In Frontiers in oncology
Acute myeloid leukemia (AML) is one of the most common hematopoietic malignancies and exhibits a high rate of relapse and unfavorable outcomes. Ferroptosis, a relatively recently described type of cell death, has been reported to be involved in cancer development. However, the prognostic value of ferroptosis-related genes (FRGs) in AML remains unclear. In this study, we found 54 differentially expressed ferroptosis-related genes (DEFRGs) between AML and normal marrow tissues. 18 of 54 DEFRGs were correlated with overall survival (OS) (P<0.05). Using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we selected 10 DEFRGs that were associated with OS to build a prognostic signature. Data from AML patients from the International Cancer Genome Consortium (ICGC) cohort as well as the First Affiliated Hospital of Wenzhou Medical University (FAHWMU) cohort were used for validation. Notably, the prognostic survival analyses of this signature passed with a significant margin, and the riskscore was identified as an independent prognostic marker using Cox regression analyses. Then we used a machine learning method (SHAP) to judge the importance of each feature in this 10-gene signature. Riskscore was shown to have the highest correlation with this 10-gene signature compared with each gene in this signature. Further studies showed that AML was significantly associated with immune cell infiltration. In addition, drug-sensitive analysis showed that 8 drugs may be beneficial for treatment of AML. Finally, the expressions of 10 genes in this signature were verified by real-time quantitative polymerase chain reaction. In conclusion, our study establishes a novel 10-gene prognostic risk signature based on ferroptosis-related genes for AML patients and FRGs may be novel therapeutic targets for AML.
Zhu Kai, Lang Zhichao, Zhan Yating, Tao Qiqi, Yu Zhijie, Chen Lili, Fan Congcong, Jin Yan, Yu Kang, Zhu Bihan, Gao Yuxiang, Wang Chengchi, Jiang Songfu, Shi Yifen
2022
AML, Immune infiltration, Prognosis signature, TCGA database, ferroptosis, machine learning
