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Genomic analysis of biomarkers related to the prognosis of acute myeloid leukemia

Acute myeloid leukemia (AML) is the most common childhood cancer and is a major cause of morbidity among adults with hematologic malignancies. Several novel genetic alterations, which target critical cellular pathways, including alterations in lymphoid development-regulating genes, tumor suppressors...

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Autores principales: Li, Guilan, Gao, Yang, Li, Kun, Lin, Anqi, Jiang, Zujun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7377096/
https://www.ncbi.nlm.nih.gov/pubmed/32724426
http://dx.doi.org/10.3892/ol.2020.11700
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author Li, Guilan
Gao, Yang
Li, Kun
Lin, Anqi
Jiang, Zujun
author_facet Li, Guilan
Gao, Yang
Li, Kun
Lin, Anqi
Jiang, Zujun
author_sort Li, Guilan
collection PubMed
description Acute myeloid leukemia (AML) is the most common childhood cancer and is a major cause of morbidity among adults with hematologic malignancies. Several novel genetic alterations, which target critical cellular pathways, including alterations in lymphoid development-regulating genes, tumor suppressors and oncogenes that contribute to leukemogenesis, have been identified. The present study aimed to identify molecular markers associated with the occurrence and poor prognosis of AML. Information on these molecular markers may facilitate prediction of clinical outcomes. Clinical data and RNA expression profiles of AML specimens from The Cancer Genome Atlas database were assessed. Mutation data were analyzed and mapped using the maftools package in R software. Kyoto Encyclopedia of Genes and Genomes, Reactome and Gene Ontology analyses were performed using the clusterProfiler package in R software. Furthermore, Kaplan-Meier survival analysis was performed using the survminer package in R software. The expression data of RNAs were subjected to univariate Cox regression analysis, which demonstrated that the mutation loads varied considerably among patients with AML. Subsequently, the expression data of mRNAs, microRNAs (miRNAs/miR) and long non-coding RNAs (lncRNAs) were subjected to univariate Cox regression analysis to determine the the 100 genes most associated with the survival of patients with AML, which revealed 48 mRNAs and 52 miRNAs. The top 1,900 mRNAs (P<0.05) were selected through enrichment analysis to determine their functional role in AML prognosis. The results demonstrated that these molecules were involved in the transforming growth factor-β, SMAD and fibroblast growth factor receptor-1 fusion mutant signaling pathways. Survival analysis indicated that patients with AML, with high MYH15, TREML2, ATP13A2, MMP7, hsa-let-7a-2-3p, hsa-miR-362-3p, hsa-miR-500a-5p, hsa-miR-500b-5p, hsa-miR-362-5p, LINC00987, LACAT143, THCAT393, THCAT531 and KHCAT230 expression levels had a shorter survival time compared with those without these factors. Conversely, a high KANSL1L expression level in patients was associated with a longer survival time. The present study determined genetic mutations, mRNAs, miRNAs, lncRNAs and signaling pathways involved in AML, in order to elucidate the underlying molecular mechanisms of the development and recurrence of this disease.
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spelling pubmed-73770962020-07-27 Genomic analysis of biomarkers related to the prognosis of acute myeloid leukemia Li, Guilan Gao, Yang Li, Kun Lin, Anqi Jiang, Zujun Oncol Lett Articles Acute myeloid leukemia (AML) is the most common childhood cancer and is a major cause of morbidity among adults with hematologic malignancies. Several novel genetic alterations, which target critical cellular pathways, including alterations in lymphoid development-regulating genes, tumor suppressors and oncogenes that contribute to leukemogenesis, have been identified. The present study aimed to identify molecular markers associated with the occurrence and poor prognosis of AML. Information on these molecular markers may facilitate prediction of clinical outcomes. Clinical data and RNA expression profiles of AML specimens from The Cancer Genome Atlas database were assessed. Mutation data were analyzed and mapped using the maftools package in R software. Kyoto Encyclopedia of Genes and Genomes, Reactome and Gene Ontology analyses were performed using the clusterProfiler package in R software. Furthermore, Kaplan-Meier survival analysis was performed using the survminer package in R software. The expression data of RNAs were subjected to univariate Cox regression analysis, which demonstrated that the mutation loads varied considerably among patients with AML. Subsequently, the expression data of mRNAs, microRNAs (miRNAs/miR) and long non-coding RNAs (lncRNAs) were subjected to univariate Cox regression analysis to determine the the 100 genes most associated with the survival of patients with AML, which revealed 48 mRNAs and 52 miRNAs. The top 1,900 mRNAs (P<0.05) were selected through enrichment analysis to determine their functional role in AML prognosis. The results demonstrated that these molecules were involved in the transforming growth factor-β, SMAD and fibroblast growth factor receptor-1 fusion mutant signaling pathways. Survival analysis indicated that patients with AML, with high MYH15, TREML2, ATP13A2, MMP7, hsa-let-7a-2-3p, hsa-miR-362-3p, hsa-miR-500a-5p, hsa-miR-500b-5p, hsa-miR-362-5p, LINC00987, LACAT143, THCAT393, THCAT531 and KHCAT230 expression levels had a shorter survival time compared with those without these factors. Conversely, a high KANSL1L expression level in patients was associated with a longer survival time. The present study determined genetic mutations, mRNAs, miRNAs, lncRNAs and signaling pathways involved in AML, in order to elucidate the underlying molecular mechanisms of the development and recurrence of this disease. D.A. Spandidos 2020-08 2020-06-05 /pmc/articles/PMC7377096/ /pubmed/32724426 http://dx.doi.org/10.3892/ol.2020.11700 Text en Copyright: © Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Li, Guilan
Gao, Yang
Li, Kun
Lin, Anqi
Jiang, Zujun
Genomic analysis of biomarkers related to the prognosis of acute myeloid leukemia
title Genomic analysis of biomarkers related to the prognosis of acute myeloid leukemia
title_full Genomic analysis of biomarkers related to the prognosis of acute myeloid leukemia
title_fullStr Genomic analysis of biomarkers related to the prognosis of acute myeloid leukemia
title_full_unstemmed Genomic analysis of biomarkers related to the prognosis of acute myeloid leukemia
title_short Genomic analysis of biomarkers related to the prognosis of acute myeloid leukemia
title_sort genomic analysis of biomarkers related to the prognosis of acute myeloid leukemia
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7377096/
https://www.ncbi.nlm.nih.gov/pubmed/32724426
http://dx.doi.org/10.3892/ol.2020.11700
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