A novel ferroptosis-related gene signature can predict prognosis and influence immune microenvironment in acute myeloid leukemia
DOI:
https://doi.org/10.17305/bjbms.2021.6274Keywords:
Acute myeloid leukemia, ferroptosis, prognostic gene signature, overall survival, tumor immune microenvironment, drug resistanceAbstract
Acute myeloid leukemia (AML) is a highly heterogeneous hematopoietic malignancy that strongly correlates with poor clinical outcomes. Ferroptosis is an iron-dependent, non-apoptotic form of regulated cell death which plays an important role in various human cancers. Nevertheless, the prognostic significance and functions of ferroptosis-related genes (FRGs) in AML have not received sufficient attention. The aim of this article was to evaluate the association between FRGs levels and AML prognosis using publicly available RNA-sequencing datasets. The univariate Cox regression analysis identified 20 FRGs that correlate with patient overall survival. The LASSO Cox regression model was used to construct a prognostic 12-gene risk model using a TCGA cohort, and internal and external validation proved the signature efficient. The 12-FRGs signature was then used to assign patients into high- and low-risk groups, with the former exhibiting markedly reduced overall survival, compared to the low-risk group. ROC curve analysis verified the predictive ability of the risk model. Functional analysis showed that immune status and drug sensitivity differed between the 2 risk groups. In summary, FRGs is a promising candidate biomarker and therapeutic target for AML.
Downloads
![Construction of a novel ferroptosis-related gene signature for predicting prognosis and immune microenvironment in acute myeloid leukemia](https://bjbms.org/ojs/public/journals/1/article_6274_cover_en_US.jpg)
Downloads
Additional Files
Published
Issue
Section
Categories
License
Copyright (c) 2021 Xianbo Huang, De Zhou, Xiujin Ye, Jie Jin
![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Funding data
-
National Natural Science Foundation of China
Grant numbers NO. 81900152 -
National Natural Science Foundation of China-Zhejiang Joint Fund for the Integration of Industrialization and Informatization
Grant numbers NO. LQ19H080005 -
Department of Health of Zhejiang Province
Grant numbers NO. 2020KY113