Affiliations: [a] School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China | [b] The Fourth Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, Heilongjiang, China
Correspondence:
[*]
Corresponding author: Jingxin Zhang, School of Life Sciences,
Zhengzhou University, No. 100, Science Avenue, Zhongyuan District, Zhengzhou, Henan,
China. Tel.: +86 371 67783235; E-mail: zhangjx@zzu.edu.cn.
Note: [1] These authors contributed equally to this work and shared the first authorship.
Abstract: BACKGROUND: Acute myeloid leukemia (AML) has a poor prognosis, and the current 5-year survival rate is less than 30%. OBJECTIVE: The present study was designed to identify the significant genes closely related to AML prognosis and predict the prognostic value by constructing a risk model based on their expression. METHODS: Using bioinformatics (Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, univariate and multivariate Cox regression analysis, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) analysis) to identify a prognostic gene signature for AML. Finally, The Cancer Genome Atlas (TCGA) database was used to validate this prognostic signature. RESULTS: Based on univariate and multivariate Cox regression analysis, eighteen prognostic genes were identified, and the gene signature and risk score model were constructed. Multivariate Cox analysis showed that the risk score was an independent prognostic factor [hazard ratio (HR) = 1.122, 95% confidence interval (CI) = 1.067–1.180, P< 0.001]. ROC analysis showed a high predictive value of the risk model with an area under the curve (AUC) of 0.705. CONCLUSIONS: This study evaluated a potential prognostic signature with eighteen genes and constructed a risk model significantly related to the prognosis of AML patients.
