Literature DB >> 31509748

AML Subtype Is a Major Determinant of the Association between Prognostic Gene Expression Signatures and Their Clinical Significance.

Caroline R M Wiggers1, Mirna L Baak2, Edwin Sonneveld3, Edward E S Nieuwenhuis4, Marije Bartels5, Menno P Creyghton6.   

Abstract

Relapse in acute myeloid leukemia (AML) may result from variable genetic origins or convergence on common biological processes. Exploiting the specificity and sensitivity of regulatory DNA, we analyze patient samples of multiple clinical outcomes covering various AML molecular subtypes. We uncover regulatory variation among patients translating into a transcriptional signature that predicts relapse risk. In addition, we find clusters of coexpressed genes within this signature selectively link to relapse risk in distinct patient subgroups defined by molecular subtype or AML maturation. Analyzing these gene clusters and the AML subtypes separately enhances their prognostic value substantially and provides insight in the mechanisms underlying relapse risk across the distinct patient subgroups. We propose that prognostic gene expression signatures in AML are valid only within patient subgroups and do not transcend these subgroups.
Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  acute myeloid leukemia; gene regulation; gene regulatory elements; relapse; subtype heterogeneity

Mesh:

Substances:

Year:  2019        PMID: 31509748     DOI: 10.1016/j.celrep.2019.08.012

Source DB:  PubMed          Journal:  Cell Rep            Impact factor:   9.423


  7 in total

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Authors:  Nan Zhang; Ping Zhang; Ying Chen; Shifeng Lou; Hanqing Zeng; Jianchuan Deng
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Journal:  Blood Adv       Date:  2022-01-11

4.  Identification of Survival-Related Genes in Acute Myeloid Leukemia (AML) Based on Cytogenetically Normal AML Samples Using Weighted Gene Coexpression Network Analysis.

Authors:  Tingting Chen; Juan Zhang; Yinying Wang; Hebing Zhou
Journal:  Dis Markers       Date:  2022-09-29       Impact factor: 3.464

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Authors:  Zhenqiu Liu; Vladimir S Spiegelman; Hong-Gang Wang
Journal:  Cancer Rep (Hoboken)       Date:  2021-12-04

6.  Bipartite graph-based approach for clustering of cell lines by gene expression-drug response associations.

Authors:  Calvin Chi; Yuting Ye; Bin Chen; Haiyan Huang
Journal:  Bioinformatics       Date:  2021-03-03       Impact factor: 6.937

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Authors:  Daniela Lamorte; Immacolata Faraone; Ilaria Laurenzana; Stefania Trino; Daniela Russo; Dilip K Rai; Maria Francesca Armentano; Pellegrino Musto; Alessandro Sgambato; Luciana De De Luca; Luigi Milella; Antonella Caivano
Journal:  Molecules       Date:  2020-10-22       Impact factor: 4.411

  7 in total

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