Predicting the influence of Circ_0059706 expression on prognosis in patients with acute myeloid leukemia using classical statistics and machine learning

Abstract

Background: Various circular RNA (circRNA) molecules are abnormally expressed in acute myeloid leukemia (AML), and associated with disease occurrence and development, as well as patient prognosis. The roles of circ_0059706, a circRNA derived from ID1, in AML remain largely unclear. Results: Here, we reported circ_0059706 expression in de novo AML and its association with prognosis. We found that circ_0059706 expression was significantly lower in AML patients than in controls (p < 0.001). Survival analysis of patients with AML divided into two groups according to high and low circ_0059706 expression showed that overall survival (OS) of patients with high circ_0059706 expression was significantly longer than that of those with low expression (p < 0.05). Further, female patients with AML and those aged >60 years old in the high circ_0059706 expression group had longer OS than male patients and those younger than 60 years. Multiple regression analysis showed that circ_0059706 was an independent factor-affecting prognosis of all patients with AML. To evaluate the prospects for application of circ_0059706 in machine learning predictions, we developed seven types of algorithm. The gradient boosting (GB) model exhibited higher performance in prediction of 1-year prognosis and 3-year prognosis, with AUROC 0.796 and 0.847. We analyzed the importance of variables and found that circ_0059706 expression level was the first important variables among all 26 factors included in the GB algorithm, suggesting the importance of circ_0059706 in prediction model. Further, overexpression of circ_0059706 inhibited cell growth and increased apoptosis of leukemia cells in vitro. Conclusion: These results provide evidence that high expression of circ_0059706 is propitious for patient prognosis and suggest circ_0059706 as a potential new biomarker for diagnosis and prognosis evaluation in AML, with high predictive value and good prospects for application in machine learning algorithms.