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Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome

Myelodysplastic syndrome (MDS) is clonal disease featured by ineffective haematopoiesis and potential progression into acute myeloid leukaemia (AML). At present, the risk stratification and prognosis of MDS need to be further optimized. A prognostic model was constructed by the least absolute shrink...

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Autores principales: Hu, Fang, Chen, Si‐liang, Dai, Yu‐jun, Wang, Yun, Qin, Zhe‐yuan, Li, Huan, Shu, Ling‐ling, Li, Jin‐yuan, Huang, Han‐ying, Liang, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7294120/
https://www.ncbi.nlm.nih.gov/pubmed/32337851
http://dx.doi.org/10.1111/jcmm.15283
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author Hu, Fang
Chen, Si‐liang
Dai, Yu‐jun
Wang, Yun
Qin, Zhe‐yuan
Li, Huan
Shu, Ling‐ling
Li, Jin‐yuan
Huang, Han‐ying
Liang, Yang
author_facet Hu, Fang
Chen, Si‐liang
Dai, Yu‐jun
Wang, Yun
Qin, Zhe‐yuan
Li, Huan
Shu, Ling‐ling
Li, Jin‐yuan
Huang, Han‐ying
Liang, Yang
author_sort Hu, Fang
collection PubMed
description Myelodysplastic syndrome (MDS) is clonal disease featured by ineffective haematopoiesis and potential progression into acute myeloid leukaemia (AML). At present, the risk stratification and prognosis of MDS need to be further optimized. A prognostic model was constructed by the least absolute shrinkage and selection operator (LASSO) regression analysis for MDS patients based on the identified metabolic gene panel in training cohort, followed by external validation in an independent cohort. The patients with lower risk had better prognosis than patients with higher risk. The constructed model was verified as an independent prognostic factor for MDS patients with hazard ratios of 3.721 (1.814‐7.630) and 2.047 (1.013‐4.138) in the training cohort and validation cohort, respectively. The AUC of 3‐year overall survival was 0.846 and 0.743 in the training cohort and validation cohort, respectively. The high‐risk score was significantly related to other clinical prognostic characteristics, including higher bone marrow blast cells and lower absolute neutrophil count. Moreover, gene set enrichment analyses (GSEA) showed several significantly enriched pathways, with potential indication of the pathogenesis. In this study, we identified a novel stable metabolic panel, which might not only reveal the dysregulated metabolic microenvironment, but can be used to predict the prognosis of MDS.
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spelling pubmed-72941202020-06-15 Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome Hu, Fang Chen, Si‐liang Dai, Yu‐jun Wang, Yun Qin, Zhe‐yuan Li, Huan Shu, Ling‐ling Li, Jin‐yuan Huang, Han‐ying Liang, Yang J Cell Mol Med Original Articles Myelodysplastic syndrome (MDS) is clonal disease featured by ineffective haematopoiesis and potential progression into acute myeloid leukaemia (AML). At present, the risk stratification and prognosis of MDS need to be further optimized. A prognostic model was constructed by the least absolute shrinkage and selection operator (LASSO) regression analysis for MDS patients based on the identified metabolic gene panel in training cohort, followed by external validation in an independent cohort. The patients with lower risk had better prognosis than patients with higher risk. The constructed model was verified as an independent prognostic factor for MDS patients with hazard ratios of 3.721 (1.814‐7.630) and 2.047 (1.013‐4.138) in the training cohort and validation cohort, respectively. The AUC of 3‐year overall survival was 0.846 and 0.743 in the training cohort and validation cohort, respectively. The high‐risk score was significantly related to other clinical prognostic characteristics, including higher bone marrow blast cells and lower absolute neutrophil count. Moreover, gene set enrichment analyses (GSEA) showed several significantly enriched pathways, with potential indication of the pathogenesis. In this study, we identified a novel stable metabolic panel, which might not only reveal the dysregulated metabolic microenvironment, but can be used to predict the prognosis of MDS. John Wiley and Sons Inc. 2020-04-26 2020-06 /pmc/articles/PMC7294120/ /pubmed/32337851 http://dx.doi.org/10.1111/jcmm.15283 Text en © 2020 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Hu, Fang
Chen, Si‐liang
Dai, Yu‐jun
Wang, Yun
Qin, Zhe‐yuan
Li, Huan
Shu, Ling‐ling
Li, Jin‐yuan
Huang, Han‐ying
Liang, Yang
Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome
title Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome
title_full Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome
title_fullStr Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome
title_full_unstemmed Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome
title_short Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome
title_sort identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7294120/
https://www.ncbi.nlm.nih.gov/pubmed/32337851
http://dx.doi.org/10.1111/jcmm.15283
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