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