Using Bioinformatics To Explore The Biomarkers Of Central Nervous System Recurrence In Childhood Acute Lymphoblastic Leukemia

Background/Objective:Acute lymphoblastic leukemia(ALL)is a malignancy with abnormal lymphocytic proliferation in the bone marrow,which is a common malignancy in children.With the improvement of diagnosis and treatment methods,the efficacy of patients has been improved,but extramedullary recurrence is inevitable,among which the central nervous system(CNS)is the most common site of recurrence,which is one of the reasons affecting the efficacy of ALL.Current prevention and treatment programs of central nervous system leukemia(CNSL)have the problems of large side effects in children and recurrence after treatment.It is of great significance to study the mechanism of central nervous system recurrence in children ALL and to search for therapeutic targets of CNSL.With the advent of a new generation of whole-genome and transcriptome sequencing technology,high-channel whole-genome analysis of gene expression profiles,DNA copy number changes and epigenetic changes can be performed,and thus the establishment of a public database provides a good platform for the in-depth study of leukemia. This study aims to use the TARGET(Therapeutically Applicable Research To Generate Effective Treatments)database and bioinformatics correlation analysis method to find the biomarkers that can predict the recurrence of CNS in children with ALL,and establish a prediction model,so as to provide theoretical basis for early prevention and targeted therapy for children with ALL at high risk of CNS recurrence,and to prevent or delay the occurrence of CNS recurrence.Methods: The transcriptome and clinical data collected by the COG(Children's Oncology Group)collaboration group in the Phase II study(use for test group)and Phase I study(use for validation group)of ALL in children were downloaded from the TARGET database.Transcriptome data were analyzed by bioinformatics method to identify the hub genes,then the hub genes were analyzed by Cox univariate and Cox multivariate analysis.The Stepwise regression analysis was used to include the target genes to establish the optimal model and calculate the risk score.Univariate Cox analysis was performed on each clinical data,and multivariate Cox regression analysis was performed on statistically significant univariate results and risk score obtained from the risk assessment model to determine the biomarkers.Finally,the samples of children All Project Phase I collected by the COG collaboration group in the Target database were used for verification.The children ALL phase I samples collected by the COG collaboration group in the TARGET database were used for verification.Results: A total of 1230 differentially expressed genes were screened out between the relapsed and non-relapsed CNS groups.The results showed that these differential genes were mainly involved in biological processes such as antigen processing and positive regulation of T cell aggregation,angiogenesis,and cell adhesion,migration,and proliferation of presenting cells;the cell components were mainly concentrated on cell surface,transport vesicle membrane,endocytic vesicle membrane,and involved in the composition of the MHC class II protein complex;as for molecular function,the results showed that genes were mainly involved in MHC class II receptor activity,growth factor activity,signal transduction activity,calmodulin-binding activity,etc.(p<0.05).KEGG pathway analysis showed that these genes were mainly involved in antigen processing and presentation-related pathways,PI3K-Akt signaling pathway,Ras signaling pathway,and cancer-related signaling pathways(p<0.05).Multivariate Cox analysis of 10 hub genes identified showed 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-risk and low-risk groups were different when the validation group was substituted into the model(p=0.018).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 recurrence in childhood ALL.Moreover,the CNS3 and T lymphocyte type at initial diagnosis are also risk factors for CNS relapse of childhood ALL.