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lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia

Acute myeloid leukemia (AML) is a highly heterogeneous hematologic malignancy with great variability of prognostic behaviors. Previous studies have reported that long non-coding RNAs (lncRNAs) play an important role in AML and may thus be used as potential prognostic biomarkers. However, thus use of...

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Autores principales: Pan, Jia-Qi, Zhang, Yan-Qing, Wang, Jing-Hua, Xu, Ping, Wang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5360362/
https://www.ncbi.nlm.nih.gov/pubmed/28204819
http://dx.doi.org/10.3892/ijmm.2017.2888
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author Pan, Jia-Qi
Zhang, Yan-Qing
Wang, Jing-Hua
Xu, Ping
Wang, Wei
author_facet Pan, Jia-Qi
Zhang, Yan-Qing
Wang, Jing-Hua
Xu, Ping
Wang, Wei
author_sort Pan, Jia-Qi
collection PubMed
description Acute myeloid leukemia (AML) is a highly heterogeneous hematologic malignancy with great variability of prognostic behaviors. Previous studies have reported that long non-coding RNAs (lncRNAs) play an important role in AML and may thus be used as potential prognostic biomarkers. However, thus use of lncRNAs as prognostic biomarkers in AML and their detailed mechanisms of action in this disease have not yet been well characterized. For this purpose, in the present study, the expression levels of lncRNAs and mRNAs were calculated using the RNA-seq V2 data for AML, following which a lncRNA-lncRNA co-expression network (LLCN) was constructed. This revealed a total of 8 AML prognosis-related lncRNA modules were identified, which displayed a significant correlation with patient survival (p≤0.05). Subsequently, a prognosis-related lncRNA module pathway network was constructed to interpret the functional mechanism of the prognostic modules in AML. The results indicated that these prognostic modules were involved in the AML pathway, chemokine signaling pathway and WNT signaling pathway, all of which play important roles in AML. Furthermore, the investigation of lncRNAs in these prognostic modules suggested that an lncRNA (ZNF571-AS1) may be involved in AML via the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway by regulating KIT and STAT5. The results of the present study not only provide potential lncRNA modules as prognostic biomarkers, but also provide further insight into the molecular mechanisms of action of lncRNAs.
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spelling pubmed-53603622017-04-10 lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia Pan, Jia-Qi Zhang, Yan-Qing Wang, Jing-Hua Xu, Ping Wang, Wei Int J Mol Med Articles Acute myeloid leukemia (AML) is a highly heterogeneous hematologic malignancy with great variability of prognostic behaviors. Previous studies have reported that long non-coding RNAs (lncRNAs) play an important role in AML and may thus be used as potential prognostic biomarkers. However, thus use of lncRNAs as prognostic biomarkers in AML and their detailed mechanisms of action in this disease have not yet been well characterized. For this purpose, in the present study, the expression levels of lncRNAs and mRNAs were calculated using the RNA-seq V2 data for AML, following which a lncRNA-lncRNA co-expression network (LLCN) was constructed. This revealed a total of 8 AML prognosis-related lncRNA modules were identified, which displayed a significant correlation with patient survival (p≤0.05). Subsequently, a prognosis-related lncRNA module pathway network was constructed to interpret the functional mechanism of the prognostic modules in AML. The results indicated that these prognostic modules were involved in the AML pathway, chemokine signaling pathway and WNT signaling pathway, all of which play important roles in AML. Furthermore, the investigation of lncRNAs in these prognostic modules suggested that an lncRNA (ZNF571-AS1) may be involved in AML via the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway by regulating KIT and STAT5. The results of the present study not only provide potential lncRNA modules as prognostic biomarkers, but also provide further insight into the molecular mechanisms of action of lncRNAs. D.A. Spandidos 2017-03 2017-02-13 /pmc/articles/PMC5360362/ /pubmed/28204819 http://dx.doi.org/10.3892/ijmm.2017.2888 Text en Copyright: © Pan 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
Pan, Jia-Qi
Zhang, Yan-Qing
Wang, Jing-Hua
Xu, Ping
Wang, Wei
lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia
title lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia
title_full lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia
title_fullStr lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia
title_full_unstemmed lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia
title_short lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia
title_sort lncrna co-expression network model for the prognostic analysis of acute myeloid leukemia
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5360362/
https://www.ncbi.nlm.nih.gov/pubmed/28204819
http://dx.doi.org/10.3892/ijmm.2017.2888
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