Dataset Information

Reverse engineering the neuroblastoma regulatory network uncovers MAX as one of the master regulators of tumor progression.

ABSTRACT: Neuroblastoma is the most common extracranial tumor and a major cause of infant cancer mortality worldwide. Despite its importance, little is known about its molecular mechanisms. A striking feature of this tumor is its clinical heterogeneity. Possible outcomes range from aggressive invasion to other tissues, causing patient death, to spontaneous disease regression or differentiation into benign ganglioneuromas. Several efforts have been made in order to find tumor progression markers. In this work, we have reconstructed the neuroblastoma regulatory network using an information-theoretic approach in order to find genes involved in tumor progression and that could be used as outcome predictors or as therapeutic targets. We have queried the reconstructed neuroblastoma regulatory network using an aggressive neuroblastoma metastasis gene signature in order to find its master regulators (MRs). MRs expression profiles were then investigated in other neuroblastoma datasets so as to detect possible clinical significance. Our analysis pointed MAX as one of the MRs of neuroblastoma progression. We have found that higher MAX expression correlated with favorable patient outcomes. We have also found that MAX expression and protein levels were increased during neuroblastoma SH-SY5Y cells differentiation. We propose that MAX is involved in neuroblastoma progression, possibly increasing cell differentiation by means of regulating the availability of MYC:MAX heterodimers. This mechanism is consistent with the results found in our SH-SY5Y differentiation protocol, suggesting that MAX has a more central role in these cells differentiation than previously reported. Overexpression of MAX has been identified as anti-tumorigenic in other works, but, to our knowledge, this is the first time that the link between the expression of this gene and malignancy was verified under physiological conditions.

SUBMITTER: Albanus RD

PROVIDER: S-EPMC3857773 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Reverse engineering the neuroblastoma regulatory network uncovers MAX as one of the master regulators of tumor progression.

Albanus Ricardo D'Oliveira RD Juliani Siqueira Dalmolin Rodrigo R Alves Castro Mauro Antônio MA Augusto de Bittencourt Pasquali Matheus M de Miranda Ramos Vitor V Pens Gelain Daniel D Fonseca Moreira José Cláudio JC

PloS one 20131205 12

Neuroblastoma is the most common extracranial tumor and a major cause of infant cancer mortality worldwide. Despite its importance, little is known about its molecular mechanisms. A striking feature of this tumor is its clinical heterogeneity. Possible outcomes range from aggressive invasion to other tissues, causing patient death, to spontaneous disease regression or differentiation into benign ganglioneuromas. Several efforts have been made in order to find tumor progression markers. In this w ...[more]

PMID: 24349289

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Project description:Hematopoiesis is a highly complex developmental process that produces various types of blood cells. This process is regulated by different genetic networks that control the proliferation, differentiation, and maturation of hematopoietic stem cells (HSCs). Although substantial progress has been made for understanding hematopoiesis, the detailed regulatory mechanisms for the fate determination of HSCs are still unraveled. In this study, we propose a novel approach to infer the detailed regulatory mechanisms. This work is designed to develop a mathematical framework that is able to realize nonlinear gene expression dynamics accurately. In particular, we intended to investigate the effect of possible protein heterodimers and/or synergistic effect in genetic regulation. This approach includes the Extended Forward Search Algorithm to infer network structure (top-down approach) and a non-linear mathematical model to infer dynamical property (bottom-up approach). Based on the published experimental data, we study two regulatory networks of 11 genes for regulating the erythrocyte differentiation pathway and the neutrophil differentiation pathway. The proposed algorithm is first applied to predict the network topologies among 11 genes and 55 non-linear terms which may be for heterodimers and/or synergistic effect. Then, the unknown model parameters are estimated by fitting simulations to the expression data of two different differentiation pathways. In addition, the edge deletion test is conducted to remove possible insignificant regulations from the inferred networks. Furthermore, the robustness property of the mathematical model is employed as an additional criterion to choose better network reconstruction results. Our simulation results successfully realized experimental data for two different differentiation pathways, which suggests that the proposed approach is an effective method to infer the topological structure and dynamic property of genetic regulations.

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Dataset Information

Reverse engineering the neuroblastoma regulatory network uncovers MAX as one of the master regulators of tumor progression.

Publications

Reverse engineering the neuroblastoma regulatory network uncovers MAX as one of the master regulators of tumor progression.

Similar Datasets

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