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:Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to discover whether there are rules or regularities governing development and evolution of complex multi-cellular organisms.

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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