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To explore the characteristics and prognostic significance of genetic mutations in acute myeloid leukemia (AML), we screened the gene mutation profile of 171 previously untreated AML patients using a next‐generation sequencing technique targeting 127 genes with potential prognostic significance. A total of 390 genetic alterations were identified in 149 patients with a frequency of 87.1%. Younger age and high sensitivity to induction chemotherapy were associated with a lower number of mutations. NPM1 mutation was closely related to DNMT3A and FLT3‐internal tandem duplication (FLT3‐ITD) mutations, but mutually exclusive with ASXL1 mutation and CEBPAdouble mutation. In univariate analysis, ASXL1 or TET2 mutation predicted shorter overall survival (OS) or relapse‐free survival (RFS), DNMT3A, FLT3‐ITD, or RUNX1 mutation predicted a higher likelihood of remission‐induction failure, whereas NRAS mutation or CEBPAdouble mutation predicted longer OS. Concurrent DNMT3A, FLT3‐ITD, and NPM1 mutations predicted shorter OS. Hypomethylation agents could improve the OS in patients with DNA methylation‐related mutations. According to multivariate analysis, TET2 mutation was recognized as an independent prognostic factors for RFS. In summary, our study provided a detailed pattern of gene mutations and their prognostic relevance in Chinese AML patients based on targeted next‐generation sequencing screening. [ABSTRACT FROM AUTHOR]

This paper develops the Bayesian empirical likelihood (BEL) method and the BEL variable selection for linear regression models with censored data. Empirical likelihood is a multivariate analysis tool that has been widely applied to many fields such as biomedical and social sciences. By introducing two special priors to the empirical likelihood function, we find two obvious superiorities of the BEL methods, that is (i) more precise coverage probabilities of the BEL credible region and (ii) higher accuracy and correct identification rate of the BEL model selection using an hierarchical Bayesian model, vs. some current methods such as the LASSO, ALASSO and SCAD. The numerical simulations and empirical analysis of two data examples show strong competitiveness of the proposed method. [ABSTRACT FROM AUTHOR]