Objective  To construct and validate a prognosis risk model for acute myeloid leukemia (AML) patients.

Methods  Based on The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) database to obtain gene sequencing data of AML samples and normal bone marrow samples, and then perform gene differential analysis. The genes associated with AML survival were selected and a random forest model was used to construct a risk scoring model based on gene features. The biological functions of the predictive model were further explored through Gene Ontology (GO) enrichment analysis.

Results  According to the results of Kaplan-Meier analysis and receiver operating characteristic (ROC) curve analysis, the survival rate predictions demonstrated good sensitivity and specificity in both the training and testing datasets. Multivariate Cox regression analysis and stratified analysis showed that the gene prognostic risk model could independently predict the survival outcomes of patients with AML. GO enrichment analysis demonstrated that differentially expressed genes were enriched in biological processes related to known AML.

Conclusion  By using bioinformatics methods, a mRNA marker containing 5 genes (ACOT7, SFXN3, SLC29A2, HINT2, ZMAT1) has been identified, which may serve as a potential prognostic marker for AML and provide a basis for personalized risk assessment and the formulation of treatment plans for AML patients.

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