Identification and Validation of Ferroptosis-Related Genes As Novel Prognosis Prediction Panel for Acute Myeloid Leukemia

• Published in: Blood 2023-11, Vol.142 (Supplement 1), p.6040-6040
• Main Authors: Hong, Yaonan, Lai, Tianyi, Hu, Huijin, Ge, Hangping, Zhao, Yuechao, Shen, Yingying, Hu, Tonglin, Liu, Qi, Zhuang, Zhenchao, Zhou, Yuhong, Ye, Baodong, Wu, Dijiong
• Format: Article
• Language: English
• ISSN: 0006-4971; 1528-0020
• DOI: 10.1182/blood-2023-184396

Acute myeloid leukemia (AML) is a main type of adult acute leukemia, especially in elderly population. The development of AML ascribed to various factors including chromosomal abnormalities, isolated gene mutations, etc. and the 5-year survival rate is around 30%. Currently, the most popular and precise assessment of AML is based on cytogenetic differences, and the patients with higher risk molecular mutations had inferior prognosis. However, with the development and application of new drugs, including bcl-2 inhibitors, many AML with high risks (such as FLT3, P53 mutated) also achieve better remission and longer survival. Up to date, many novel prognosis models are in process, with the better understanding of the initiation and development of leukemia. Ferroptosis, a widespread and ancient form of cell death driven by iron-dependent phospholipid peroxidation. Its involvement in AML has been reported, as leukemic cells frequently exhibit enhanced iron uptake and reduced iron efflux, resulting in elevated intracellular iron levels. Targeting iron homeostasis and inducing ferroptosis may provide novel insights into AML therapy. Herein, we integrated multi-gene information to identify a novel prediction model for ferroptosis-related genes (FRGs), and validated the accuracy of the model using clinical samples from our center (Figure A). In this study, we downloaded RNA-seq data and clinicopathological features of 151 AML patients from The Cancer Genome Atlas (TCGA) database (https://www.cancer.gov/ccg/research/genome-sequencing/tcga) and 70 corresponding health samples from the Genotype-Tissue Expression (GTEx) database (https://gtexportal.org/). Retrieve the ferroptosis-related genes (FRGs) set from the FerrDb database (http://www.zhounan.org/ferrdb/current/). The difference analysis, LASSO regression and Cox regression were used to determine the FRGs signature to construct prognostic models (Figure B). We finally identified 10 FRGs signature (ACSF2, SOCS1, CDO1, MYB, LPIN1, DNAJB6, PSAT1, GPX4, MT1G, FH) to establish a risk scoring model, and AML patients were divided into high-risk and low-risk group according to the median risk score. The risk score for each sample was determined using the following formula: . Kaplan-meier analysis showed that the overall survival of patients in the high-risk group was significantly worse( P