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In the present study, we aimed to identify some genes closely related to AML prognosis and investigate their potential roles. RNA-seq data of AML samples were accessed from the TCGA database and then analyzed in the Wilcox test. AML survival-related genes were selected and an 8-gene signature-based risk score model was in turn constructed (including TET3, S100A4, BATF, CLEC11A, PTP4A3, SPATS2L, SDHA, and ATOX1 8 feature genes) using the multivariate Cox regression analysis. Kaplan-Meier analysis was performed on the 8 genes in the training set (p = 2.826e - 11) and the test set (p = 2.213e - 2), and there was a remarkable difference in survival between the high and low-risk samples. Meanwhile, ROC analysis was conducted and revealed the relative higher accuracy of the risk score model applied in both the training set (1-year AUC = 0.864; 3-year AUC = 0.85) and test set (1-year AUC = 0.685; 3-year AUC = 0.678). Our study helps to extend our knowledge of the potential methods for AML prognosis.HighlightsA prognostic 8-gene (including TET3, CLEC11A, ATOX1, S100A4, BATF, PTP4A3, SPATS2L and SDHA 8) signature for acute myeloid leukemia (AML) was identified and validated.The influence of the expression of single gene in the model on the survival risk of AML patients was confirmed and the risk rate of 8 single-gene was compared.
Zhang et al. (Wed,) studied this question.