Identification of Early Recurrence Factors in Childhood and Adolescent B-Cell Acute Lymphoblastic Leukemia Based on Integrated Bioinformatics Analysis.

Over the past 50 years, great progress has been made in the diagnosis and treatment of acute lymphoblastic leukemia (ALL), especially in pediatric patients. However, early recurrence is still an important threat to the survival of patients. In this study, we used integrated bioinformatics analysis to look for biomarkers of early recurrence of B-cell ALL (B-ALL) in childhood and adolescent patients. Firstly, we obtained gene expression profiles from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and the Gene Expression Omnibus (GEO) database. Then, we identified differentially expressed genes (DEGs) based on whether the disease relapsed early. LASSO and Cox regression analysis were applied to identify a subset of four genes: HOXA7, S100A11 , S100A10, and IFI44L. A genetic risk score model was constructed based on these four optimal prognostic genes. Time-dependent receiver operating characteristic (ROC) curves were used to evaluate the predictive value of this prognostic model (3-, 5-, and 10-year AUC values >0.7). The risk model was significantly associated with overall survival (OS) and event-free survival in B-ALL (all p < 0.0001). In addition, a high risk score was an independent poor prognostic risk factor for OS ( p < 0.001; HR = 3.396; 95% CI: 2.387-4.832). Finally, the genetic risk model was successfully tested in B-ALL using an external validation set. The results suggested that this model could be a novel predictive tool for early recurrence and prognosis of B-ALL.