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A 16-miRNA Prognostic Model to Predict Overall Survival in Neuroblastoma

Neuroblastoma is the most malignant childhood tumor. The outcome of neuroblastoma is hard to predict due to the limitation of prognostic markers. In our study, we constructed a 16-miRNA prognostic model to predict the overall survival of neuroblastoma patients for early diagnosis. A total of 205 DE...

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Autores principales: Wang, Jiepin, Xiao, Dong, Wang, Junxiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9278893/
https://www.ncbi.nlm.nih.gov/pubmed/35846139
http://dx.doi.org/10.3389/fgene.2022.827842
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author Wang, Jiepin
Xiao, Dong
Wang, Junxiang
author_facet Wang, Jiepin
Xiao, Dong
Wang, Junxiang
author_sort Wang, Jiepin
collection PubMed
description Neuroblastoma is the most malignant childhood tumor. The outcome of neuroblastoma is hard to predict due to the limitation of prognostic markers. In our study, we constructed a 16-miRNA prognostic model to predict the overall survival of neuroblastoma patients for early diagnosis. A total of 205 DE miRNAs were screened using RNA sequencing data from GSE121513. Lasso Cox regression analysis generated a 16-miRNA signature consisting of hsa-let-7c, hsa-miR-135a, hsa-miR-137, hsa-miR-146a, hsa-miR-149, hsa-miR-15a, hsa-miR-195, hsa-miR-197, hsa-miR-200c, hsa-miR-204, hsa-miR-302a, hsa-miR-331, hsa-miR-345, hsa-miR-383, hsa-miR-93, and hsa-miR-9star. The concordance index of multivariate Cox regression analysis was 0.9, and the area under the curve (AUC) values of 3-year and 5-year survival were 0.92 and 0.943, respectively. The mechanism was further investigated using the TCGA and GSE90689 datasets. Two miRNA–gene interaction networks were constructed among DEGs from two datasets. Functional analysis revealed that immune-related processes were involved in the initiation and metastasis of neuroblastoma. CIBERSORT and survival analysis suggested that lower CD8 T-cell proportion and higher SPTA1 expressions were related to a better prognosis. Our study demonstrated that the miRNA signature may be useful in prognosis prediction and management improvement.
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spelling pubmed-92788932022-07-14 A 16-miRNA Prognostic Model to Predict Overall Survival in Neuroblastoma Wang, Jiepin Xiao, Dong Wang, Junxiang Front Genet Genetics Neuroblastoma is the most malignant childhood tumor. The outcome of neuroblastoma is hard to predict due to the limitation of prognostic markers. In our study, we constructed a 16-miRNA prognostic model to predict the overall survival of neuroblastoma patients for early diagnosis. A total of 205 DE miRNAs were screened using RNA sequencing data from GSE121513. Lasso Cox regression analysis generated a 16-miRNA signature consisting of hsa-let-7c, hsa-miR-135a, hsa-miR-137, hsa-miR-146a, hsa-miR-149, hsa-miR-15a, hsa-miR-195, hsa-miR-197, hsa-miR-200c, hsa-miR-204, hsa-miR-302a, hsa-miR-331, hsa-miR-345, hsa-miR-383, hsa-miR-93, and hsa-miR-9star. The concordance index of multivariate Cox regression analysis was 0.9, and the area under the curve (AUC) values of 3-year and 5-year survival were 0.92 and 0.943, respectively. The mechanism was further investigated using the TCGA and GSE90689 datasets. Two miRNA–gene interaction networks were constructed among DEGs from two datasets. Functional analysis revealed that immune-related processes were involved in the initiation and metastasis of neuroblastoma. CIBERSORT and survival analysis suggested that lower CD8 T-cell proportion and higher SPTA1 expressions were related to a better prognosis. Our study demonstrated that the miRNA signature may be useful in prognosis prediction and management improvement. Frontiers Media S.A. 2022-06-29 /pmc/articles/PMC9278893/ /pubmed/35846139 http://dx.doi.org/10.3389/fgene.2022.827842 Text en Copyright © 2022 Wang, Xiao and Wang. 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
Wang, Jiepin
Xiao, Dong
Wang, Junxiang
A 16-miRNA Prognostic Model to Predict Overall Survival in Neuroblastoma
title A 16-miRNA Prognostic Model to Predict Overall Survival in Neuroblastoma
title_full A 16-miRNA Prognostic Model to Predict Overall Survival in Neuroblastoma
title_fullStr A 16-miRNA Prognostic Model to Predict Overall Survival in Neuroblastoma
title_full_unstemmed A 16-miRNA Prognostic Model to Predict Overall Survival in Neuroblastoma
title_short A 16-miRNA Prognostic Model to Predict Overall Survival in Neuroblastoma
title_sort 16-mirna prognostic model to predict overall survival in neuroblastoma
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9278893/
https://www.ncbi.nlm.nih.gov/pubmed/35846139
http://dx.doi.org/10.3389/fgene.2022.827842
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