Survival prediction in acute myeloid leukemia using gene expression profiling.

ABSTRACT:

Background

Acute myeloid leukemia (AML) is a genetically heterogeneous blood disorder. AML patients are associated with a relatively poor overall survival. The objective of this study was to establish a machine learning model to accurately perform the prognosis prediction in AML patients.

Methods

We first screened for prognosis-related genes using Kaplan-Meier survival analysis in The Cancer Genome Atlas dataset and validated the results in the Oregon Health & Science University dataset. With a random forest model, we built a prognostic risk score using patient's age, TP53 mutation, ELN classification and normalized 197 gene expression as predictor variable. Gene set enrichment analysis was implemented to determine the dysregulated gene sets between the high-risk and low-ris

SUBMITTER: Lai B 

PROVIDER: S-EPMC8892720 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature