu, Zhichao Li, Bin Yang, Yang Hao, Jie Hua, Rong Front Genet Genetics BACKGROUND: Esophageal cancer (EC) is a common malignant tumor. MicroRNAs (miRNAs) play a key role in the occurrence and metastasis and are closely related to the prognosis of EC. Therefore, it will provide a powerful tool to predict the overall survival (OS) of EC patients based on miRNAs expression in EC tissues and blood samples. METHODS: Five independent databases, TCGA, GSE106817, GSE113486, GSE122497, and GSE112264, were used to construct nine-miRna signature and nomograms for prognosis. The bioinformatics analysis was used to predict the enrichment pathways of targets. RESULTS: A total of 132 overexpressed miRNAs and 23 suppressed miRNAs showed significant differential expression in both EC serum and tissue samples compared with normal samples. We also showed that nine miRNAs were related to the prognosis of EC. Higher levels of miR-15a-5p, miR-92a-3p, miR-92a-1-5p, miR-590-5p, miR-324-5p, miR-25-3p, miR-181b-5p, miR-421, and miR-93-5p were correlated to the shorter survival time in patients with EC. In addition, we constructed a risk prediction model based on the levels of nine differentially expressed miRNAs (DEMs) and found that the OS time of EC patients with high-risk score was shorter than that of EC patients with low-risk score. Furthermore, our results showed that the risk prediction scores of EC samples were higher than those of normal samples. Finally, the area under the curve (AUC) was used to analyze the risk characteristics of EC and normal controls. By calculating the AUC and the calibration curve, the RNA signature showed a good performance. Bioinformatics analysis showed that nine DEMs were associated with several crucial signaling, including p53, FoxO, PI3K-Akt, HIF-1, and TORC1 signaling. Finally, 14 messenger RNAs (mRNAs) were identified as hub targets of nine miRNAs, including BTRC, SIAH1, RNF138, CDC27, NEDD4L, MKRN1, RLIM, FBXO11, RNF34, MYLIP, FBXW7, RNF4, UBE3C, and RNF111. TCGA dataset validation showed that these hub targets were significantly differently expressed in EC tissues compared with normal samples. CONCLUSION: We have constructed maps and nomograms of nine-miRna risk signals associated with EC prognosis. Bioinformatics analysis revealed that nine DEMs were associated with several crucial signaling, including p53, FoxO, PI3K-Akt, HIF-1, and TORC1 signaling, in EC. We think that this study will provide clinicians with an effective decision-making tool. Frontiers Media S.A. 2021-05-19 /pmc/articles/PMC8170160/ /pubmed/34093662 http://dx.doi.org/10.3389/fgene.2021.670405 Text en Copyright © 2021 Zhang, He, Gu, Liu, Li, Yang, Hao and Hua. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Zhang, Xiaobin
He, Yi
Gu, Haiyong
Liu, Zhichao
Li, Bin
Yang, Yang
Hao, Jie
Hua, Rong
Construction of a Nine-MicroRNA-Based Signature to Predict the Overall Survival of Esophageal Cancer Patients
title Construction of a Nine-MicroRNA-Based Signature to Predict the Overall Survival of Esophageal Cancer Patients
title_full Construction of a Nine-MicroRNA-Based Signature to Predict the Overall Survival of Esophageal Cancer Patients
title_fullStr Construction of a Nine-MicroRNA-Based Signature to Predict the Overall Survival of Esophageal Cancer Patients
title_full_unstemmed Construction of a Nine-MicroRNA-Based Signature to Predict the Overall Survival of Esophageal Cancer Patients
title_short Construction of a Nine-MicroRNA-Based Signature to Predict the Overall Survival of Esophageal Cancer Patients
title_sort construction of a nine-microrna-based signature to predict the overall survival of esophageal cancer patients
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8170160/
https://www.ncbi.nlm.nih.gov/pubmed/34093662
http://dx.doi.org/10.3389/fgene.2021.670405
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