Acute myeloid leukaemia (AML) is the most common type of adult acute leukaemia and has a poor prognosis. Thus, optimal risk stratification is of greatest importance for reasonable choice of treatment and prognostic evaluation. For our study, a total of 1707 samples of AML patients from three public databases were divided into meta-training, meta-testing and validation sets. The meta-training set was used to build risk prediction model, and the other four data sets were employed for validation. By log-rank test and univariate COX regression analysis as well as LASSO-COX, AML patients were divided into high-risk and low-risk groups based on AML risk score (AMLRS) which was constituted by 10 survival-related genes. In meta-training, meta-testing and validation sets, the patient in the low-risk group all had a significantly longer OS (overall survival) than those in the high-risk group (P < .001), and the area under ROC curve (AUC) by time-dependent ROC was 0.5854-0.7905 for 1 year, 0.6652-0.8066 for 3 years and 0.6622-0.8034 for 5 years. Multivariate COX regression analysis indicated that AMLRS was an independent prognostic factor in four data sets. Nomogram combining the AMLRS and two clinical parameters performed well in predicting 1-year, 3-year and 5-year OS. Finally, we created a web-based prognostic model to predict the prognosis of AML patients (https://tcgi.shinyapps.io/amlrs_nomogram/).
Keywords: acute myeloid leukaemia; gene expression profiling; nomogram; prognosis; signature.
© 2020 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.