RT Journal Article
T1 Personalized Survival Prediction of Patients With Acute Myeloblastic Leukemia Using Gene Expression Profiling
A1 Mosquera Orgueira, Adrián
A1 Peleteiro Raíndo, Andrés
A1 Cid López, Miguel
A1 Díaz Arias, José Ángel
A1 González Pérez, Marta Sonia
A1 Antelo Rodríguez, Beatriz
A1 Alonso Vence, Natalia
A1 Bao Pérez, Laura
A1 Ferreiro Ferro, Roi
A1 Albors Ferreiro, Manuel
A1 Abuín Blanco, Aitor
A1 Fontanes Trabazo, Emilia
A1 Cerchione, Claudio
A1 Martinnelli, Giovanni
A1 Montesinos Fernández, Pau
A1 Pérez Encinas, Manuel Mateo
A1 Bello López, José Luis
K1 Acute myeloid leukemia
K1 Survival
K1 Prediction
K1 Cancer
K1 Machine learning
K1 Gene expresion
K1 Prognosis
AB Acute Myeloid Leukemia (AML) is a heterogeneous neoplasm characterized by cytogenetic and molecular alterations that drive patient prognosis. Currently established risk stratification guidelines show a moderate predictive accuracy, and newer tools that integrate multiple molecular variables have proven to provide better results. In this report, we aimed to create a new machine learning model of AML survival using gene expression data. We used gene expression data from two publicly available cohorts in order to create and validate a random forest predictor of survival, which we named ST-123. The most important variables in the model were age and the expression of KDM5B and LAPTM4B, two genes previously associated with the biology and prognostication of myeloid neoplasms. This classifier achieved high concordance indexes in the training and validation sets (0.7228 and 0.6988, respectively), and predictions were particularly accurate in patients at the highest risk of death. Additionally, ST-123 provided significant prognostic improvements in patients with high-risk mutations. Our results indicate that survival of patients with AML can be predicted to a great extent by applying machine learning tools to transcriptomic data, and that such predictions are particularly precise among patients with high-risk mutations.
PB Frontiers Media
SN 2234-943X
YR 2021
FD 2021-03-29
LK https://hdl.handle.net/10347/42538
UL https://hdl.handle.net/10347/42538
LA eng
NO Mosquera Orgueira A, Peleteiro Raíndo A, Cid López M, Díaz Arias JÁ, González Pérez MS, Antelo Rodríguez B, et al. Personalized Survival Prediction of Patients With Acute Myeloblastic Leukemia Using Gene Expression Profiling. Front Oncol [Internet]. 2021 Mar 29 [cited 2025 Jul 13];11. Available from: https://www.frontiersin.org/articles/10.3389/fonc.2021.657191/full
DS Minerva
RD 30 abr 2026