TY - EJOU AU - LIU, ZHANSHU AU - HUANG, XI TI - A model based on eight iron metabolism-related genes accurately predicts acute myeloid leukemia prognosis T2 - BIOCELL PY - 2023 VL - 47 IS - 3 SN - 1667-5746 AB - Purpose: Iron metabolism maintains the balance between iron absorption and excretion. Abnormal iron metabolism can cause numerous diseases, including tumor. This study determined the iron metabolism-related genes (IMRGs) signature that can predict the prognosis of acute myeloid leukemia (AML). The roles of these genes in the immune microenvironment were also explored. Methods: A total of 514 IMRGs were downloaded from the Molecular Characteristics Database (MSigDB). IMRGs related to AML prognosis were identified using Cox regression and LASSO analyses and were used to construct the risk score model. AML patients were stratified into high-risk groups (cluster 1) and low-risk groups (cluster 2) based on the mean value of the risk score. The accuracy and prognosis prediction potential of the risk-score model was evaluated using Kaplan-Meier and receiver operating characteristics analysis. The stromal score, immune scores, and immune cells infiltrated in AML samples were estimated using CIBERSORT, MCPcountre, and Xcell algorithms. The role of immune checkpoint genes in the AML microenvironment and the prognostic value of the IMRGs were also evaluated. Results: An AML prognosis prediction model was established based on the eight most critical IMRGs. Further analyses revealed that the model could accurately predict AML prognosis. The expression of IMRGs correlated with the infiltration of several immune cells and could influence response to certain chemotherapy drugs and immunotherapy. Conclusion: A model based on IMRGs can accurately predict the overall survival and disease-free survival of AML patients. KW - Acute myeloid leukemia; IMRGs; Prognostic signature; Infiltrating immune cells; Bioinformatics DO - 10.32604/biocell.2023.024148