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Nan Zhang,* Ying Chen,* Shifeng Lou, Yan Shen, Jianchuan DengDepartment of Hematology, The Second Affiliated Hospital, Chongqing Medical University, Chongqing 400010, People’s Republic of ChinaCorrespondence: Jianchuan DengDepartment of Hematology, The Second Affiliated Hospital, Chongqing Medical University, 76 Linjiang Road, Chongqing 400010, People’s Republic of ChinaTel +86 1 590 230 5571Email dengjccq@hospital.cqmu.edu.cn*These authors contributed equally to this workObjective: Acute myeloid leukemia (AML) is a malignant clonal disorder. Despite enormous progress in its diagnosis and treatment, the mortality rate of AML remains high. The aim of this study was to identify prognostic biomarkers by using the gene expression profile dataset from public database, and to improve the risk-stratification criteria of survival for patients with AML.Materials and methods: The gene expression data and clinical parameter were acquired from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) database. A total of 856 differentially expressed genes (DEGs) were obtained from the childhood AML patients classified into first complete remission (CR1) group (n=791) and not CR group (n=249). We performed a series of bioinformatics analysis to screen key genes and pathways, further comprehending these DEGs through Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses.Results: Six genes (SLC17A7, MSX2, CDC26, MSLN, CTSZ and DEFA3) identified by univariate, Kaplan-Meier survival and multivariate Cox regression analyses were used to develop the prognostic model. Further analysis showed that the survival estimations in the high-risk group had an increased risk of death compared with the low-risk group based on the model. The area under the curve of the receiver operator characteristic curve in the prognostic model for predicting the overall survival was 0.729, confirming good prognostic model. We also performed a nomogram to provide an individual patient with the overall probability, and internal validation in the TARGET cohort.Conclusion: We identified a six-gene prognostic signature for risk-stratifying in patients with childhood AML. The risk classification model can be used to predict CR markers and may assist clinicians in providing realize the individualized treatment in this patient population.Keywords: childhood acute myeloid leukemia, remission induction, gene expression profiling, prognosis, bioinformatics, survival analysis |
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