Background
Acute myeloid leukemia (AML) is the most common hematological malignancy in adult patients. Ferroptosis-related signatures have been shown to act as regulators of the progression of multiple cancer types, but the role of ferroptosis in AML remains to be elucidated. We performed the present study to preliminarily investigate the roles of ferroptosis-related genes (FRGs) in AML.
Conclusions
DPP4 and TFRC act as biomarkers for predicting and diagnosing AML, and their expression levels also have significant correlations with drug resistance in AML.
Methods
The transcriptome data of AML patients was downloaded from The Cancer Genome Atlas (TCGA) and the transcriptome data of normal samples was obtained from the Genotype-Tissue Expression (GTEx) database. FRGs were selected via public articles. Expression levels of FRGs between AML and normal samples were analyzed. The prognostic model based on FRGs was constructed via lasso regression. The expression levels and prognostic role of FRGs were identified from the risk model. We also performed validation experiments to verify the expression levels of the final selected genes via immunohistochemistry, polymerase chain reaction (PCR), and RNA-seq. Finally, we explored the associations between immune infiltration, drug sensitivity, and the selected FRGs.
Results
The transcriptome data of 151 AML samples were retrieved from TCGA and 70 bone marrow normal samples were retrieved from the GTEx database. Additionally, 23 FRGs were collected from the published articles. There were 22 differentially expressed FRGs, and among them, dipetidyl peptidase-4 (DPP4) (P= 0.011, HR =1.504), GPX4 (P=0.055, HR =1.569), LPCAT3 (P<0.001, HR =2.243), SLC7A11 (P=0.012, HR =2.243), and transferrin receptor (TFRC) (P=0.029, 0.774) had a significant influence on the prognosis of AML patients via lasso regression. The area under the curve (AUC) values of the 1-, 3-, and 5-year receiver operating characteristic (ROC) curves of the FRG signatures indicated that this model is novel and effective method for predicting the prognosis of AML patients. DPP4 (P<0.001) was overexpressed while LPCAT3 (P<0.001), TFRC (P<0.001), GPX4 (P<0.001), and SLC7A11 (P<0.001) were downregulated, further validation experiment results indicated that DPP4 was significantly downregulated but TFRC was upregulated in AML samples. Dysregulation of DPP4 and TFRC influence numbers of chemotherapy regimens sensitivity. Conclusions: DPP4 and TFRC act as biomarkers for predicting and diagnosing AML, and their expression levels also have significant correlations with drug resistance in AML.