Abstract
BACKGROUND: Acute lymphoblastic leukemia (ALL) is the most common type of childhood cancer. It can be caused by mutations that turn on oncogenes or turn off tumour suppressor genes. For instance, changes in certain genes including Rb and p53 are common in ALL cells. Oncogenes and TSGs may serve as a modulator gene to regulate the gene expression level via their respective target genes. To investigate the regulatory relationship between oncogenes, tumour suppressor genes and transcription factors at the post translational level in childhood ALL, we performed an integrative network analysis on the gene regulation in the post-translational level for childhood ALL based on many publicly available cancer gene expression data including TARGET and GEO database. METHODS: We collected 259 childhood ALL-related genes from the latest online leukemia database, Leukemia Gene Literature Database. These 259 genes were selected from a comprehensive systematic literature with experimental evidences. The identified and curated genes were also associated with patient survival cases and we incorporated this pediatric ALL-related gene list into our analysis. We extracted the known human TFs from the TRRUST database. Among 259 childhood ALL-related genes, 101 unique regulators were mapped to the list of oncogene and tumour suppressor genes (TSGs) from the ONGene and the TSGene databases, and these included 74 TSGs, 62 oncogenes and 46 TF genes. RESULTS: The resulted regulation was presented as a hierarchical regulatory network with transcription factors (TFs) as intermediate regulators connecting the top modulators (oncogene and TSGs) to the common target genes. Cross-validation was applied to the results from the TARGET dataset by identifying the consistent regulatory motifs based on three independent ALL expression datasets. A three-layer regulatory network of consistent positive modulators in childhood ALL was constructed in which 74 modulators (40 oncogenes, 34 TSGs) are considered as the most important regulators. The middle layer and the bottom layer contain 34 TFs and 176 target genes, respectively. Oncogenes mostly participated in positive regulation of gene expression and the transcription process of RNA II polymerase, while TSGs were mainly involved in the negative regulation of gene expression. In addition, the oncogene-specific targets were enriched with regulators of the MAPK cascade while tumour suppressor-specific targets were associated with cell death. CONCLUSION: The results revealed that oncogenes and TSGs possess a different functional regulatory pattern with regard to not only their biological functions but also their specific target genes in childhood ALL cancer progression. Taken together, our findings could contribute to a better understanding of the important regulatory mechanisms and this method could be used to analyse the targeted genes at the post-translational level in childhood ALL through integrative network analysis.