Objective: To identify biomarkers that can predict the recurrence of the central nervous system (CNS) in children with acute lymphoblastic leukemia (ALL).
Materials and methods: The transcriptome and clinical data of ALL in children were downloaded from the TARGET database. Transcriptome data were analyzed by bioinformatics method to identify core (hub) genes and establish a risk assessment model. Univariate Cox analysis was performed on each clinical data, and multivariate Cox regression analysis was performed on the obtained results and risk score. The children ALL phase I samples from TARGET database were used for validation.
Results: Univariate multivariate Cox analysis of 10 hub genes identified showed that PPARG (HR = 0.78, 95%CI = 0.67-0.91, p = 0.007), CD19 (HR = 1.15, 95%CI = 1.05-1.26, p = 0.003) and GNG12 (HR = 1.25, 95%CI = 1.04-1.51, p = 0.017) had statistical differences. The risk score was statistically significant in univariate (HR = 3.06, 95%CI = 1.30-7.19, p = 0.011) and multivariate (HR = 1.81, 95%CI = 1.16-2.32, p = 0.046) Cox regression analysis. The survival analysis results of the high and low-risk groups were different when the validation dataset was substituted into the model (p = 0.018). Then, we constructed a Nomogram which had a concordance index of survival prediction of 0.791(95%CI= 0.779-0.803). In addition, the CNS involvement grading status at first diagnosis CNS3 vs. CNS1 (HR = 5.74, 95%CI = 2.01-16.4, p = 0.001), T cell vs B cell (HR = 1.63, 95% CI = 1.06-2.49, p = 0.026) were also statistically significant.
Conclusions: PPARG, GNG12, and CD19 may be predictors of CNS relapse in childhood ALL.
Keywords: CD19; Childhood acute lymphoblastic leukemia; GNG12 ; PPARG; TARGET; bioinformatics; biomarkers; central nervous system leukemia.